Location-Routing Problem with Simultaneous Home Delivery and Customer’s Pickup for City Distribution of Online Shopping Purchases

Lin Zhou, Xu Wang, Lin Ni and Yun Lin
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Lin Zhou: College of Mechanical Engineering, Chongqing University, Chongqing 400030, China
Xu Wang: Chongqing Key Laboratory of Logistics, Chongqing University, Chongqing 400030, China
Lin Ni: College of Mechanical Engineering, Chongqing University, Chongqing 400030, China
Yun Lin: College of Mechanical Engineering, Chongqing University, Chongqing 400030, China

Sustainability, 2016, vol. 8, issue 8, 1-20

Abstract: With the increasing interest in online shopping, the Last Mile delivery is regarded as one of the most expensive and pollutive—and yet the least efficient—stages of the e-commerce supply chain. To address this challenge, a novel location-routing problem with simultaneous home delivery and customer’s pickup is proposed. This problem aims to build a more effective Last Mile distribution system by providing two kinds of service options when delivering packages to customers. To solve this specific problem, a hybrid evolution search algorithm by combining genetic algorithm (GA) and local search (LS) is presented. In this approach, a diverse population generation algorithm along with a two-phase solution initialization heuristic is first proposed to give high quality initial population. Then, advantaged solution representation, individual evaluation, crossover and mutation operations are designed to enhance the evolution and search efficiency. Computational experiments based on a large family of instances are conducted, and the results obtained indicate the validity of the proposed model and method.

Keywords: location-routing problem; simultaneous home delivery and customer’s pickup; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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