Partner selection is an important issue in the supply chain management. Since environment protection has been of concern to public in recent years, and the traditional supplier selection did not consider about this factor; therefore, this paper introduced green criteria into the framework of supplier selection criteria. The aim of this research was to develop an optimum mathematical planning model for green partner selection, which involved four objectives such as cost, time, product quality and green appraisal score. In order to solve these conflicting objectives, we adopted two multi-objective genetic algorithms to find the set of Pareto-optimal solutions, which utilized the weighted sum approach that can generate more number of solutions. In experimental analysis, we introduced a {4, 4, 4, 4} supply chain network structure, and compared average number Pareto-optimal solutions and CPU times of two algorithms.
In a broad sense, green supply chain refers to the management between suppliers, their products and environment, that is to say, the environment protection principle is brought into suppliers’ management system. Its purpose is to add environment protection consciousness into original products and to improve competitive capacity in markets. The green supply chain known at present refers to supply chain effect brought about by green products proposed by European Community in the 21st century. Some companies, especially, small and medium enterprises, started to build cooperative corporations with supply chain partners, with the hope of promoting propagation of environment management initialization and designing new green products.
With increasing public awareness in environmental protection, enterprises began producing more green products than last decade. Deans (1999) pointed out, environmental protection was initiated by American industry, and environmental considerations became a significant factor to the procurement policy and selection of suppliers. WEEE and RoHS published by European Union in 2003 have exerted impact on the industries associated with electric and electronic equipment (EEE), since incompatible products are barred from the market of EU countries. Since environment protection was not taken into consideration in the traditional supply chain management, this paper introduced this concept to the green supplier selection mechanism which accords with the real situation.
The purpose of this paper was to construct an optimum mathematical planning model for defective supply chain system, and adopted two algorithms to solve this model, and obtained Pareto-optimal solutions for the supplier selection and product volume transportation problems. Four objectives were considered: (1) minimization of total cost comprised of product cost and transportation cost, (2) minimization of total time comprised of product time and transportation time, (3) maximization of average product quality, (4) maximization of green appraisal score. At last, the experimental study adopted two multi-objective genetic algorithms that were proposed by Murata, Ishibuchi, and Tanaka (1996) and Altiparmak, Gen, Lin, and Paksoy (2006) to solve the optimum mathematical planning model, and compared average number of Pareto-optimal solutions and CPU times of two algorithms to find the most efficient algorithm.
This paper is organized as follows: Section 2 is about literature review such as green supply chain management, green supplier selection criteria, WEEE/RoHS directives, defective supply chain system. Section 3 gives a problem statement and mathematical programming for supply chain. Section 4 gives a methodology. Section 5 gives computational results of three algorithms. Concluding remarks and future research are outlined in Section 6.
In this paper, we utilized two multi-objective genetic algorithms for solving green supplier selection and production volumes transportation problems. Four objectives were considered: (1) minimization of total cost comprised of product cost and transportation cost, (2) minimization of total time comprised of product time and transportation time, (3) maximization of average product quality, (4) maximization of green appraisal score. Then we adopted the weighted sum approach to obtain the set of Pareto-optimal solutions offering the decision maker to evaluate some better of alternative solutions. In order to evaluate the performance of two genetic algorithms, called as MOGA_1 and MOGA_2, we compared two indicators of each algorithm for four problems: (1) average number of Pareto-optimal solutions, (2) CPU time. Experimental results showed that MOGA_1 not only generates more number of Pareto-optimal solutions than MOGA_2 but also obtains better solutions than MOGA_2. Thence, MOGA_1 was superior to MOGA_2. In future, we hope to develop a modified weight sum approaching to obtain more Pareto-optimal solutions than previous studies. Additionally, uncertainty of costs and demands can be considered in this model, and a new solution methodology can be developed.
