Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
A tabu search heuristic for the quay crane scheduling problem
Journal of Scheduling
Computers and Operations Research
Integrating simulation and optimization to schedule loading operations in container terminals
Computers and Operations Research
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
A heuristic for the quay crane scheduling problem based on contiguous bay crane operations
Computers and Operations Research
An efficient genetic algorithm for solving the quay crane scheduling problem
Expert Systems with Applications: An International Journal
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Quay crane scheduling is one of the most important operations in seaport terminals. The effectiveness of this operation can directly influence the overall performance as well as the competitive advantages of the terminal. This paper develops a new priority-based schedule construction procedure to generate quay crane schedules. From this procedure, two new hybrid evolutionary computation methods based on genetic algorithm (GA) and genetic programming (GP) are developed. The key difference between the two methods is their representations which decide how priorities of tasks are determined. While GA employs a permutation representation to decide the priorities of tasks, GP represents its individuals as a priority function which is used to calculate the priorities of tasks. A local search heuristic is also proposed to improve the quality of solutions obtained by GA and GP. The proposed hybrid evolutionary computation methods are tested on a large set of benchmark instances and the computational results show that they are competitive and efficient as compared to the existing methods. Many new best known solutions for the benchmark instances are discovered by using these methods. In addition, the proposed methods also show their flexibility when applied to generate robust solutions for quay crane scheduling problems under uncertainty. The results show that the obtained robust solutions are better than those obtained from the deterministic inputs.