A Novel Multi-Objective Model for the Cold Chain Logistics Considering Multiple Effects

Feiyue Qiu, Guodao Zhang, Ping-Kuo Chen, Cheng Wang, Yi Pan, Xin Sheng and Dewei Kong
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Feiyue Qiu: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Guodao Zhang: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Ping-Kuo Chen: Business School, Shantou University, Shantou City 515063, China
Cheng Wang: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Yi Pan: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Xin Sheng: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Dewei Kong: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Sustainability, 2020, vol. 12, issue 19, 1-28

Abstract: This paper focuses on solving a problem of green location-routing with cold chain logistics (GLRPCCL). Considering the sustainable effects of the economy, environment, society, and cargos, we try to establish a multi-objective model to minimize the total cost, the full set of greenhouse gas (GHG) emissions, the average waiting time, and the total quality degradation. Several practical demands were considered: heterogeneous fleet (HF), time windows (TW), simultaneous pickup and delivery (SPD), and a feature of mixed transportation. To search the optimal Pareto front of such a nondeterministic polynomial hard problem, we proposed an optimization framework that combines three multi-objective evolutionary algorithms (MOEAs) and also developed two search mechanisms for a large composite neighborhood described by 16 operators. Extensive analysis was conducted to empirically assess the impacts of several problem parameters (i.e., distribution strategy, fleet composition, and depots’ time windows and costs) on Pareto solutions in terms of the performance indicators. Based on the experimental results, this provides several managerial insights for the sustainale logistics companies.

Keywords: cold chain; location routing problem; green logistics; multi-objective optimization; heterogeneous fleet (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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