Two-echelon location-routing optimization with time windows based on customer clustering. (15th August 2018)

• Record Type: Journal Article
• Authors: Wang, Yong; Assogba, Kevin; Liu, Yong; Ma, Xiaolei; Xu, Maozeng; Wang, Yinhai
• Is Part Of: Expert systems with applications. Volume 104(2018)
• Page Start: 244
• Page End: 260
• Publication Date: 2018-08-15
• Subjects: Location routing optimization with time windows -- Periodic demand forecasting -- Customer clustering -- Validity measurement function -- Non-dominated Sorting Genetic Algorithm-II (NSGA-II); Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.03.018
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A two-echelon location-routing problem is optimized based on customer partitioning. A mathematical model is proposed to minimize cost and maximize service reliability. Customers demand uncertainty is assumed and estimated during optimization. A modified NSGA-II method and a validity function are designed to obtain solutions. Computational results reveal that M-NSGA-II outperforms MOGA and MOPSO algorithms. Abstract: This paper develops a three-step customer clustering based approach to solve two-echelon location routing problems with time windows. A bi-objective model minimizing costs and maximizing customer satisfaction is formulated along with an innovative measurement function to rank optimal solutions. The proposed methodology is a knowledge-based approach which considers customers locations and purchase behaviors, discovers similar characteristics among them through clustering, and applies exponential smoothing method to forecast periodic customers demands. We introduce a Modified Non-dominated Sorting Genetic Algorithm-II (M-NSGA-II) to simultaneously locate logistics facilities, allocate customers, and optimize the vehicle routing network. Different from many existing version of NSGA-II, our algorithm applies partial-mapped crossover as genetic operator, instead of simulated binary crossover, in order to properly handle chromosomes. The initial population is generated through a nodes' scanning algorithm which eliminates sub-tours. Finally, to demonstrate the applicability of our mathematical model and approach, we conduct two empirical studies on generated benchmarks and the distribution network of a company in Chongqing city, China. Further comparative analyses with multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) algorithm indicate that M-NSGA-II performs better in terms of solution quality and computation time. Results also support that: (1) the formation of clusters containing highly similar customers improves service reliability, and favors a productive customer relationship management; (2) considering product preference contributes to maximizing customer satisfaction degree and the effective control of inventories at each distribution center; (3) clustering, instead of helping to improve services, proves detrimental when too many groups are formed. Thus, decision makers need to conduct series of simulations to observe appropriate clustering scenarios.