Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand

Abstract

Concerns about environmentally sustainable supply chain management have increased widely in recent years. As a consequence, supply chain members have cooperated with one another to make efficient contracts, frequently called green supply-chain management contracts. The purpose of this paper is to investigate one such contract between a single manufacturer and multiple retailers with limited resources for several types of products under greenhouse-gas emission regulations. Each retailer orders the products regularly within a limited budget and warehouse capacity. In response to orders, the manufacturer produces products and ships them after inspections. Demand for the products can be either known or have some uncertainty, which can best be represented using fuzzy number demand. To reflect demand properties, this paper introduces two nonlinear integer programming models, a crisp model and a fuzzy model. A genetic algorithm (GA) and hybrid genetic algorithm-pattern search (HGAS) are developed to solve the models. Numerical experiments evaluating the efficiency of the algorithms showed that the HGAS method performed better than the GA. Also observed is that the crisp model's average total costs were lower than those of the fuzzy model. The results as a whole indicate that the models can evaluate the performance of contracts and optimize cooperative green supply chain management.