PARTICLE SWARM OPTIMIZATION-BASED ALGORITHM FOR BILEVEL JOINT PRICING AND LOT-SIZING DECISIONS IN A SUPPLY CHAIN

This study considers joint pricing and lot-sizing policies in a single-manufacturer–single-retailer system. Because a supply chain is a hierarchical system, we adopt a bilevel programming technique to establish a bilevel joint pricing and lot-sizing model guided by the manufacturer. The objective of the problem here is to respectively maximize the manufacturer's and the retailer's net profits by determining the manufacturer's and retailer's lot size, the wholesale price and the retail price simultaneously. Following the properties of the bilevel programming problem (BLPP), we design a novel bilevel particle swarm optimization algorithm (BPSO), and it can solve BLPP without any assumed conditions of the problem. BPSO shows a good performance on eight benchmark bilevel problems. Then BPSO is employed to solve the proposed bilevel model, and the experimental data are used to analyze the features of the proposed bilevel model, and the results support the finding that BPSO is effective in optimizing BLPP.