Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Optimal Routing Algorithm for a Transfer Crane in Port Container Terminals
Transportation Science
Comparison of three automated stacking alternatives by means of simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Real Time Scheduling by Coordination for Optimizing Operations of Equipments in a Container Terminal
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Sequencing Two Cooperating Automated Stacking Cranes in a Container Terminal
Transportation Science
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We address the problem of scheduling twin automated stacking cranes (ASCs) used in automated container terminals. By extending the previous works, we show that it is important to make explicit the hidden jobs needed to prepare for the main requested jobs. Since the preparatory jobs can be done by any of the two ASCs, appropriate assignment of these jobs can help to promote cooperation and avoid interference between the two ASCs. The proposed genetic algorithm (GA) performs search within the framework of iterative rescheduling to cope with the uncertainty of ASC operation. To boost the search performance under tight real-time constraint of iterative rescheduling, our GA uses some of the solutions of the previous iteration to initialize the population of the current iteration. It has also been shown that our GA performs more robustly than other algorithm such as simulated annealing in an uncertain environment.