Abstract
ABSTRACTIn this article, the problem of scheduling a set of jobs on parallel batch machines with arbitrary capacities is considered. The jobs have identical processing time, non-identical sizes and unequal weights. After being processed, the jobs are delivered to the customers by the vehicles. The objective is to minimize the total weighted delivery time of the jobs. Two heuristic algorithms and an algorithm based on ant colony optimization (ACO) are presented to address the problem. Considering the relationship between the jobs and the batches, a three-level candidate list is designed for building the solutions effectively. Furthermore, based on the change of the normalized weight of the batch, the heuristic information is designed to control the search direction of the ants and improve the solution quality. The experimental results show that the performance of the proposed ACO algorithm is superior to the other algorithms compared.
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