An improved ant colony optimization for scheduling identical parallel batching machines with arbitrary job sizes

Authors:
Bayi Cheng;Qi Wang;Shanlin Yang;Xiaoxuan Hu
Affiliations:
School of Management, Hefei University of Technology, Hefei 230009, PR China and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei University of ...;School of Management, Hefei University of Technology, Hefei 230009, PR China;School of Management, Hefei University of Technology, Hefei 230009, PR China and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei University of ...;School of Management, Hefei University of Technology, Hefei 230009, PR China and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei University of ...
Venue:
Applied Soft Computing
Year:
2013

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Abstract

In this paper we consider the problem of scheduling parallel batching machines with jobs of arbitrary sizes. The machines have identical capacity of size and processing velocity. The jobs are processed in batches given that the total size of jobs in a batch cannot exceed the machine capacity. Once a batch starts processing, no interruption is allowed until all the jobs are completed. First we present a mixed integer programming model of the problem. We show the computational complexity of the problem and optimality properties. Then we propose a novel ant colony optimization method where the Metropolis Criterion is used to select the paths of ants to overcome the immature convergence. Finally, we generate different scales of instances to test the performance. The computational results show the effectiveness of the algorithm, especially for large-scale instances.