Evolutionary algorithms for optimal operating parameters of vendor managed inventory systems in a two-echelon supply chain

This paper focuses on the operational issues of a Two-echelon Single-Vendor–Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. The operational parameters for TSVMBSC model are: sales quantity and sales price that determine the channel profit of the supply chain, and contract price between the vendor and the buyer, which depends upon the understanding between the partners on their revenue sharing. The optimal sales quantity for each buyer in TSVMBC is determined using a mathematical model available in the literature. The optimal sales price, the optimal channel profit and contract price between the vendor and buyer are determined based on the optimal sales quantity determined. Particle Swarm Optimization (PSO) and a hybrid of Genetic Algorithm and Artificial Immune System (GA–AIS) are proposed to solve this TSVMBSC problem. These two algorithms are evaluated for their solution quality. The robustness of the algorithms with their parameters are also analyzed and presented.

► Two-echelon Single-Vendor–Multiple-Buyers Supply chain (TSVMBC) problem addressed.
► Evolutionary algorithms to find optimal operating parameters for TSVMBC.
► PSO and GA–AIS algorithms are proposed.
► Algorithms are useful to identify the optimal parameters for each buyer in TSVMBC.