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Solving Multiple Objective Optimization Problem using Multi-Agent Systems: A case in Logistics Management
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2020 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Background: Multiple Objective Optimization problems(MOOPs) are common and evident in every field. Container port terminals are one of the fields in which MOOP occurs. In this research, we have taken a case in logistics management and modelled Multi-agent systems to solve the MOOP using Non-dominated Sorting Genetic Algorithm-II (NSGA-II).

Objectives: The purpose of this study is to build AI-based models for solving a Multiple Objective Optimization Problem occurred in port terminals. At first, we develop a port agent with an objective function of maximizing throughput and a customer agent with an objective function of maximizing business profit. Then, we solve the problem using the single-objective optimization model and multi-objective optimization model. We then compare the results of both models to assess their performance.

Methods: A literature review is conducted to choose the best algorithm among the existing algorithms, which were used previously in solving other Multiple Objective Optimization problems. An experiment is conducted to know how well the models performed to solve the problem so that all the participants are benefited simultaneously.

Results: The results show that all three participants that are port, customer one and customer two have gained profits by solving the problem in multi-objective optimization model. Whereas in a single-objective optimization model, a single participant has achieved earnings at a time, leaving the rest of the participants either in loss or with minimal profits.

Conclusion: We can conclude that multi-objective optimization model has performed better than the single-objective optimization model because of the impartial results among the participants.

Place, publisher, year, edition, pages
2020.
Keywords [en]
Multiple Objective Optimization Problem, Non-dominated Sorting Genetic Algorithm-II, Multi-agent systems, Multi-objective optimization model, Single-objective optimization model.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-20745OAI: oai:DiVA.org:bth-20745DiVA, id: diva2:1501741
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVADA Master Qualification Plan in Computer Science
Supervisors
Examiners
Available from: 2020-11-18 Created: 2020-11-18 Last updated: 2020-11-18Bibliographically approved

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Solving Multiple Objective Optimization Problem using Multi-Agent Systems: A case in Logistics Management(674 kB)818 downloads
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Citation style
  • apa
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Output format
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