Title: Optimising a logistics system with multiple procurements and warehousing using endosymbiotic evolutionary algorithm

Authors: Salik R. Yadav, Amol Ghorpade, Chetan Mahajan, Manoj Kumar Tiwari, Ravi Shankar

Addresses: Department of Manufacturing Engineering, National Institute of Foundry and Forge Technology, Ranchi 834003, India. ' Department of Manufacturing Engineering, National Institute of Foundry and Forge Technology, Ranchi 834003, India. ' Hewlett Packard GlobalSoft Limited, Chennai, India. ' Department of Industrial Engineering and Management, Indian Institute of Technology Kharagpur, Kharagpur 721302, India. ' Department of Management Studies, Indian Institute of Technology Delhi, New Delhi 110016, India

Abstract: This paper presents a robust optimisation technique viz. Endosymbiotic Evolutionary Algorithm (EEA) for a multi-stage, multi-period logistics system. The optimisation problem corresponds to a combinatorial cum integer optimisation problem where decision variables attended are options| selections and service times. As the problem corresponds to one with two interrelated sub-problems, there are ample prospects for EEA. The logistics optimisation model incorporates inventory holding costs, packaging and handling costs, finishing and transportation costs are incorporated in order to resemble recent advances in logistics research. EEA which works on the biological coevolution phenomenon based on the serial reciprocal changes in two or more cooperative interacting species has rarely been applied to a logistics optimisation problem. For the undertaken case study, it performs well and produces better or compatible solutions, in most of the cases, as compared to Genetic Algorithm (GA). The solution methodology utilised in the paper is a promising one and can be employed to resolve many other logistics and supply chain optimisation problems.

Keywords: logistics optimisation; procurement; warehousing; shipping; endosymbiotic evolutionary algorithm; random search; fitness; biological coevolution; cooperation; genetic algorithms; supply chain optimisation.

DOI: 10.1504/IJLSM.2009.021649

International Journal of Logistics Systems and Management, 2009 Vol.5 No.1/2, pp.154 - 175

Published online: 30 Nov 2008 *

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