Integrated scheduling on parallel batch processing machines with non-identical capacities

ABSTRACT

In 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.

KEYWORDS:

• Batch processing machines
• non-identical machine capacities
• total weighted delivery time
• ant colony optimization

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant number 71601001], [Grant number 71671168]; the foundation of Ministry of Education of the People's Republic of China [Grant number 15YJC630041]; the Natural Science Foundation of Anhui Province [Grant number 1608085MG154].