Paper Info
Title
A Binary Borg-Based Heuristic Method for Solving a Multi-Objective Lock and Transshipment Co-Scheduling Problem
Abstract
The lock and transshipment co-scheduling problem (LTCP) is studied so that the delay time of ships at a dam as well as the extra cost of transshipment is minimized. A bi-objective optimization model of LTCP is proposed, which is decomposed to a main 0–1 optimization problem and two sub-problems, the lock scheduling and continuous berth allocation problem. Meanwhile, a binary Borg (B-Borg) multi-objective evolutionary algorithm is proposed to combine with an adaptive large neighborhood search and a multi-order best fit method to solve the LTCP. In order to evaluate the feasibility of the proposed LTCP model and the B-Borg-based heuristic, a large number of instances are generated by extracting the record from historical traffic at the Three Gorges Dam. The results show that the average delay of ships at the dam is reduced because of transshipment, among which the ships with lower extra transshipment costs tend to be more likely to be transshipped. Meanwhile, it indicates the number of ships that choose to be transshipped is mainly dependent on the level of traffic congestion as well as the available quay for transshipment. Furthermore, the performance of the B-Borg is confirmed superiority by comparing with non-dominated sorting genetic algorithm and decomposition-based multi-objective evolutionary algorithm in terms of solving LTCP.
Year
DOI
Venue
2019
10.1109/TITS.2018.2841022
IEEE Transactions on Intelligent Transportation Systems
Keywords
Field
DocType
Marine vehicles,Containers,Delays,Optimization,Cranes,Resource management,Evolutionary computation
Transshipment,Mathematical optimization,Heuristic,Evolutionary algorithm,Berth allocation problem,Simulation,Scheduling (computing),Evolutionary computation,Engineering,Optimization problem,Genetic algorithm
Journal
Volume
Issue
ISSN
20
3
1524-9050
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Bin Ji1252.96
Xiao-Hui Yuan253475.44
Yanbin Yuan314017.67