A hybrid search approach in production-distribution planning problem in supply chain using multi-agent systems

Abolfazl Kazemi, Mohammad Hossein Fazel Zarandi and Mahdi Azizmohammadi

International Journal of Operational Research, 2017, vol. 28, issue 4, 506-527

Abstract: The production-distribution planning is one of the most important approaches to support global optimisation in supply chain management (SCM), and should be solved within the integrated structure. The production-distribution planning problem (PDPP) involves the determination of the best configuration regarding location, size, technology content and product range to achieve the firm's long-term goals. On the other hand, teams of autonomous agents (ATeams), cooperating by sharing solutions through a common memory, have been proposed as a means of solving combinatorial optimisation problems. In this paper a hybrid search approach is presented using an agent-based system by considering ATeams concept for solving the PDPP. For this purpose, three algorithms are provided to solve the PDPP: genetic algorithm (GA), tabu search (TS) and simulated annealing (SA). Then we combine these algorithms using a multi-agent system and an integrated solution algorithm is proposed. Finally, the proposed approach is compared against LINGO software. The obtained results reveal that the use of multi-agent system delivers better solutions to us.

Keywords: multi-agent systems; MAS; agent-based systems; production-distribution planning; supply chain management; SCM; ATeams; autonomous agents; hybrid search; global optimisation; genetic algorithms; tabu search; simulated annealing. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=82611 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:28:y:2017:i:4:p:506-527

Access Statistics for this article

More articles in International Journal of Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().