Title: Disruption management for vehicle routing problem with time-window changes

Authors: Chunhua Ju; Guanglan Zhou; Tinggui Chen

Addresses: College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China ' School of Business Administration, Zhejiang Gongshang University, Hangzhou, 310018, China ' College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China

Abstract: In this paper, based on disruption management method, logistics distribution vehicle routing problem with soft time-window changes considering disruptions is explored. Firstly, by analysing the customers' requirements characteristics and the disadvantage of rigid time window, the soft time-window constraint is considered and the corresponding mathematical model is also setup. Secondly, according to the disruption management method, disruption factors including customers' satisfaction, path offsets, total distribution cost for time-window changes are analysed and the method of disruptions measure which considers synthetically the customers, logistics service provider and driver is proposed. A disruption recovery model for the problems is put forward based on the reposition strategy. Thirdly, in order to solve this multi-objective model, an artificial bee colony (ABC) which can optimise this NP-hard problem is developed. Finally, a numerical simulation experiment is used to verify this model and algorithm. The results illustrate the effectiveness of ABC in solving this problem.

Keywords: disruption management; logistics distribution; time-window changes; vehicle routing problem; VRP; mathematical modelling; customer satisfaction; path offsets; total distribution cost; disruption recovery; reposition strategy; swarm intelligence; artificial bee colony; ABC; numerical simulation.

DOI: 10.1504/IJSTL.2017.080568

International Journal of Shipping and Transport Logistics, 2017 Vol.9 No.1, pp.4 - 28

Received: 19 May 2015
Accepted: 24 Nov 2015

Published online: 30 Nov 2016 *

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