In an age where sustainability and reduced carbon dioxide emissions are highly prioritized, the transport sector faces one of its biggest challenges. Namely, reducing the environmental impact of heavy transport. In the search for solutions to make heavy transport more sustainable, electric roads and stationary charged trucks have emerged as promising alternatives. By replacing diesel trucks with electric alternatives, the solutions offer environmental benefits and contribute to Sweden's longer-term ability to achieve its climate goals. Increasing the speed of change is critical to accelerating the implementation of electrification and maximizing its positive effects on the environment and society. Therefore, it is of great interest to investigate the factors that influence the conversion process, as well as identify strategies and measures to promote a faster transition to electrified heavy transports.With this background, this study aims to investigate conditions and challenges for increasing the conversion rate of heavy transports. The study aims to gain a better understanding of the haulage companies' needs and preferences regarding the transition. Through semi-structured interviews with respondents from ten companies with varying needs, the results could be obtained, and several insights into the research question could be obtained. The analysis of the interviews, using Rogers' theory regarding the diffusion of innovation, gave rise to several barriers and driving forces for the change. The conditions that need to be created to increase the speed of the transition have then been based on the barriers and the respondents' answers. The results indicate that the speed of conversion to the electrification solutions in Sweden is controlled by the speed of adoption of the newly introduced technologies. Adoption, in turn, is affected by a series of driving forces and barriers. It is the barriers that prevent haulers from adopting the solutions. For electric roads, it has been established that the solution may be less interesting depending on the type of traffic that a company conducts. There is also uncertainty about whether electric roads will be built. Increased communication efforts by the authorities are required to spread knowledge about electric roads and address the uncertainty of potential users. When it comes to stationary charging, an electrified truck needs to achieve cost neutrality compared to a diesel truck. This can be achieved through financial support for the expansion of the charging infrastructure, subsidies, and financing services such as leasing. An additional prerequisite for increasing the adoption rate is forecasts and guarantees regarding electricity supply and electricity prices in the future. The technology for stationary charging needs to be developed further to meet the varying needs of companies in the market. The needs regarding the technology may differ from one company to another depending on the type of traffic and the goods being delivered. In order to speed up the conversion of the heaviest transports over 60 tons, the vehicles need to have reliable battery capacity. It is also necessary to offer a well-developed charging infrastructure to facilitate the electrification of all three types of transports, namely local, regional, and long-distance transport. Furthermore, knowledge-raising efforts by the authorities are required to counteract uncertainties regarding policies and future solutions. This study suggests initiatives such as the “effektkommissionen” of Region Skåne as a suitable communication channel between the actors involved. The initiative has the potential to address various uncertainties and make the solution more visible to potential users. By creating the proposed conditions, an increased rate of adoption can be achieved, which in turn, leads to Sweden fulfilling its climate goals.

Background. E-commerce is ever expanding, and with it, so is last-mile delivery. Last-mile delivery is the last part of any delivery from a retailer or etailer (online retailer) to a consumer. Last-mile delivery is one of the most costly parts of the supply chain for many etailers and as such efforts have been made to understand what drives preferences for last-mile delivery. To broaden this field, this study will introduce time preference theory to the last-mile delivery and examine if delayed payment of an online purchase has any effect on the last-mile deliver preferences. 

Objectives. The objectives of this study are to examine if delayed payment of online purchases has any effect on the last-mile delivery preference for consumers, as well as broaden the knowledge on last-mile delivery preferences in general. This is done with the inclusion of time preference theory as an underlying theory. 

Methods. The study is carried out through the use of an experiment study with an experiment survey. The survey was made into three different versions, distributed to three different experiment groups. The data was then tested with a non-parametric proportion test with varying values of proportion to see whether there was any significant proportion of the replies that were in favor of delay of payment having any effect on the consumer preference.  

Results. The results of the study showed that consumers generally choose the last-mile delivery option with the lowest delivery fee. This is in line with previous findings which concluded that delivery fee and delivery speed are the two most important factors for consumers. The only case in which consumers indicated that there was any effect of the payment delay was in the case of an expensive product. Even then it is hard to rule out that it was not something else that brought this result.  

Conclusions. The concluding findings of the study is that while time preference was added as an attribute for last-mile delivery, it had little to no effect. This may have been because of the study design of this study. For future research on the topic, examining time preference in a greater depth may give different findings from this study.

Background

Many firms seek to embrace mass customization, a strategy that brings competitive advantages. Adopting mass customization impacts the area of assembly line feeding as it is a central point of the manufacturing process of firms. A research field on the line feeding has been defined as the assembly line feeding problem (ALFP), which addresses how different line feeding policies perform under varying circumstances. One of the existing line feeding policies, kitting, can be used to improve flexibility in production. There is today a lack of knowledge on how multi-model lines are affected by kitting, which this study intends to provide.

Objectives

The objective of the study is to disseminate knowledge about the performance of line feeding policies. In specific, the thesis studies the impact that different levels of kitting have on multi-model assembly lines that operates in the automotive industry, when batch sizes are decreased.

Method

Following the optimisation process, a descriptive model has been built to portray and react to the choice of line feeding method for firms utilising multi model lines. The descriptive model is then complemented with a ranking procedure called Analytic Hierarchy Process (AHP). This tool gives access to the importance of the impacts that a line feeding policy has on firms, without relying on monetary values.

Results

The results of our study show that different scenarios are optimal depending on batch sizes. The results from the thesis show that kitting was preferable compared to line stocking when batch sizes are lowered. This by proving to be the optimal line feeding solution when batch sizes are decreased to cover ⅒ and ⅕ of the daily demand. If the batch size is ½ of the daily demand, a hybrid feeding approach showed to be a feasible option.

Conclusions

This study highlights the superiority of kitting for large firms in the automotive industry, using multi-model assembly lines when batch sizes are reduced. A finding that is highly relevant in today’s manufacturing industry as they follow the trend of mass customisation.

The transportation sector plays a significant role in global carbon emissions, emphasizing the need for transitioning to alternative fuel technologies. This thesis primarily aims to examine the factors that influence small road freight transport companies in Sweden when transitioning to alternative fuel technology. The focus is on identifying these determinants and their respective significance, utilizing a company internal Resource-Based view as the analytical framework. Semi-structured interviews were conducted among small road freight transport companies, with data analyzed using pattern and keyword matching analysis. The results reveal multiple determinants within financial, technological and organizational resources. Range, purchase cost, and customer demand emerged as the most influential determinants, with range limitations hindering adoption and high costs deterring investment. However, customer demand for eco-friendly transport presents an opportunity for companies to gain a competitive advantage. Company size, cargo type, and operational constraints also influence decision-making. In the data selection, micro companies exhibit a higher propensity for drop-in alternative fuels, while small-sized companies engaged in short-haul transportation are more open to alternative fuel adoption and riskier investments. Risk-averse followers tend to stick to established practices. Policymakers are recommended to invest in infrastructure, provide subsidies, and implement regulations to incentivize adoption, while vehicle producers should optimize designs and collaborate with fuel producers. Further research is advised to investigate the viability of different low-carbon fuel alternatives, explore challenges and motivations faced by different company types, and conduct comparative studies. Quantitative research methods can offer broader insights into this area of study.

Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of transmission control protocol/internet protocol con- nectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search pro- cess is triggered to identify the expected arrival-time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart-pole data streams reporting congestion rates across parking area junc- tions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state-transition matrix, showing the transfor- mation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capa- bilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion-aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot.

To become a successful company today all costs, must be kept to a minimum. To make sure they are companies need to try new methods and policies to get closer to an optimum production. One of the things that need attention is the inventory of spare parts and the replacement of the same. The companies want their machines to have as many active hours as possible and therefore they need to replace components in them from time to time.

This study will compare the current policy with a new one to see if it is economically justifiable to minimize the inventory of spare parts. This will be done by replacing two identical components in the same machine before they fail and production stops. We call it the proactive block replacement policy. To test this new policy a simulation based on historical data was made where the costs associated with the different replacements such as cost of component, cost o lost production and cost of the two types of replacements. All these costs were considered to see is the company could save money through this change.

The result showed that the new policy was not suitable for this specific component because the cost of the component and the variation of lifetime on it was too high. Because of the big variation of lifetime, the time of the replacement had to be set after fewer hours of production which means more components will be used and the cost per active hour will be higher.

This study is limited because only one specific component at a specific machine was studied and it is not possible to make any assumptions for other components from this study. This had to be done to get the most precise information from the company to get the best result.

The conclusion of this study is that the company should keep their current replacement theory and not change into the new one. Though there might be possible ways of lowering the costs by only having one component in inventory instead of two.

Earlier studies have mainly focused on technology, economy and advantages of electric buses, and they have largely shown that electric buses could be one of the solutions for sustainable public transport. Despite this, the present procurement process for public transport in Sweden is not suitable for including support systems for electric buses. This study was aimed to find a stakeholder collaboration model that would allow electric bus systems to be more effectively included in the procurement process for public transport. The results were achieved by several multi-stakeholder collaboration seminars and meetings that included representatives from regional public transport authorities, bus operators, bus manufacturers, energy companies, municipalities, and experts involved in bus transport. The study primarily developed two stakeholder collaboration models, suggesting that charging infrastructure should be designed separately from the common procurement process. In these models, energy companies, electric grid owners, charging infrastructure operators, regional public transport authorities, and municipalities need to collaborate. The first model is designed for a system that includes chargers at certain locations along a route and/or stakeholders with a low level of experience of electric bus systems, while the second is designed for a system that includes bus charging at the depot and/or stakeholders with a high level of experience of electric bus systems.

Traffic flow estimates play a key role in traffic network management and planning of transportation networks. Commonly it is the average daily traffic (ADT) flow for different road segments that constitute the data. This paper shows how an advanced and detailed analysis based on hourly flow measurements over the day can contribute to a deeper understanding of how hourly flows together reflect the vehicles' routes. The proposed method identifies the shortest travel time paths between all possible origins and destinations in a transportation network, and thereafter it identifies the most significant routes in the network by performing statistical tests. For this purpose, the paper presents a mathematical model, a vehicle simulator based on this model, and a statistical framework that is able to find the most probable underlying routes. The paper contains a real test scenario based on 24-hour traffic flows (hour by hour) to demonstrate the applicability of the method. © 2020 IEEE.

The project was carried out at Mirro AB, a small company that manufactures sliding door solutions in its production in Hillerstorp, Småland. Within the company there are two brands, Mirro and Pelly System. The company has faced a challenge due to the fact that that there has been a big increase in volumes. This has led to consequences such as an increased number of picking errors for both brands as well as queues in Mirros area for compilation of orders. The purpose has been to suggest cost-effective solutions to these problems.

One delimitation is that the complaints have been evaluated solely for the full calendar year of 2016. The study has only been limited to the picking stock for the brands Mirro and Pelly System. Therefore, the study has not taken into account any improvements needed in the production itself in addition to the proposed layout changes proposed to create better conditions for the flow of the stock. The study has been limited to the picking stock and the area for compilation of orders near by the picking stock.

The two questions that were answered are:

a)      How can picking errors from the stock at a small company be minimized?

b)      In a small company with customer unique products, how can queuing in the area for compiling orders, be avoided by optimizing order picking in relation to the production flow?

Case study regarding the company Mirro AB has been carried out with data collection through interviews and review of statistical material for complaints and storage transactions. This has since been used in various forms of systematic processing through SWOT analyses, brainstorming, Pareto diagrams and Ishikawa diagrams.

Important results have been that a new, more efficient layout has been suggested for the warehouse that leads to more efficient order picking and order compilation. It is recommended to introduce barcode scanners with display and handheld computers as picking help and to reallocate personnel resources between the two brands to get a more even division of labour.

The recommendations of the studies will help the company to correct its problems regarding the two issues without having to increase its resources. There will also be a better working situation due to the improved layout.

The conclusion is that if the company implements the recommended improvement measures proposed, it will have a good possibility to correct the two issues. The result from the case study provided the expected results, it required more changes than I had expected.

Key words: Order picking, warehouse, error picking, article 

Background. Several technologies are being developed to reduce emissions to make the transportsector greener. Extensive research has been conducted on the supply side of these technologies, but not as much on the demand side.

Objectives. The purpose of the study is to analyze end consumers’ preferences for green last mile delivery. Electric roads are used to represent green technologies. The purpose is examined by investigating consumers’ preferences for the last mile delivery attributes delivery fee, delivery speed, delivery point and emission reduction using mental accounting.

Methods. A choice-based conjoint analysis is conducted to gather data on 196 Swedish online consumers’ preferences. The choice experiment is conducted in two contexts to examine how these preferences differ between involvement in the product being transported. Data is analyzed with a Hierarchical Bayesian model.

Results. The results show that consumers think emission reductions are more important than delivery fee when choosing last mile delivery. Consumers with low purchase frequency have higher preferences for emission reductions than consumers with higher purchase frequency. Also, females prefer higher rates of emission reductions relative to males when choosing last mile delivery for low involvement products, while there are no differences in the high involvement case. Preferences for the green attribute of last mile delivery do not vary between age and income groups, but preferences between these groups differ for other attributes.

Conclusions. These findings indicate that consumers use an environmental responsibility account when choosing last mile delivery and that it has high importance relative to a money account but similar importance as the convenience and time accounts. Preferences are different when choosing transport for high and low involvement products and consumers with different characteristics.

Implications. Firms offering last mile delivery services could use technologies to make their services greener and gain a competitive advantage since consumers prefer these green options.

Traffic analysis is vital for enhancing the performance of transportation systems, where continuous evaluation of traffic states helps responsible road authorities detect and address issues. High-quality traffic data is key to analysis, as it aids in planning and investments. Traditionally, traffic data collection has been costly and limited. Nowadays, connected vehicles and mobile phones have transformed this process, enabling traffic data collection across large geographic regions without the need for dedicated measurement devices. The availability of large-scale and detailed traffic data allows for in-depth analysis using mathematical models. This thesis develops models to utilize available traffic data for transportation system improvements, aiming to enhance traffic conditions and road user experience. It utilizes data from link flows and travel times, applying models over large geographic areas. The thesis addresses transportation engineering issues through data-driven methods. The thesis proposes two methods for allocating electric vehicle charging stations using optimization and route sampling techniques. It introduces a new index for assessing travel time reliability. It shows how clustering analysis of descriptive travel time statistics can be used to detect different traffic states. Furthermore, this thesis presents a statistical model to estimate link flow propagation using measured link flow data, analyzing traffic influence across surrounding areas. The thesis also uses traffic simulation, focusing on combining speed cameras and probe vehicles for data collection and developing a model to identify probable routes based on hourly link flows. The thesis results highlight the importance of data-driven models in optimizing transportation systems and improving road user travel experiences.

This thesis concerns the mathematical modeling of transportation systems for improved decision support and analysis of transportation-related problems. The main purpose of this thesis is to develop and evaluate models and methods that exploit link flows. Link flows are straightforward to obtain by measurements or estimation methods and are commonly used to describe the traffic state. The models and methods used in this thesis apply mathematical optimization techniques, computer simulations, and probabilistic methods to gain insights into the transportation network under study and provide benefits for both traffic managers and road users. 

First, we present an optimization model for allocating charging stations in a transportation network to serve owners of electric vehicles. The model utilizes a probabilistic route selection process to detect locations through which vehicles may pass. It also considers the limited driving range of electric vehicles. The iterative solution procedure finds the minimal number of minimal charging stations and their locations, which provides a lower bound of charging stations to cover each of the considered routes. Second, we present a case study, in which we argue that stationary and mobile measurement devices possess complementary characteristics. In that study, we investigate how speed cameras and probe vehicles can be used in conjunction with each other for the collection of detailed traffic data. The results show that the share of successfully observed and identified vehicles can be significantly improved by using both stationary and mobile measurement devices. Third, we present a simulation model with the intent of finding the most probable underlying routes based on hourly link flows. The model utilizes Dijkstra's algorithm to find the shortest paths and uses a straightforward statistical test procedure to find the most significant routes in the network based on replicated movements of trucks. Finally, we investigate the possibility to study how the traffic flow in one location reflects the flows in the surrounding area. The statistical basis of the proposed model is built upon measured link flows to study the dispersion of aggregate traffic flows in nodes. By considering the alternative ways vehicles can travel between locations, the model is able to determine the expected link flow that originates from a node in a nearby region.

The results of the thesis show that the link flows, which are basic descriptors of the road segments in a transportation network, can be used to study a broad range of problems in transportation.

Electric vehicles (EVs) are environmentally friendly and are considered to be a promising approach toward a green transportation infrastructure with lower greenhouse gas emissions. However, the limited driving range of EVs demands a strategic allocation of charging facilities, hence providing recharging opportunities that help reduce EV owners' anxiety about their vehicles' range. In this paper, we study a set covering method where self-avoiding walks are utilized to find the most significant locations for charging stations. In the corresponding optimization problem, we derive a lower bound of the number of charging stations in a transportation network to obtain full coverage of the most probable routes. The proposed method is applied to a transportation network of the southern part of Sweden.

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V.

The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an ``iterative route cover optimization method'' to suggest  charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each  route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. 

Modern vehicles are to a large extent connected today, either directly by built-in navigation systems in the vehicles or indirectly by other devices such as mobile phones and GPS units. This enables the possibility to continuously collect traffic data in a cost-effective way. The increased access to detailed data allows practitioners and researchers to analyze the transportation system from various perspectives. The travel speed is a common descriptor of the traffic state, and it can be extracted from GPS data. By analyzing how the travel speed vary over time and detect anomalies among the measured travel speeds, it is possible to detect potential deficiencies in the transportation system, e.g., insufficient road capacity which may cause bottlenecks. Often, a weakness in the infrastructure is detected in a very late stage which means that extensive investments may be required to resolve the deficiency.

The purpose of the pilot study is to develop methods and models to detect deficiency in the transportation system and to identity travel speeds that deviates from the normal state, i.e., travel speeds that are considered as very low or very high with respect to the normal behavior. Thus, the starting point of the pilot study is to find appropriate ways to model the traffic state along the studied road segments by using measured travel speeds from a general point of view. Analysis of the traffic state allows the study of how the normal state of the road segments change of time to detect deficiency related to road capacity and road access which may occur if no changes are made, or to detect road segments where the normal state is unchanged.

Typically, slower travel speeds may be an indicator of that a deficiency along a road segment exists. Thus, we present a method to systematically partition measured travel speeds in low, normal, and high travel speeds. The method is robust and enable the possibility to compare different road segment with different attributes, such as number of lanes and free-flow travel speed, with each other. Furthermore, we present a new measurement to describe how the low travel speeds relates to the free flow travel speed, e.g., the speed limit. Existing measurements and indicators used today utilize travel speeds which range from low to high. Our proposed measurement uses low travel speed and free flow travel speed exclusively and aims to quantify the accessibility and condition of a road segment.

The pilot study also includes an initial attempt to apply cluster analysis to detect recurrent patterns along the studied road segments. Cluster analysis is in several contexts an effective method to group time series to detect recurrent patterns among the speed profiles. The purpose of using cluster analysis is to evaluate if speed profiles with similar behavior is related to, for instance, weekday or time of the day. Thus, cluster analysis may be used to detect road segments with recurring low travel speeds, and potentially be used to forecast when congestion or queues may occur.

The pilot study is mainly limited to travel speed data. The proposed methods and models show that it is possibly to solely use travel speed data to detect deficiencies in the transportation system. In particular, the pilot study shows the potential to detect deficiencies in the transportation system without additional data sources such as link flow data.

Traffic flows play a very important role in transportation engineering. In particular, link flows are a source of information about the traffic state, which is usually available from the authorities that manage road networks. Link flows are commonly used in both short-term and long-term planning models for operation and maintenance, and to forecast the future needs of transportation infrastructure. In this paper, we propose a model to study how traffic flow in one location can be expected to reflect the traffic flow in a nearby region. The statistical basis of the model is derived from link flows to find estimates of the distribution of traffic flows in junctions. The model is evaluated in a numerical study, which uses real link flow data from a transportation network in southern Sweden. The results indicate that the model may be useful for studying how large departing flows from a node reflect the link flows in a neighboring geographic region. 

Characterizing links in road networks is vital for understanding recurring traffic state patterns. For long-term planning, clustering can reveal links with similar characteristics and patterns that may indicate degraded performance in the future. In this paper, we apply cluster analysis to automate this process and identify similarities among links and days to find potential infrastructure deficiencies. Our study uses different clustering techniques on descriptive statistics to categorize link-types and day-types. Applying our method to high-resolution travel speed data, reveals consistent link characteristics across different clustering algorithms. The preliminary results show that the identified clusters maintain stability both in space and time, confirming their effectiveness in identifying consistent link characteristics and daily patterns. This offers insights into traffic state variations based on travel speed.

Characterizing link-types and day-types in road networks is vital for understanding recurring traffic state patterns. Link-types and day-types in road networks describe road segments and days based on their specific characteristics.For long-term planning, clustering can be used to categorize links and days with similar characteristics and patterns that may indicate degraded performance in the road network in the future. In this paper, we apply cluster analysis to automate this process and identify similarities among links and days to find potential infrastructure deficiencies and recurring traffic states. Our study uses k-means on descriptive statistics to reveal link-types and day-types. Applying our method to high-resolution travel speed data from a road in Sweden reveals distinct characteristics based on the link and day. The results indicate that the relative difference between the measured travel speed and the free-flow travel speed is negative on links with higher free-flow travel speeds. Additionally, the variability in travel speeds is greater on links with lower free-flow travel speeds.

Travel time reliability is vital for both road agencies and road users. Expected travel time reliability can be used by road agencies to assess the state of a transportation system, and by road users, to schedule their trips. Road network deficiencies, such as insufficient traffic flow capacity of a road segment or poor road design, have a negative impact on the reliability of travel times. Thus, to maintain robust and reliable travel times, the detection of road network deficiencies is vital. By continuously analyzing travel times and using appropriate travel time reliability measurements, it is possible to detect existing deficiencies or deficiencies that may eventually occur unless necessary actions are taken. In many cases, indices and measurements of travel time reliability are related to the distribution of the travel times, specifically the skewness and width of the distribution. The current paper introduces a median-based misery index for travel time reliability. The index is robust and handles travel times that follow a skewed distribution well. The index measures the relative difference between the slow travel speeds and the free-flow travel speed. The index is inspired by the median absolute deviation, and its primary application is to detect routes or road segments with potential road network deficiencies. To demonstrate the applicability of the index, we conducted an empirical case study using real travel speed data from the European route E4 in Sweden. The results from the empirical case study indicate that the index is capable of detecting road segments with slow travel speeds regardless of the travel speed distribution.

Travel times and travel time reliability are important indicators of the traffic state in a transportation system. Analyzing travel times and their reliability and how they vary over time makes it possible to identify existing road network deficiencies or deficiencies that may eventually occur unless necessary actions are taken. Road network deficiencies, such as inadequate road capacity or substandard road design, typically lead to congestion and trip delays for road users. A novel median-based misery index is proposed to highlight potential road network deficiencies in the transportation system. The proposed index offers a way to gauge travel time reliability, providing valuable insights into roads where improvements may be needed. The newly developed median-based misery index operates by computing the relative disparity between slow travel speeds and free-flow travel speeds. The median-based misery index is more robust to skewed distributions of travel times than the ordinary misery index. Our empirical case study includes a spatiotemporal analysis of travel speed data from a section of the European route E4 in Sweden. The case study results show that the new index may be used to detect peak periods when the traffic conditions for road users have deteriorated.

Effectiveness in managing disturbances and disruptions in railway traffic networks, when they inevitably do occur, is a significant challenge, both from a practical and theoretical perspective. In this paper, we propose a heuristic approach for solving the real-time train traffic re-scheduling problem. This problem is here interpreted as a blocking job-shop scheduling problem, and a hybrid of the mixed graph and alternative graph is used for modelling the infrastructure and traffic dynamics on a mesoscopic level. A heuristic algorithm is developed and applied to resolve the conflicts by re-timing, re-ordering, and locally re-routing the trains. A part of the Southern Swedish railway network from Karlskrona centre to Malmö city is considered for an experimental performance assessment of the approach. The network consists of 290 block sections, and for a one-hour time horizon with around 80 active trains, the algorithm generates a solution in less than ten seconds. A benchmark with the corresponding mixed-integer program formulation, solved by commercial state-of-the-art solver Gurobi, is also conducted to assess the optimality of the generated solutions.

Denna rapport tar upp undersökningen av effektivisering av materialpåfyllnad som utförts på företaget Alfa Laval Lund AB i Ronneby. Undersökningen har involverat förbättringsåtgärder för emballagematerialets flöde. Hur detta material kan hanteras med rätt material, i rätt tid och rätt kvantitet på ett tidseffektivt sätt utgör frågeställningen för denna undersökning. Då företaget idag använder sig av leanfilosofin utgör denna en stor del i det teoretiska kapitlet. Teorier om hur lean fungerar samt hur logistiken är kopplad till förbättring av materialflödet har ett teoretiskt kapitel där teorin förklarar hur dessa bör utformas. Även ett kapitel om hur metoderna observationer, intervjuer och brainstorming med flera används och varför finns också beskrivet. En förklaring om hur företaget arbetar idag och hur de involverade stationerna påverkar materialflödet tas också upp. Idag genomförs många kontrollrundor för att enbart kolla om produktionen har det emballagematerial som de behöver eller om något saknas. Dessa rundor sker flera gånger om dagen och resulterar inte i något värde för varken kunden eller företaget. Rundorna utgör en stor del i grunden till problemformuleringen, tillsammans med det andra problemet som handlar om kommunikation. Hur kan denna lösas då kontrollrundorna elimineras? Här har teorin om hur man bör signalera om behov genom dragande system istället för tryckande system som görs idag använts. Denna ständiga ovetskap om produktionsflödet materialbehov skapar stor stress för de anställda. Genom att spendera tid på att kontrollera samt kommunisera på ett sätt som tar upp mer tid än vad som behövs, flyter inte flödet på i ett dragande system.

Tre olika åtgärdsförslag togs fram från metodgenomförandet. Dessa är följande:

 organisera lagret där emballagematerialet förvaras

 använda sig av ett flödeslager där man fyller på material som är uträknat att räcka för tre dagar

 en elektronisk kanbansignal som används för att signalera behov

Slutsatsen för undersökningen är att dessa åtgärder effektiviserar materialpåfyllnaden genom att eliminera kontrollrundorna. Detta sparar tid och minskar stressen för de anställda genom att man nu får informationen till sig när produktionen har behov av material istället för att komma ihåg att kontrollera materialet med jämna mellanrum. Produktionen kan använda rätt materialet i rätt tid och har rätt kvantitet att tillgå. 

A case study has been done at Dynapac Compaction Equipment AB in Karlskrona in order to improve the internal flow of the production. The “Supermarket Storage”, an adjoining storage that feed material to the lean production in the “Z-line” assembly line with the help of kitting, was chosen to be focused during the optimization of the internal flow. Also, due to the little academic research about kitting it was decided to focus the research on the kitting process and identify how to optimize it.

The purpose of the research is to determine optimization methods of a kitting process and fill in the gap in the subject field about kitting optimization. Given the research time limit, the focus was only on the kitting process in the Supermarket Storage and no optimization could change the storage’s layout. This resulted in three research question that will be investigated in the thesis.

 Which common approaches exist when it comes to optimizing a kitting process?

 What is the result of each optimizing method in the time aspect?

 When should an optimization method be used, compared to the other methods that will be tested in this research?

In order to solve these questions, was a needfinding process used in order to identify the kitting process current problems and the needs of the employees. With this, three optimization methods were identified and selected to be used to optimize the kitting process; optimization of routing, optimization of family grouping and optimization of an electronic system. The optimization of routing focused on the route that the kitters travel and the optimization of the family grouping focused on the article distribution in the Supermarket Storage; there each kitting operation’s articles should be stored in the same zone. Finally, the optimization of the electronic system, investigated the possibility to utilize a pick to scan system with the kitting process.

Each optimization was implemented in different field experiment in order to identify how each optimization affected the kitting process. This resulted in that each optimization had improved the kitting process time efficiency and the electronic system had the biggest impact. Some other results were also observed during the experiments. The route optimization improved the learning curve of the kitting process and the family grouping optimization decreased the bottlenecks in kitting process. The electronic system optimization also implemented new benefits that resulted in a profit 2.5 times the cost of the system. Some of the benefits include removal of unneeded processes, quality control of the kitting process and statistics gathering that can be used to improve the process in the future.

These results imply that all three optimization methods can be used in order to improve the time efficiency of a kitting process in a similar storage layout. The routing optimization should be used in a kitting operation with a high rotation of new kitters. The family grouping should be used in a kiting process with bottlenecks in the process and low organization of the article distribution. Ultimately, the electronic system optimization should be used in a kitting process that has unneeded processes and has the need of new tools that the electronic system can implement.

The process of collecting traffic data is a key component to evaluate the current state of a transportation network and to analyze movements of vehicles. In this paper, we argue that both active stationary and mobile measurement devices should be taken into account for high-quality traffic data with sufficient geographic coverage. Stationary devices are able to collect data over time at certain locations in the network and mobile devices are able to gather data over large geographic regions. Hence, the two types of measurement devices have complementary properties and should be used in conjunction with each other in the data collection process. To evaluate the complementary characteristics of stationary and mobile devices for traffic data collection, we present a traffic simulation model, which we use to study the share of successfully identified vehicles when using both types of devices with varying identification rate. The results from our simulation study, using freight transport in southern Sweden, shows that the share of successfully identified vehicles can be significantly improved by using both stationary and mobile measurement devices.

In this paper, we study the complementary characteristics of stationary and mobile devices for traffic data collection. Since stationary devices continuously collect traffic data at fixed locations in a network, they can give insight of the traffic at particular locations over a longer period of time. Mobile devices have wider range and are able to collect traffic data over a larger geographic region.Thus, we argue that both types of technology should be considered to obtain high-quality information about vehicle movements .We present a traffic simulation model, which we use to study the share of successfully identified vehicles when considering both stationary and mobile technologies with varying identification rate. The results of our study, where we focus on freight transport insouthern Sweden, confirms that it is possible to identify the majority of vehicles, even when the identification rate is low, and thatthe share of identified vehicles can be increased by using both stationary and mobile measurement devices

Emerging digital transformation in industry is noticeable among others in Supply Chain Management (SCM). For instance, applying new-generation digitalized technologies in the Dairy Supply Chain (DSC) enables an increase of manufacturing productivity, improves planning and forecasting, and also enhances competitive capabilities according to Industry 4.0 assumptions. It is worth mentioning, that in modern DSC, high visibility of raw materials, components, products, and processes by all contributors on all stages of DSC is crucial. This article focuses on the transparency aspect of the DSC supported by IoT-based technologies enabling interoperability among all DSC participants. The paper addresses the problem of effective integration of heterogeneous data sources, i.e., deployed new technological IoT solutions with traditional SCM systems and a third-party software component. The main objective of this report is to propose the IoT-based DSC model comprising four chain stages: milk production, milk transportation, milk processing, and dairy products distribution. Moreover, the comprehensive DSC domain ontology as a knowledge model is formulated and described. The ontology aims on improvement of the DSC management efficiency by facilitating interoperability within DSC. The applicability of the proposed ontological model is verified using a sustainable-oriented case study, which estimates the environmental footprint at the milk transportation stage of the DSC. © 2021 Kauno Technologijos Universitetas. All rights reserved.

This study is devoted to understanding traffic cruising causation through exploring and enhancing parking data. Five recent (2017-2020) studies modeling parking congestion relied on occupancy as their only parking lot feature, then compared modeling techniques using this feature, to find the best performance. However, recently some computer scientists pointed out that it is more effective for the computer science community to focus more on data preparation for performance improvements, rather than exclusively comparing modeling techniques. This inspired us to add more parking lot features and evaluate them, to investigate how they should be composed into a congestion score, acting as a more accurate picture of reality. The score is then compared to the performance of a version where occupancy is the only parking lot feature. An experimental case study is designed in three parts. The first measures how the features should be summed into a score according to drivers' expectations. The second analyzes how much data can be reused from the real data, and whether spatial or temporal comparisons are better for data synthesis of parking data. The third part compares the performance of the score against the occupancy-only version using k-means clustering algorithm and dynamic time warping distance. The experimental results show performance improvements in all spatial and temporal categories, and increasing improvement as the sample sizes grow. © 2021 IEEE.

Background

Warehouse efficiency has gained a lot of attention in the recent years where racking layout and slotting of the SKUs are essential. The efficiency can be measured in the total traveling distance that occur in the warehouse. Two of the most central racking layout and slotting policies are the fishbone layout and ABC class-based slotting. These policies offer many new advantages for the traveling distance compared to others.

Objectives

The purpose of this paper is to investigate how a combination of a fishbone layout and ABC class-based slotting affects the total traveling distance relative to a traditional layout with random slotting. This will then be analyzed in order to investigate to what degree it can be implemented depending on the situation a company has.

Methods

The study was carried out as a simulation study, simulating the forklift traveling distance between four different warehouse layout and slotting configurations. This simulation was done by gathering of information in the form of observations, interviews, measuring and data from the EDI system of the company. The simulation was made in excel and featured eight possible scenarios depending on different events which could occur in the warehouse. The data in the study were then analyzed through a one-way ANOVA test, Scheffe’s post hoc test and t-Tests.

Results

The result of the study shows that the different events which can occur in the warehouse is affected by a change in layout and slotting configurations. With the implementation of new layout and slotting configurations it is also shown that the forklift traveling distance in the warehouse can be reduced. The result also shows that the isolation of the effects that the respective layout or slotting policies provides reduces the forklift traveling distance individually as well as a combination would do.

Conclusions

The conclusion is that the Fishbone layout with ABC class-based slotting is the optimal solution considering the reduction in traveling distance in a warehouse. When considering the implementation process and the potential benefits with the configurations, a general company is recommended to consider what amount of resources they are willing to invest before they carry out the implementation.

Delimitations

This paper is delimited to the total travelling distance that occur in a warehouse because of the time restriction. The study will not examine the intangible part of a warehouse which means that the WMS- system will not be addressed. The paper will not create an investment calculus for the proposed solution.

In railways, it is essential to achieve high train punctuality. Thus, whenever disturbances occur, it is important to reschedule the trains effectively. This task is typically handled manually by traffic controllers in real-time. This thesis presents efficient computer algorithms for assisting traffic controllers in effectively rescheduling a train timetable during disturbances.

The train timetable rescheduling problem is typically hard to solve as the solutions of interest, spread across a vast solution space, need to be searched quickly. Two main solution approaches involve using (i) exact algorithms, which typically search the entire solution space, and (ii) heuristic algorithms, which try to search for a good-enough solution quickly. Although research on competitive algorithms is prevalent, limited research exists on exploring the benefits and challenges of using parallel computing to tackle the problem.

The primary objectives of this thesis are: (i) to model the train timetable rescheduling problem's search tree to be well-suited for parallel computing, (ii) to devise parallel heuristic search algorithms that can quickly and effectively solve the problem for one or many rescheduling objectives, (iii) to investigate the potential and limitations of parallel computing in the context of the problem, (iv) to investigate the comparison and evaluation of alternative solution approaches to analyze their strengths and limitations.

In this thesis, we model the problem's search tree as a binary tree where the edges represent alternative rescheduling decisions and leaf nodes represent feasible timetables. We solve the problem by searching the tree using a parallel strategy that combines a depth-first search with simultaneous breadth-wise tree exploration. We evaluate our parallel algorithms for various disturbances on a Swedish railway network through experiments.

The results of our research show that a parallel depth-first search algorithm can quickly search the devised search tree for solutions. With multiple rescheduling objectives, the parallel search algorithm obtained better solutions and showed higher speedups. Additional problem constraints often improved the search process by making the parallel algorithm reach the solutions faster. The results also show the potential and challenges of using graphics processing units for detecting conflicts in the timetable during the search. In conclusion, this thesis shows that parallel train timetable rescheduling algorithms can improve the search speed and the quality of the solution(s) obtained in real-time within the computational time limit.

In railway traffic systems, whenever disturbances occur, it is important to effectively reschedule trains while optimizing the goals of various stakeholders. Algorithms can provide significant benefits to support the traffic controllers in train rescheduling, if well integrated into the overall traffic management process. In the railway research literature, many algorithms are proposed to tackle different versions of the train rescheduling problem. However, limited research has been performed to assess the capabilities and performance of alternative approaches, with the purpose of identifying their main strengths and weaknesses. Evaluation of train rescheduling algorithms enables practitioners and decision support systems to select a suitable algorithm based on the properties of the type of disturbance scenario in focus. It also guides researchers and algorithm designers in improving the algorithms. In this paper, we (1) propose an evaluation framework for train rescheduling algorithms, (2) present two train rescheduling algorithms: a heuristic and a MILP-based exact algorithm, and (3) conduct an experiment to compare the two multi-objective algorithms using the proposed framework (a proof-of-concept). It is found that the heuristic algorithm is suitable for solving simpler disturbance scenarios since it is quick in producing decent solutions. For complex disturbances wherein multiple trains experience a primary delay due to an infrastructure failure, the exact algorithm is found to be more appropriate. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

During the last few years, the demand for deliveries in residential areas have increased dramatically due to an increase in online shopping and changed lifestyles where reduced car use among other things have led to people rely on home deliveries to a wider extent. The built environment thus faces a new set of requirements in order to accommodate these deliveries. At the same time, current sustainability-goals dictate that less emphasis is put on the car in the modern city, which is in part done by reducing space for cars in general, emphasizing a need for safe streets and reduced traffic. In order for these aspects to work together, the street and public space must be designed in such a way that deliveries can still reach their destination without inhibiting the social values of the street and public space.

This study aims to explore the tools possessed by planners, as well as the designs available to accommodate these competing needs. In order to analyze the accessibility and how modern residential areas accommodate these needs, the study is applied to a physical context where availability of deliveries and social sustainability is tested. 

The global population is growing, and more people than before are moving to cities. This creates a need for increased building efficiency and possibility to work in remote environments. On today’s construction sites, there is a need to able to organize the site in a better way. In the future, autonomous vehicles will instead find it difficult to localize materials on a construction site. The autonomous vehicles can localize themselves with cameras and sensors, but they do not know how to localize the materials and items.

This report is based on a project where Volvo Construction Equipment acted as a customer and the project was performed by students from Blekinge Institute of Technology and Stanford University. The prompt for this project was “From elephants to ants – from Earth to Mars” and would later be interpreted as finding a solution for the future that will be able to function without human’s intervention.

From this project, this report was created. The following research questions for this report were:

• How can workers locate building materials on today’s construction sites?

• How will autonomous vehicles be able to locate material without human assistance in future construction sites?

To solve these problems a design-process started, using an engineering design method. This method was chosen because of the type of problem. In engineering, the problem is identified to create a solution to the problem, comparing to when studying science, a question should be answered.

The outcome from this report is a Product Service System (PSS) for a tracking system and a device for materials on today’s and future construction sites. When this solution was created no economic aspects were considered. Also, the focus of this report is the first steps of going from today’s construction sites to the future construction sites where autonomous vehicles will be used.

The result from this research shows that the same problem of organizing a construction site is a pattern that can be seen in the majority of the sites that were visited during field works. Also, the workers today have little trust in the autonomous vehicles which is a result of lacking information and communication within companies. Furthermore, to be able to move to an autonomous future the mindset and attitude has to be changed. The collected data was analysed, and the outcome was a tracing system that will enable, both humans and machines, to localize materials on today’s and future construction sites. With this solution, today’s workers can track their materials wherever it is placed, without any need of changing the site. The autonomous vehicles will be able to use the tags to localize materials when there are no humans around.

Background: The understanding of the inefficiencies that the road haulages suffer from is more important than ever, specifically in today's society where the environmental problems and technological development are rapidly increasing. The perception of the inefficiencies could support road haulage firms to improve their operations. In addition, industry and commerce can develop new technology to decrease the inefficiency in the industry. This master thesis will contribute to more knowledge regarding the inefficiencies that road haulages suffer from to favour road haulage firms and the society’s economic development. Purpose: The purpose of the master thesis is, to identify and visualize the root causes in information flow that leads to inefficiencies. The impact the inefficiencies have on the physical flow, will also be investigated.Methods: The research strategy adopted to fulfil the purpose of the thesis consist of a detailed empirical case study at two road haulage firms. The data were collected through semi- and un- structured interviews. The data were analysed with the data reduction approach. Furthermore, numerous adopted frameworks 7-waste framework, RCA & Ishikawa matrix, and data flow diagram from previous studies were used to analyse the derived data from the interviews. Results: The results revealed that road haulages were operating in similar processes and the conveyed information during an operation was similar between the two case firms. Most of the root causes in the information flow occurred during upstream operations for both case firms, which affected the downstream operations. There were 4 different types of inefficiencies that were similar in both case firms. The manual handling of information, misunderstanding during operations, customer requirements, and incorrect runs. Conclusions: The findings of this study prove that (1) the road haulage industry has a similar conveyed information flow throughout the conducted operations. (2) The root causes in the information flow that leads to inefficiencies, appeared foremost in the upstream operations for the road haulage industry. (3) The inefficiencies inhibited mostly the utilization of time and cost during operations for the road haulage industry, which was a subjective judgment from the authors based on adopted frameworks. 

This paper addresses a significant challenge experienced by infrastructure managers concerninghandling and scheduling slot requests for additional trains, or urgent track maintenance,after the master timetable has been finalized. In congested railway networks, wherepassenger trains share the tracks with freight trains and where the freight train operatorscannot fully predict the actual need for access to train slots long in advance, there is aneed for a flexible and effective timetabling revision process. Since the re-scheduling oftraffic and maintenance is a demanding task, the benefits of using computational schedulingsupport is evident. From the perspective of an infrastructure manager, we propose andexperimentally evaluate an optimization-based approach for assessment and scheduling ofadditional slot requests. When inserting several trains, the relations between time and routeoverlap as well as direction of trains, and the required computation time are investigated.The optimization-based approach relies on a Mixed Integer Linear Programming formulation.In this model, the explicit capacity restrictions of line segments and station tracks,including track and platform length, are considered. This model also permits bidirectionaltraffic on all lines where relevant. The experimental results show that optimal solutions canbe retrieved quickly in many scenarios, while for certain scenarios the proposed approachis too time-consuming. The required computation time is very dependent on the propertiesof the inserted train and maintenance slots, respectively.

Despite several developments in logistics that strive to decrease costs, tendencies can be found for increased logistical costs (Pewe, 2011, p. 17). Some factors causing increased system complexity are increased selection and faster market movements. The Warehouse Department at Carlsberg Sverige in Falkenberg has been chosen to be studied regarding continuous quality improvements. Carlsberg Sverige implemented an automated inventory system and automated order-picking system in 2012-2013, which has had about 40% increase of stock keeping units (SKUs) since the investments. Since the implementation of the automation Carlsberg Sverige has encountered challenges with other quality of goods. The system has become more sensitive and it has become more important to eliminate deviation.The study aimed to develop a way for long-term reduction or elimination of quality errors that result in negative effects to the system. A specific area investigated was regarding both effect of the organization in a holistic perspective and on a level of detail of the analyzed area. The study did not include quality of beverages, but only external quality problems of goods, such as faulty stacking of packages or plastic wrapping hanging outside of the goods.An exploratory study was conducted with predominantly quantitative data collection methods. Initially a current state mapping was made, a flow chart was created through interviews and observations of employees. Critical activities / situations were identified in the flow chart and three areas were determined for further investigation regarding quality errors. Observations were carried out where all occurred abnormalities were registered. The results were analyzed and a focus area determined for continued deeper examination. A final model was created with the influence of theories and collected data from the case study.The examined focus area was part of the fully automated warehouse. Adaptation to the new automation was still under investigation and a need for more clarity in the continuous quality improvements . From the observations, it was revealed that in approximately 70% of the observing time in the focus area, there was an error in the area affected system. Registered errors were categorized into ten groups, seven groups were included in the research scope. Out of the seven categories there were two significance regarding amount of repetitions of registered errors; unreadable label was measured 30 times, and plastic wrapping outside goods was discovered 12 times during 7 hours of measurement. Significance regarding average duration was discovered for; system errors of labels and wood detected by sensors on conveyors.Detected errors with significance were further analyzed with fishbone diagrams to find root-causes. Several common causes were discovered in the fishbone diagrams, for instance regarding inadequate methods and measurements. The discoveries functioned as basis for the final model that consists of: Key Performance Indexes (KPI's), a flow chart of affecting processes, daily whiteboard meetings, improved communication channels and a process for implementation of routines. The model developed was to fit this case study, which represents; a complex system with lots of shift work, both manual and automated processes and handling of beverage goods. Despite this, the model is considered to function as a basis to work on continuous quality improvements for organizations outside the mentioned scope, but with modifications of the model.

Urban transport systems are facing seemingly irreconcilable problems.Sustainable transport policies are necessary to address this sustainability challenge. However,their effectiveness highly depends on the public attitude towards them. A method combiningboth qualitative and quantitative research was used to redefine a sustainable transport policybased on sustainability principles, and find out the key factors of public attitude towardssustainable transport policies. Furthermore, the interrelations between these factors arerevealed by using structural equation modelling. 1685 questionnaires were sent to fourrepresentative cities in Sweden: Stockholm, Göteborg, Uppsala and Karlskrona. By using thisfirst hand data, we discovered some differences between Karlskrona and these other threecities. The negative attitudes towards reducing car use and promoting public transport inKarlskrona is opposite to the positive attitudes in other cities. The acceptance of taxes, speedlimitation and parking regulation is notably different as well. The results also indicate that citycharacteristics influence public attitudes towards sustainable transport policies more thancitizens’ characteristics. The functionality of a city is the most significant factor. Moreover,the results show a high dependence on individual car use. This suggests that planners shouldnot use the experience gained from other cities without investigating actual local conditions.

This study analyzes the determinants of road freight companies' adoption intention regarding the electric road systems (e-roads) on Swedish motorways. A research model was developed based on the technology adoption literature and Technology-Organization-Environment (TOE) framework. Survey data were gathered from 160 Swedish road freight companies, and structural equation modeling was performed to test the model empirically. The results reveal relative advantage, a technology characteristic, as the main determinant of road freight companies' intention to adopt electric road systems. The study contributes to the TOE framework by reporting on the indirect effects of technological and organizational determinants on organizational intention to adopt technologies. © 2023 World Scientific Publishing Company.

One of the key challenges related to the threat posed by the COVID-19 pandemic is preservation of employment and protecting staff who are working in port operations and struggling to keep ports operating for ship calls. These activities performed by port labour are deemed to be crucial for the EU and European ports, since 75% of the EU external trade and 30% of intra-EU transport goods are moved by waterborne transport. As a response to the global lockdown and the vulnerability of global supply chains, the majority of international organisations and maritime ports networks have shortlisted measures necessary to keep the severe effects of the lockdown to a minimum. One of the key measures identified is how to limit physical interaction. As an effect, millions of people and organisations across the globe have had to use and/or increase their deployment of digital technologies, such as digital documentation, tracing information systems and digital group-working platforms. Hence, blockchain and data-enabling systems have become to be recognised as a core element maintaining the uninterrupted flow of goods and services at ports. In pursuing uninterrupted trade and keeping ports open and running, this research paper addresses how the current situation afflicts the small and medium-sized ports located on the Baltic Sea which are argued to be critical actors of the port-centric logistics' ecosystem. Given the topicality of this research and addressing the research gap, the authors suggest a conceptual capacity-building framework for port employees. This suggested framework is based on empirical insights: primary and secondary data collected from the project Connect2SmallPorts, part-financed by the Interreg South Baltic Programme 2014-2020 from the European Regional Development Fund (ERDF). The conceptual framework aims towards a practical training programme dedicated to fill in the missing skills or expand the limited competence of human resources and ports' capacity when adapting or advancing digitalisation in the ports' ecosystems. In particular, specific areas of capacity building are addressed and individual solutions suggested to foster a digital transformation of ports. The conceptual training framework is designed as a training tool indicating opportunities to help ports upgrade their competences with the blockchain technology, and to advance their transportation, environmental and economic performance with improved digitalisation. For this purpose, the conducted research employed mixed methods and applied concepts and approaches based on the field of management. For example, the construct of absorptive capacity, organisational learning, transformation, resource-based view and the concept of dynamic capabilities are included in the ecosystem discourse and are linked with open innovation and service design. The research presented in this article provides both theoretical and practical contributions, in which the affected stakeholders can test and utilise the developed tool as well as transfer it to other regions. © 2021 Christopher Meyer et al., published by Sciendo 2021.

The study was conducted in 2020 by The SustainTrans team at Blekinge Institute of Technology with support fromNetPort Science park as a pre-study within the Roadmapper project, an effort to accelerate regional transitions to sustainable transport.This particular pre-study was financed by the municipalities of Karlshamn, Olofström and Sölvesborgbut it also benefits the larger Roadmapper project that, in turn, is supported by a whole consortium of public and private organizations, led by the Swedish Energy Agency. The preliminary resultswere reported to themunicipalitiesin December 2020but thescientific review of the studywas finalized in March2021. 

As an alternative to traditional fuel and energy source LNG (Liquefied Natural Gas) has many advantages, such as lower emissions while providing a means of energy for trucks, trains and ships. In focusing on the maritime transport sector the reasons for using LNG make a convincing business case, but lead to many discussions on LNG investments. The key issue has been is: “should investment be implemented first on LNG bunkering stations and then wait for the market to build ships to use the facilities or should investment wait until there is a demand?” Obviously, this creates a “chicken-and-egg” situation on when and where to invest for LNG use to take place. The initial experiences in using LNG in maritime and road transport suggest that the transport firms often take the risk themselves by not only investing into the transport units (ships, trucks) themselves but also invest into the infrastructure as well, e.g., developing LNG bunkering facilities. At the same time with these large initial investments for developing LNG bunkering networks there are more and more requests for identifying optimal solutions, often are based on real LNG fuel demand in ports and on the roads. This paper is oriented on the study for optimal bunkering network creation, which is argued to help with improved efficiency in the supply of LNG fuel to transport users. In addition, optimal investments for LNG bunkering networks can be realized. © 2018 Kaunas University of Technology. All rights reserved.

Today different transport modes use to deliver cargo between regions, from ports to final destination location or visa-versa. It is quite common to use road transport, which can deliver cargo “from door to door” but road transport causes big environmental impact. Considering alternative possibilities (road, railway and/or inland waterway transport) to decrease environmental impact from transport, it is very important. Based on theoretical and experimental tests, were find optimal solutions, which transport mode make minimum environmental impact and could be the most technically and economically effective solution. Traffic congestion on the roads, in some cases very high railway traffic in some regions, generates requirements by many stakeholders on ways to decrease the environmental impact from transport modes, which studded in Article to find and identify optimal transportation solutions with minimum environmental impact. A theoretical method evaluation conducted on the optimal transportation possibility that minimizes environmental impact. A transport modes environmental comparative index (ECI) is developed and used for evaluations. This paper presents possible alternative transportation conditions based on multi-criteria evaluation system, proposes theoretical basis for the optimal solutions from environmental and economic point of view, and provides for experimental testing during the specific case study, and finally provides recommendations and conclusions. © 2022 The Authors.

Optimization transportation cargo and passengers between ports and regions are very important, because industrial regions are located some distance from ports. The demand for energy request for the movement of transport is a necessity in the modern world. Transport and activity called transportation are used daily, everywhere, and a lot of energy is needed to power the various transport modes. Today different transport modes are being used to transport passengers and cargo. It is quite common to use road transport, which can transport passengers and cargo from door to door. Considering alternative possibilities (road, railway and/or inland waterway transport), it is important, based on theoretical and experimentation, to identify optimal solutions. In finding transport modes that are either most technically or economically effective, we could unearth possible solutions which would require minimal energy use. Unfortunately, with increased transportation, this often leads to traffic congestion on the roads, which requires additional energy (fuel). This situation generates requirements from many stakeholders in terms of finding ways to decrease the transportation time and energy (fuel) consumed by transport modes. A theoretical method evaluation is conducted on the optimal transportation possibility that minimizes transportation time and energy (fuel) use by employing graph theory, which is presented in this paper. The scientific contribution is the development of a transport modes comparative index, which is then used for evaluations. This paper presents possible alternative transportation conditions based on a multi-criteria evaluation system, proposes a theoretical basis for the optimal solutions from an eco-economic perspective that considers energy, and provides for experimental testing during a specific case study. The final results from the case study provide recommendations and conclusions. © 2022 by the authors.

The increasing demand for Liquid Natural Gas (LNG) is causing many challenges for users and suppliers worldwide. Though there is strong interest in using LNG, the research published in this paper indicates there are challenges in developing adequate delivery and distribution chains within the supply chain. Ideally, LNG distribution chains should be created on the basis of user demands and need. In this paper we have articulated an optimisation model that considers the various potential users and their characteristics in order to identify if possibilities and prospects exist in developing an adequate LNG supply chain. The case study of Lithuania and Latvia serves as a model from which we are able to use our tool to help identify the factors for success in creating such LNG supply chains. © 2017 Kaunas University of Technology. All rights reserved.

Nowadays, ports are actively seeking ways to improve their safety and operational activity. An essential driver in this context is digitalisation. Since seaports are also key actors for the sustainable development of coastal regions, it is important that they transform into smart port ecosystems. Hence, the automation and digitisation of ports’ operations are important not only for the ports themselves, but also for the regions and countries hosting regional port ecosystems. Studies on the digitalisation level of ports bear the potential to detect optimal ways for increasing safety, security and visibility in terms of the digital transformation, as well as attracting passengers and freight flows, which in turn positively affects not only the ports, but particularly also the sustainable development of coastal regions. Therefore, the paper presents the results of a conducted assessment of small and medium-sized ports’ digitisation level as well as introduces ways and recommendations how to improve the level of digitisation on the path towards becoming a smarter port ecosystem. The research builds upon key insights from the Connect2SmallPorts project, part-financed by INTERREG South Baltic Programme 2014–2020. Thereby, the research utilises collected primary data concerning ports located in the Baltic, North and Mediterranean Sea Regions. Thus, the study bases on well-grounded theoretical and practical findings in the maritime science field in the nexus of digital transformation. © 2021 Kaunas University of Technology. All rights reserved.

Agentteorin behandlar förhållandet mellan två parter, till exempel en leverantör (agent) och en kund (principal), med fokus att bland annat hitta det mest effektiva kontraktet. Trots att relationen regleras av ett kontrakt kan det uppstå situationer där agentens agerande inte behöver ligga i linje med principalens intressen. Detta har att göra med de olika antagande som agentteorin bygger på till exempel syn på risk, maximering av egennytta och värdet av information. Denna problematik är av relevans vid materialpåfyllnadsstrategier. En kundinitierad påfyllnadsstrategi kännetecknar en traditionell inköpsprocess initierad av kunden. Ytterligare ett exempel av en påfyllnadsstrategi är leverantörsstyrda konsignationslager. Påfyllnadsstrategin innebär att leverantören sköter påfyllnaden av kundens lager och att leverantören även står för lagerförda produkters kapitalbindning. Till skillnad mot en kundinitierad påfyllnadsstrategi förändrar denna strategi grunden för samarbetet mellan parterna där leverantörens möjligheter att styra påfyllnadsprocessen är betydligt större. Detta kan teoretiskt sett förändra hur olika faktorer påverkar lagernivå. Det är därför av intresse att undersöka om samma faktorer som påverkar lagernivåer för kundinitierad påfyllnadsstrategi även påverkar leverantörsstyrda konsignationslager.

Syftet med denna rapport är att undersöka hur produktionsledtid, förbrukning och volatilitet i efterfrågan med hänsyn till geografiskt avstånd påverkar lagernivån för leverantörsstyrda konsignationslager.

För att besvara frågeställningen har en regressionsanalys av typen Fixed Effects Generalized Least Squares med lagged dependent variable använts.

Från analysen fastställs statistiskt signifikanta samband mellan produktionsledtid och lagernivå samt mellan förbrukad mängd produkter och lagernivå. Det konstateras att en ökning av produktionsledtid ökar lagernivåer och att en högre förbrukning av produkter minskar lagernivåer. För volatilitet i efterfrågan med hänsyn till geografiskt avstånd uppnås inget statistiskt samband och därför kan inga vidare slutsatser dras för denna variabel.

Firms today maintain complex supply chains containing many dependent stages to manufacture a product to the end customer. Here lies a dilemma, keeping extensive inventory generate higher customer satisfaction by enabling quick deliveries. This increase in inventory, however, comes at an increase in costs. Finding the balance between minimizing costs and increasing customer satisfaction is difficult. The search to solve this dilemma has led to the development of research in the area of inventory control. For long, optimization models have been developed to improve the supply chain holistically, from raw material-to-customer. Those approaches, however, have not been possible to apply in practice by being too computationally demanding. Also, they imply organizational adjustments that create issues beyond the optimization flow of materials. It explains why, despite significant technolog- ical advancements, many firms prefer to use non-holistic approaches. This thesis evaluates the tangible effect of using holistic optimization.

The objective of this thesis’ is to determine optimal stock levels between dif- ferent stages of production. The method used is an optimization of inventory, stemming from Inventory Control Theories. Inventory optimization is at the cross- roads between logistics, economics, and management. The thesis builds on a case of inventory optimization identified at a large manufacturing firm. The literature in inventory optimization has indicated that inventory and customer services is a domain of continuous development. This thesis focuses on the adequation between total inventory cost and the customer service level. The method used to answer this question is the Multi-Echelon optimization model. This model seeks to optimize reorder points and reorder quantities. A comparison to the Single-Echelon model and a Base-Case allows for evaluation of the performance, discrete event simulation based on a company’s supply chain measures the effect. The result shows that the Multi-Echelon optimization model outperforms the two non-holistic models. This thesis shows the triple advantages of the Multi-Echelon optimization allowing for leaner manufacturing system by 1- reduction of waste, 2- less unproductive capi- tal tied up in inventory, and 3- adding value by increasing customer satisfaction. Other areas of inventory optimization in manufacturing should be able to take ad- vantage of the Multi-Echelon optimization model to manage materials stock-levels, delivery flows, and customer satisfaction.

Electrification of the transport sector has been pointed out as a key factor for tackling some of today's main challenges, such as global warming, air pollution, and eco-system degradation. While numerous studies have investigated the potential of electrifying passenger transport, less focus has been on how road freight transport could be powered in a sustainable future. This study looks at Electric Road Systems (ERS) in comparison to the current diesel system. The Framework for Strategic Sustainable Development was used to assess whether ERS could be a stepping stone on the way towards sustainability. Strategic life-cycle assessment was applied, scanning each life-cycle phase for violations against basic sustainability principles. Resulting sustainability "hot spots" were quantified with traditional life-cycle assessment. The results show that, if powered by renewable energy, ERS have a potential to decrease the environmental impact of freight transport considerably. Environmental payback times of less than five years are achievable if freight traffic volumes are sufficiently high. However, some severe violations against sustainability principles were identified. Still, ERS could prove to be a valuable part of the solution, as they drastically decrease the need for large batteries with high cost and sustainability impact, thereby catalyzing electrification and the transition towards sustainable freight transport. © 2018 by the authors.

The cosmetic industry is characterized by having the ability to offer a wide range of differentiatedproducts. This leads in turn retailers to make strategic decisions regarding assortment planning. Itmeans choosing the right breadth and depth of products that should be allocated to a distributioncenter. This is essential to the ability to answer the needs of their customers. Besides the range ofproducts retailers also face the choice of the optimal location of the distribution center. Both the rangeof carefully chosen products and agglomeration economies affect efficiency, customer satisfaction aswell as transportation delays and costs. Therefore, in this research, we have developed a framework tooptimize both the ranges of products and agglomeration economies. To do this study, we havecollaborated with LYKO AB, a firm within the cosmetics industry offering highly differentiatedproducts with e-commerce solutions. We frame their problem by creating a three-step optimizationsolution. It is combined with a demand module, optimization module, and localization module. Theresult showed that 36 products within the selected subset had a high demand,10 out of these productsfurther maximized the profit of the firm. The localization module showed that among the fourconsidered countries in Europe (Germany, Netherlands, Poland, and Austria) the optimal geographicallocation to locate the warehouse was Germany. This result was based on logistic decisions such ascustomer population, best distance, and level of competition. In conclusion, to be able to optimize theassortment planning of highly differentiated products to maximize the profit based on localization andcustomer demand complexity one can use a three-step optimization solution.