Location-Routing Optimization with Split Demand for Customer Self-Pickup via Data Analysis and Heuristics Search

Zhuo Sun, Ni Yan (), Yining Sun () and Haobin Li ()
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Zhuo Sun: Department of Logistics, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, P. R. China
Ni Yan: Department of Logistics, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, P. R. China
Yining Sun: Department of Logistics, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, P. R. China
Haobin Li: Department of Industrial Systems Engineering & Management, Faculty of Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore 117576, Singapore

Asia-Pacific Journal of Operational Research (APJOR), 2019, vol. 36, issue 06, 1-24

Abstract: Customer self-pickup, offered as an option at most distribution centers, can provide flexible service times and save operational costs. Customers can either choose to self-pickup their demand or to have it delivered by a traditional way. At each customer point, the delivery demand is split, with the amount depending on the service and personal characteristics. In this situation, how to efficiently locate distribution centers and route deliveries becomes a vital problem for express companies that has not been studied in the literature. In this paper, for the first time, we propose a mathematical programming model for optimizing the location-routing problem with split demand (LRP-SD), together with a delivery ratio analysis model to predict self-pickup and delivery demand. To adapt the model to real-world cases, two heuristics as used in large-scale simulation-based optimization are devised and implemented. One is biogeography-based optimization (BBO) for solution speed, and the other is an adaptive large neighborhood search (ALNS) for solution quality. The two algorithms are compared using real data from a Shanghai-based express delivery company.

Keywords: Location routing; split demand; self-pickup; ALNS; BBO (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S021759591940013X

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