A Necessity-Based Optimization Approach for Closed-Loop Logistics Considering Carbon Emission Penalties and Rewards under Uncertainty

Author:

Li Botang1,Liu Kaiyuan2,Chen Qiong3,Lau Yui-yip4ORCID,Dulebenets Maxim A.5ORCID

Affiliation:

1. Department of Port & Shipping Management, Guangzhou Maritime University, Guangzhou 510725, China

2. Logistics Engineering and Simulation Laboratory, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China

3. Navigation College, Jimei University, No.1 Jiageng Road, Jimei District, Xiamen 361021, China

4. Division of Business and Hospitality Management, College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hong Kong, China

5. Department of Civil and Environmental Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310, USA

Abstract

The recycling of waste products can bring enormous economic and environmental benefits to supply chain participants. Under the government’s reward and punishment system, the manufacturing industry is facing unfolded pressure to minimize carbon emissions. However, various factors related to the design of closed-loop logistics networks are uncertain in nature, including demand, facility capacity, transportation cost per unit of product per kilometer, landfill cost, unit carbon penalty cost, and carbon reward amount. As such, this study proposes a new fuzzy programming model for closed-loop supply chain network design which directly relies on fuzzy methods based on the necessity measure. The objective of the proposed optimization model is to minimize the total cost of the network and the sum of carbon rewards and penalties when selecting facility locations and transportation routes between network nodes. Based on the characteristics of the problem, a genetic algorithm based on variant priority encoding is proposed as a solution. This new solution encoding method can make up for the shortcomings of the four traditional encoding methods (i.e., Prüfer number-based encoding, spanning tree-based encoding, forest data structure-based encoding, and priority-based encoding) to speed up the computational time of the solution algorithm. Several alternative solution approaches were considered to evaluate the proposed algorithm including the precision optimization method (CPLEX) and priority-based encoding genetic algorithm. The results of numerous experiments indicated that even for large-scale numerical examples, the proposed algorithm can create optimal and high-quality solutions within acceptable computational time. The applicability of the model was demonstrated through a sensitivity analysis which was conducted by changing the parameters of the model and providing some important management insights. When external parameters change, the solution of the model maintains a certain level of satisfaction conservatism. At the same time, the changes in the penalty cost and reward amount per unit of carbon emissions have a significant impact on the carbon penalty revenue and total cost. The results of this study are expected to provide scientific support to relevant supply chain enterprises and stakeholders.

Funder

Fujian Provincial Department of Education

Xiamen Society Scientific Research [2023]

Guangzhou Basic Research Program Basic and Applied Basic Research Project

higher education institutions of Guangdong Provincial Department of Education

Educational Science Planning Project of Guangdong Provincial Department of Education

Discipline Construction Project of Guangzhou Jiaotong University

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

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