An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem

Longlong Leng, Yanwei Zhao, Jingling Zhang and Chunmiao Zhang
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Longlong Leng: Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, China
Yanwei Zhao: Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, China
Jingling Zhang: Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, China
Chunmiao Zhang: Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, China

IJERPH, 2019, vol. 16, issue 11, 1-28

Abstract: In this paper, we consider a variant of the location-routing problem (LRP), namely the the multiobjective regional low-carbon LRP (MORLCLRP). The MORLCLRP seeks to minimize service duration, client waiting time, and total costs, which includes carbon emission costs and total depot, vehicle, and travelling costs with respect to fuel consumption, and considers three practical constraints: simultaneous pickup and delivery, heterogeneous fleet, and hard time windows. We formulated a multiobjective mixed integer programming formulations for the problem under study. Due to the complexity of the proposed problem, a general framework, named the multiobjective hyper-heuristic approach (MOHH), was applied for obtaining Pareto-optimal solutions. Aiming at improving the performance of the proposed approach, four selection strategies and three acceptance criteria were developed as the high-level heuristic (HLH), and three multiobjective evolutionary algorithms (MOEAs) were designed as the low-level heuristics (LLHs). The performance of the proposed approach was tested for a set of different instances and comparative analyses were also conducted against eight domain-tailored MOEAs. The results showed that the proposed algorithm produced a high-quality Pareto set for most instances. Additionally, extensive analyses were also carried out to empirically assess the effects of domain-specific parameters (i.e., fleet composition, client and depot distribution, and zones area) on key performance indicators (i.e., hypervolume, inverted generated distance, and ratio of nondominated individuals). Several management insights are provided by analyzing the Pareto solutions.

Keywords: regional low-carbon location-routing problem; multiobjective optimization; fuel consumption; carbon emission; multiobjective hyperheuristics (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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