Two-echelon location-routing optimization with time windows based on customer clustering

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. (C) 2018 Elsevier Ltd. All rights reserved.