A tabu search heuristic for the quay crane scheduling problem
Journal of Scheduling
Computers and Industrial Engineering
A fast heuristic for quay crane scheduling with interference constraints
Journal of Scheduling
A unified approach for the evaluation of quay crane scheduling models and algorithms
Computers and Operations Research
A modified genetic algorithm for quay crane scheduling operations
Expert Systems with Applications: An International Journal
Modeling and solving rich quay crane scheduling problems
Computers and Operations Research
Hybrid evolutionary computation methods for quay crane scheduling problems
Computers and Operations Research
Hi-index | 12.05 |
This paper addresses the quay crane scheduling problem (QCSP), which has been shown to be NP-complete. For this reason, a number of studies have proposed the use of genetic algorithm (GA) as the means to obtain the solution in reasonable time. This study extends the research in this area by utilizing the GA that is available in the latest version of Global Optimization Toolbox in MATLAB 7.13 to facilitate development. It aims to improve the efficiency of the GA search by (1) using an initial solution based on the S-LOAD rule developed by Sammarra, Cordeau, Laporte, and Monaco (2007), (2) using a new approach for defining the chromosomes (i.e., solution representation) to reduce the number of decision variables, and (3) using new procedures for calculating tighter lower and upper bounds for the decision variables. The effectiveness of the developed GA is tested using several benchmark instances proposed by Meisel and Bierwirth (2011). Compared to the current best-known solutions, experimental results show that the proposed GA is capable of finding the optimal or near-optimal solution in significantly shorter time for larger problems.