Paper Info
Title
Adaptive Large Neighborhood Search for Vehicle Routing Problem with Cross-Docking
Abstract
Cross-docking is considered as a method to manage and control the inventory flow, which is essential in the context of supply chain management. This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD which has been extensively studied due to its ability to reduce the overall costs occurring in a supply chain network. Given a fleet of homogeneous vehicles for delivering a single type of product from suppliers to customers through a cross-dock facility, the objective of VRPCD is to determine the number of vehicles used and the corresponding vehicle routes, such that the vehicle operational and transportation costs are minimized. An adaptive large neighborhood search (ALNS) algorithm is proposed to solve the available benchmark VRPCD instances. The experimental results show that ALNS is able to improve 80 (out of 90) best known solutions and obtain the same solution for the remaining 10 instances within short computational time. We also explicitly analyze the added value of using an adaptive scheme and the implementation of the acceptance criteria of Simulated Annealing (SA) into the ALNS, and also present the contribution of each ALNS operator towards the solution quality.
Year
DOI
Venue
2020
10.1109/CEC48606.2020.9185514
2020 IEEE Congress on Evolutionary Computation (CEC)
Keywords
DocType
ISBN
cross-docking,vehicle routing problem,scheduling,adaptive large neighborhood search
Conference
978-1-7281-6930-9
Citations 
PageRank 
References 
0
0.34
7
Authors
4
Name
Order
Citations
PageRank
Aldy Gunawan114212.18
Audrey Tedja Widjaja203.04
Pieter Vansteenwegen3102648.63
Vincent F. Yu442427.32