Green closed-loop supply chain network design considering cost control and CO2 emission

Lufei Huang,Liwen Murong,Wencheng Wang

doi:10.1108/mscra-02-2019-0005

Abstract

PurposeEnvironmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control.Design/methodology/approachA biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand.FindingsThe proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO2 emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO2 emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO2 emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO2 Emissions Control Reduction.” It can provide managers with a way to double-target conversion.Originality/valueWe proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.

Highlights

Chains are growing and becoming more complex as demands increase
The results show that the presented mixed-integer programming (MIP) model with uncertain demand is an effective way of formulating and solving closed-loop supply chain (CLSC) network design problem
In order to ensure the accuracy of the CLSC model, a series of assumptions are proposed as follows: (1) Background information of elements in the green CLSC network, such as locations of involved points, options for facilities in environmental levels and capacity levels, the unit cost of logistics activities, and the unit emission emitted during logistics activities, is known; (2) uncertain demand of different customer segments can be met by an alternative facility; (3) attributes of products remain the same during the logistic activities, and capacity constraints in the reverse phase remain the same as in the forward phase

Summary

Introduction

Chains are growing and becoming more complex as demands increase. consumers tend to require higher quality of products. The proposed optimization model in this study integrates environmental factors and factors of CLSC design issues, including facility allocation, facility capacity, and environment-level, uncertain demand and channel flow decision, which is rather useful for decision-makers. In order to ensure the accuracy of the CLSC model, a series of assumptions are proposed as follows: (1) Background information of elements in the green CLSC network, such as locations of involved points, options for facilities in environmental levels and capacity levels, the unit cost of logistics activities, and the unit emission emitted during logistics activities, is known; (2) uncertain demand of different customer segments can be met by an alternative facility; (3) attributes of products remain the same during the logistic activities, and capacity constraints in the reverse phase remain the same as in the forward phase. Ψ cgj fcg g∈G;c∈C;j∈J (3) The variable cost of manufacturing, recovery, distribution, collection, and disassembling is calculated by

Objective

Findings

Conclusions