Toward sustainable optimization with stackelberg game between green product family and downstream supply chain

Abstract

Green products are increasingly considered by companies, owing to the significant attention of government regulations, customers’ requests and competitors. Here, we deal with Green Product Families (GPFs) by selecting green components, modules and products which are produced based on the assembling to order (ATO) approach to cover diverse customer needs. Designing a GPFs is important but not sufficient for sustainable optimization. In fact, we need to simultaneously consider the supply chain of a GPF to control the pollution generated in the downstream supply chain. For a sustainable optimization approach, this joint configuration is usually structured based on economic, environmental and social criteria, which are often conflicting. Maximizing the total profit, maximizing the product greenness and minimizing the total supply chain cost with environmental considerations constitute our main objectives. We apply the Leader-Follower Stackelberg game in order to present a joint configuration of the GPF and its green supply chain (GSC). Maximizing the total profit (an economic criterion) and maximizing product greenness (a social criterion) are the two objectives of the leader problem to attain the GPF. This is done by determining the optimal selection of components, modules, and product variants. The follower of the joint problem seeks to configure the supply chain by minimizing the GSC costs considering carbon emissions (an environmental criterion). This includes the optimal selection of suppliers, manufacturers, the assembly plant, distribution centers, and retailers. A bi-level multi-objective linear programming problem (B-MOLP) is used to model the Stackelberg game of the GPF-GSC. A new particle swarm optimization algorithm, named as bi-level multi-objective PSO (B-MOPSO), is developed to solve the proposed bi-level multi-objective model. To show the validity of the proposed model and the efficiency of our algorithm, a case study at a mobile phone company is worked through. Finally, certain results with some managerial implications are obtained through a sensitivity analysis.