A Multi-Objective Optimization Model for Closed Loop Supply Chain with Emphasis on Energy Factors in Transportation

Today, it is essential to achieve competitive market interests and since attention to environmental issues and reduced raw resources and energy has increased, design of a suitable supply chain network can be a great help in this regard. To this end, in this study, a multi-product closed-loop green supply chain has been designed. The innovative aspect of the research is the establishment of specific multiple objective functions in the field of transportation-based energy management, which is an optimization model for the green closed loop supply chain with four optimization objectives of carbon dioxide, transportation cost, energy, and waste, and the relevant model is designed by innovative genetic algorithms. And the refrigeration simulation is solved. In order to integrate the target functions, the weighting method of the targets has been used, the weight of the targets has been chosen according to the experts' opinion. The results of the algorithms' efficiency show that when we consider the four objective functions at the same time, the genetic algorithm performs better. Also, by comparing the results of these two algorithms, the efficiency of the genetic algorithm for the two objective functions of energy and cost was proven, and the efficiency of the refrigeration simulation algorithm was determined for the two objective functions of carbon dioxide and waste. This paper helps managers to benefit from green transportation and improving environmental performance and reducing costs in the entire supply chain as a complementary strategy in order to gain a sustainable competitive advantage.