Resource allocation and scheduling problem based on genetic algorithm and ant colony optimization

Faced with the increasing growth of container throughput and more large ships in shorter time, a key factor of success is to generate the best resource allocation plan for the future. This paper discusses a heuristic GA-ACO method which combines Genetic Algorithm and Ant Colony Optimization for resource allocation and scheduling problem in container terminals. In the first phase GA uses character string to represent chromosome for allocation plans and finds the best allocation by self-learning. In the second phase, an improved ACO algorithm is introduced to optimize the scheduling jobs based on the allocation plan from GA. We examine the performance of tugboat allocation optimization in container terminals and obtain satisfactory results.