Designing an Agile Closed-Loop Supply Chain with Environmental Aspects Using a Novel Multiobjective Metaheuristic Algorithm

Liu Kai,Ramina Malekalipour Kordestanizadeh,Irfan Kaymaz

doi:10.1155/2021/3811417

Abstract

Success in supply chain implementation depends on the way of dealing with market changes and customer needs. Agility is a concept that has been introduced in recent years to improve the supply chain. On the other hand, paying attention to environmental problems is another issue, and chains are trying to increase their popularity by focusing on this issue. Considering the importance of this issue, designing a multiobjective closed-loop supply chain network has been discussed in this research. The main contribution of this research is the integration of green and agility concepts in supply chain design. In this regard, a mathematical model is presented with economic, environmental, and agility objectives. First, the mathematical model is solved using the Epsilon constraint method, and then, the multiobjective weed algorithm is proposed to solve the model. The results of comparisons between the two methods show that the multiobjective weed algorithm has performed well in terms of various metrics of NPS, SNS, and Max Spread. In terms of the solving time, the average solving time of this algorithm was about 0.1% of the solving time of the Epsilon constraint method. Moreover, all cases show the superiority of the multiobjective weed algorithm over the Epsilon constraint method in solving the proposed mathematical model.

Highlights

Success in supply chain implementation depends on the way of dealing with market changes and customer needs
A multiobjective mathematical model with a green and agile approach was presented. e integration of agility and environmental concepts in supply chain design is an important innovation of research. e use of a new multiobjective metaheuristic algorithm called MOIWO is the innovation of this research. e results showed that economic, environmental, and agility objectives in the supply chain are in conflict with each other, and their independent optimization is not efficient enough to be used in the supply chain
The MOIWO algorithm has performed well compared with the Epsilon constraint method

Summary

Parameters

Dtcp: demand of customer c for product p during period t SCtsr: the purchasing cost of one unit of raw material r from supplier s during period t M_Ctip: the manufacturing cost of each unit of product p in factory i during period t ICtjp: the inspection and recycling cost of each unit of product p in distribution center j during period t HCtjP: maintaining the cost of each unit of product p in distribution center j during period t. ESItsir: the unit of CO2 pollution resulting from the transportation of raw material r from supplier s to factory i during period t. EJCtjcpl: the unit of CO2 pollution resulting from the transportation of product p from distribution center j to customer c with transportation system l during period t E_Ctip: the unit of CO2 pollution resulting from the manufacture of product p in factory i during period t nrp: the consumption coefficient of raw material r in product p mp: the rate of capacity utilization in manufacture of product p. W1: the impact (weight) of agility of factories on the agility of the whole supply chain. W2: the impact (weight) of agility of distribution centers on the agility of the whole supply chain. W3: the impact (weight) of agility of suppliers on the agility of the whole supply chain

Decision Variables

Mathematical Model Relations

Numerical Results

Conclusion