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

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.