A variable neighborhood search based algorithm and game theory models for green supply chain design

Abstract

With the increase in the volume of greenhouse gases and pollutants, supply chain managers have sought to design and set up networks that pay special attention to environmental factors besides the economic aspects. In this research, a three-level supply chain network is considered including one manufacturer, several retailers and several customers. Greenhouse gas emissions as an environmental issue, the dependence of demand on the selling price of products and cooperative advertising have been examined to design this network. Also, several transportation systems and production methods with different environmental effects and different costs have been considered. The problem has been modeled by both the general and advertising cost classification approaches. Each model has been linearized by McCormick and sequential linear programming methods, and in each approach, Stackelberg, Nash and cooperative games have been used to determine the relationship between members of the supply chain. Finally, an algorithm has been proposed from a combination of variable neighborhood search algorithm and mathematical modeling and after adjusting its parameters by Taguchi statistical method, Stackelberg, Nash and cooperative games have been implemented on it. The results of linearization methods and the meta-heuristic algorithm show that the profit of the manufacturers, retailers and the whole supply chain depends on the type of the game selected. The profit of the whole supply chain is greater in cooperative conditions than in non-cooperative conditions, and in non-cooperative games, the final profit of the manufacturer will be greater in Stackelberg game.