This report details the development of a comprehensive simulation environment designed to model and analyse large-scale evacuation processes. The primary objective is to provide a structured, operationally relevant framework that represents individual and group responses to evacuation orders, movement through transportation networks, and the influence of infrastructure and resource availability on overall evacuation performance.

The environment integrates discrete-event simulation with intelligent agent modelling to operationalize established evacuation principles within a testable and verifiable framework. Development, verification, and experimentation processes are documented, emphasizing model robustness and operational relevance.

A case study based on an evacuation scenario in Karlshamn demonstrates the simulation’s ability to capture behavioural dynamics and logistical constraints, enabling scenario analysis, performance assessment, and evidence-based regional preparedness planning. Results highlight strong sensitivity to background traffic, significantly affecting rescue times and overall system performance. In the baseline scenario, private car use achieves the highest efficiency despite infrastructure limitations, whereas public transport - including buses and trains - is constrained by capacity and scheduling dependencies, potentially reducing performance without coordinated implementation. Simulation outputs align with observed traffic dynamics, supporting model validity. The framework also captures the effects of checkpoint configurations, modal distribution, and multi-destination routing on evacuation outcomes.

Product-Service System (PSS) development prioritizes the technical aspects of implementing Artificial Intelligence (AI), overlooking the strategic rationale behind its adoption. This paper investigates the impact of AI on value co-creation within industrial contexts by analyzing successful AI applications in PSS development across diverse domains like industrial systems engineering and healthcare technology. Through a multiple case study approach, this paper aims to explore the strategic motivations for using AI and its influence on the co-creation process. The analysis reveals several key benefits of AI adoption. Firstly, it fosters collaborative and iterative development, empowering both internal teams and external stakeholders to actively participate in value co-creation. Secondly, AI helps uncover novel value propositions that might remain hidden through traditional methods thus boosting the value co-creation. Finally, AI acts as a catalyst for building dynamic knowledge ecosystems. By facilitating data-driven insights and collaboration, AI enables continuous learning and adoption within the co-creation process. 

The increasing demand for products and services coupled with growing environmental concerns has necessitated a shift towards sustainable product development. Traditional methods often prioritize functionality over environmental impact, leading to resource depletion and waste generation. To address this, as environmental concerns are increasing in importance, innovative solutions are required to integrate sustainability considerations into product lifecycles.

This study investigates the role of Artificial Intelligence (AI) in promoting sustainability within product service systems. A systematic literature review was conducted to identify key AI technologies and methodologies employed across different stages of product development. The analysis focused on the impact of these technologies on environmental sustainability and business performance.

The findings reveal that AI technologies, including machine learning, natural language processing, and virtual prototyping, can significantly enhance sustainability. These tools may optimize product design, reduce material consumption, and minimize environmental impact. Furthermore, AI applications in predictive maintenance, end-of-life management, and energy efficiency contribute to resource optimization and waste reduction.

AI has the potential to transform product service system development by integrating sustainability principles. By optimizing resource utilization, reducing waste, and enhancing decision-making, AI can drive both environmental and economic benefits. While challenges such as data quality and algorithm development exist, the overall positive impact of AI on sustainability is evident.  

Effective evacuation planning is a complex challenge that aims to save lives, reduce travel time, and ensure the provision of essential care to vulnerable individuals. Evacuation simulation models are vital tools for evaluating different scenarios, identifying potential bottlenecks, and optimising strategies for large-scale evacuations.This report contains original and unpublished work that examines how these tools and their components can strengthen disaster preparedness and support decision-makers in managing the complexities of emergency response. It kicks off by outlining the key performance indicators (KPIs) relevant to large-scale evacuation, to then review current simulation approaches and their foundational building blocks. These include functions, algorithms, and models used to simulate and analyse various stages of evacuation: from pre-evacuation processes and individual decision making after a warning is issued, to traffic assignment, road network dynamics, vehicle behaviours,and shelter capacity management.The research work primarily focuses on macroscopic and mesoscopic simulation models, highlighting the strengths and limitations of the different paradigms and frameworks discussed in the literature. The report also features a curated selection of commercially available software, providing a timeline of their development, a comparison of key features, and insights into their real-world application in evacuation planning. This overview is further enriched with a series of case studies illustrating how these tools have been employed in disaster and crisis scenarios around the world.This report is published by Blekinge Institute of Technology (BTH) and is funded by the Swedish Civil Contingencies Agency (Myndigheten för samhällsskydd och beredskap, MSB) as part of the project Digital Decision Support for Large-Scale Evacuation (DISTURB). The DISTURB project is financed through the 2:4 Crisis Preparedness Fund (Anslag 2:4 Krisberedskap), which aims tosupport initiatives that strengthen society’s ability to manage crises and their consequences, and to develop and maintain the capacity for heightened civil defence preparedness.

The paper presents a replicable simulation architecture to assess the economic, environmental, and resilience performance of commercial electric and automated marine passenger transport systems. Built on Discrete Event Simulation (DES), the architecture balances simulation complexity and detail in early system development. It comprises key components such as the modelling framework, data flow, data integration and user interface. A case study in Karlskrona, Sweden, demonstrates its application, showing that DES enhances transparency, facilitates stakeholder communication, and supports iterative solution refinement. The results highlight the architecture’s potential to support sustainable urban waterborne transport and decision-making under uncertainty.

The increasing digitalization of manufacturing is transforming product development towards intelligent, service-oriented systems. This study explores the application of Digital Twins (DT) technology in smart Product Service System (sPSS) design, proposing a framework of Super-System Digital Twin (SSDT) and eXtended Reality (XR) for supporting design. The SSDT integrates service offerings, user scenarios, and product features throughout the design process. Case is applied to the conceptual design of a Smart Electric Tourist Bus in collaboration with aglobal manufacturer, the framework utilizes eXtended Reality (XR) environment to enhance decision-making and prototyping. This research validates SSDT’s role in fostering participatory design and collaborative innovation, while addressing its limitations and proposing future improvements.

Recent academic contributions explore the integration of Digital Twins (DTs) within smart Product-Service System (sPSS). This integration aims to innovate business propositions, hardware and services. However, gaps persist in developing DT environments to support early-stage collaborative innovation for sPSS, and limited studies explore how real-time synchronized digital replicas enhance value co-creation in this area. This paper addresses this gap by presenting a framework and practical example of integrating value-driven decision support into early sPSS conceptual design. A case study on the development of the Smart Electric Vehicle (SEV) conducted with a global automotive Original Equipment Manufacturer (OEM) demonstrates the framework’s efficacy. Through qualitative data analyses based on experimental validation in a case company, the DT proves effective in aiding decision makers in selecting value-adding configurations within specific scenarios. Furthermore, the DT serves as a visual decision-making tool, fostering collaboration across diverse teams within the automotive company. This collaboration facilitates value creation across practitioners with varied backgrounds, emphasizing the DT’s role in enhancing early-stage innovation and co-creation processes in the sPSS domain.

With the increased focus on smart manufacturing and Industry 4.0, the use of simulations for the creation of cyber-physical manufacturing systems is increasing. The sheet metal forming manufacturing process, commonly used for production of automotive body components, is one of the processes that currently benefits from the use of simulations without exploiting them in a cyber-physical system setup. This study set out to initially identify the key controllable and uncontrollable parameters of the sheet metal forming manufacturing process for the design of an intelligent quality controller. Subsequently, the study investigates the possibility of using data points from a stochastic numerical analysis as training data for an Artificial Neural Network. The stochastic numerical model used is based on the existing Finite Element simulation standard at Volvo Cars to allow for a seamless integration of the methodology into the standard workflow of CAE departments. Lastly, the study will present a validation of the trained Artificial Neural Network using the Volvo XC90 inner front door component as an industrial demonstrator.

Transportsektorn står för en betydande del av världens växthusgasutsläpp, och projektets mål är att minska dessa genom att förbättra resiliens, energieffektivitet och miljöpåverkan samt optimera ett marint transportsystem. Vattenburen kollektivtrafik har stor potential att minska trängsel och öka kapaciteten i städer, särskilt genom att utnyttja vattenvägar i kombination med väg- och spårtrafiknät.

Projektet har undersökt olika lösningar för ett marint transportsystem, med särskilt fokus på energieffektivitet, laddning, energilagring och infrastruktur. Ett centralt mål har varit att utveckla en modell för att optimera ett system som kan motstå störningar i energiförsörjningen. Genom simuleringar har systemets dimensionering, batterilagring och användning av bränsleceller utvärderats för att förbättra både resiliens och driftsäkerhet.

Resultaten visar att det är möjligt att utveckla ett mer resilient och flexibelt transportsystem genom att kombinera elektrifiering, landbaserade batterilager och bränsleceller ombord på fartyg. Detta system har potential att minska klimatpåverkan, förbättra tillgängligheten och öka transporteffektiviteten, särskilt i städer. Simuleringarna indikerar att systemet kan minska både energi- och tidsåtgång i urban trafik. För att vidareutveckla systemet planeras framtida tester och optimering med hjälp av AI för realtidsdrift samt tester av systemet i Karlskrona. Dessutom undersöks möjligheten att implementera en dual-use-användning av systemet, vilket skulle göra det möjligt att använda färjorna för andra ändamål, såsom evakuering av civila och djur eller transport av förnödenheter och skadade.

The transport sector accounts for a significant share of the world's greenhouse gas emissions, and the project's goal is to reduce these by improving resilience, energy efficiency and environmental impact, and optimizing a marine transport system. Waterborne public transport has great potential to reduce congestion and increase capacity in cities, especially by utilizing waterways in combination with road and rail transport networks.The project has investigated different solutions for a marine transport system, with a particular focus on energy efficiency, charging, energy storage and infrastructure. A central goal has been to develop a model to optimize a system that can withstand disruptions in energy supply. Through simulations, the system's dimensioning, battery storage and use of fuel cells have been evaluated to improve both resilience and operational reliability.The results show that it is possible to develop a more resilient and flexible transport system by combining electrification, land-based battery storage and fuel cells on board ships. This system has the potential to reduce climate impact, improve accessibility and increase transport efficiency, especially in cities. The simulations indicate that the system can reduce both energy and time consumption in urban traffic. To further develop the system, future tests and optimization using AI for real-time operation are planned, as well as tests of the system in Karlskrona. In addition, the possibility of implementing a dual-use use of the system is being investigated, which would make it possible to use the ferries for other purposes, such as evacuating civilians and animals or transporting supplies and injured people.

This paper introduces and explores triple-use innovation, a novel conceptual framework that extends beyond the established dual-use paradigm by embedding sustainability as a core dimension within innovation processes. Whereas dual-use research traditionally focuses on the civilian–military duality, triple-use innovation integrates civil, military, and sustainability domains from the outset. It argues that conventional innovation approaches create long-term vulnerabilities by relying on finite resources and infrastructures susceptible to disruption, without systematically incorporating resilience. In contrast, triple-use innovation seeks to align technological progress with societal and ecological stability by addressing how innovation can integrate crisis management, adaptation, and foresight while reducing resource intensity and unsustainable practices. Situated within planetary and social boundaries, triple-use innovation emphasises adaptability under uncertainty and offers a structure for co-designing civil, defence, and sustainability domains to maintain technological development within safe and regenerative limits.