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postgraduate thesis: Modeling and solving decentralized supply chain management problems using multi-agent system with dynamic-control agents

TitleModeling and solving decentralized supply chain management problems using multi-agent system with dynamic-control agents
Authors
Issue Date2015
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Chau, W. D. [鄒允軒]. (2015). Modeling and solving decentralized supply chain management problems using multi-agent system with dynamic-control agents. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5387964
AbstractManaging large scale supply chains are never an easy task. Numerous researches have put emphasis on supply chain modeling and optimization to assist businesses in searching for the best practices so as to endure the extremely competitive business landscape. To some, the paradigm of centralized supply chain management is adequate for solving its strategic and operational problems. Yet with the improper use of authoritative assumptions, the efficiency of the management process is often jeopardized. Furthermore, current researches in decentralized supply chain are mostly focused on dyadic or linear relationship and seldom consider quantitative modeling and analysis with scalability. Recent development in multi-agent systems provided a means for such a modeling methodology and hence researches in this area. To enhance model representativeness and computational efficiency, vision-based control models that are able to simulate individual operational and strategic traits are developed. In this research, pyramidal agent alignment is proposed for simulating the management-operation dimension with regards to decision exercising and bargaining power management. The system offers one thousand supply chain agents that are simulated in a mono-layer, multi-tier network in real time. Stochastic and dynamic behaviors of the network are handled by statistical regression on scenario-based model evaluation. The proposed design enabled grand scale supply chain modeling and optimization that follows a general or custom simulation supported optimization architecture. Network governance problems and dynamic steering problems are considered and solved using genetic algorithm and dynamic programming. The thesis looks into the potential benefits and limitations of the proposed methods in details, and future research directions are discussed.
DegreeDoctor of Philosophy
SubjectBusiness logistics - Management
Multiagent systems
Dept/ProgramIndustrial and Manufacturing Systems Engineering
Persistent Identifierhttp://hdl.handle.net/10722/208622

 

DC FieldValueLanguage
dc.contributor.authorChau, Wan-hin, Derek-
dc.contributor.author鄒允軒-
dc.date.accessioned2015-03-13T01:44:10Z-
dc.date.available2015-03-13T01:44:10Z-
dc.date.issued2015-
dc.identifier.citationChau, W. D. [鄒允軒]. (2015). Modeling and solving decentralized supply chain management problems using multi-agent system with dynamic-control agents. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5387964-
dc.identifier.urihttp://hdl.handle.net/10722/208622-
dc.description.abstractManaging large scale supply chains are never an easy task. Numerous researches have put emphasis on supply chain modeling and optimization to assist businesses in searching for the best practices so as to endure the extremely competitive business landscape. To some, the paradigm of centralized supply chain management is adequate for solving its strategic and operational problems. Yet with the improper use of authoritative assumptions, the efficiency of the management process is often jeopardized. Furthermore, current researches in decentralized supply chain are mostly focused on dyadic or linear relationship and seldom consider quantitative modeling and analysis with scalability. Recent development in multi-agent systems provided a means for such a modeling methodology and hence researches in this area. To enhance model representativeness and computational efficiency, vision-based control models that are able to simulate individual operational and strategic traits are developed. In this research, pyramidal agent alignment is proposed for simulating the management-operation dimension with regards to decision exercising and bargaining power management. The system offers one thousand supply chain agents that are simulated in a mono-layer, multi-tier network in real time. Stochastic and dynamic behaviors of the network are handled by statistical regression on scenario-based model evaluation. The proposed design enabled grand scale supply chain modeling and optimization that follows a general or custom simulation supported optimization architecture. Network governance problems and dynamic steering problems are considered and solved using genetic algorithm and dynamic programming. The thesis looks into the potential benefits and limitations of the proposed methods in details, and future research directions are discussed.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.subject.lcshBusiness logistics - Management-
dc.subject.lcshMultiagent systems-
dc.titleModeling and solving decentralized supply chain management problems using multi-agent system with dynamic-control agents-
dc.typePG_Thesis-
dc.identifier.hkulb5387964-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineIndustrial and Manufacturing Systems Engineering-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b5387964-

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