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

Authors: Abolfazl Kazemi; Mohammad Hossein Fazel Zarandi; Mahdi Azizmohammadi

Addresses: Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran ' Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran ' Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

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.

DOI: 10.1504/IJOR.2017.082611

International Journal of Operational Research, 2017 Vol.28 No.4, pp.506 - 527

Received: 19 Nov 2013
Accepted: 17 Aug 2014

Published online: 03 Mar 2017 *

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