Integrated Inbound Vehicle Routing and Scheduling Under a Fixed Outbound Schedule at a Multi-Door Cross-Dock Terminal

Abstract

This paper presents a newly formulated model for the integrated inbound vehicle routing and scheduling problem at a multi-door crossdocking terminal and develops a new cooperative co-evolutionary decomposition-based algorithm for its solution. The approach employs two different metaheuristic algorithms to solve the two decomposed subproblems alternatively and iteratively. For solving the routing subproblem, a hybrid of artificial bee colony (ABC) with simulated annealing (SA) is used. For solving the scheduling subproblem, a hybrid ant colony optimization (ACO) with local search is used. The proposed solution method is called SAABC-HACO. For comparison, also developed are three methods called ABCAL-HACO (ABCAL is short for ABC with adaptive limit), HABC-HACO (HABC is an ABC variant hybridized with local search), and ABC-ACO (simplified version of SAABC-HACO). All four methods were tested with 22 newly generated data sets. The test results indicate that: (i) all 4 methods are capable of finding the optimal solution identified by enumeration for a small dataset; (ii) SAABC-HACO emerges to be the best in terms of both solution quality and computational time; and (iii) both fewer number of doors and earlier outbound vehicle departure times can make it more difficult to find near-optimal solutions. Moreover, a number of issues are discussed; managerial insights are highlighted; and topics for further research are mentioned.