Constrained binary artificial bee colony to minimize the makespan for single machine batch processing with non-identical job sizes

• We propose artificial bee colony (ABC) approach to minimize the makespan for a single batch-processing machine.
• ABC is capable of solving problems with 50 jobs or more within reasonable time.
• We notice throughout the experimentations that the solution quality and run time are highly sensitive to the choice of parameters of the ABC method.

This paper proposes an artificial bee colony approach to minimize the makespan for a single batch-processing machine. The single batch-processing problem is characterized by discontinuity in the objective function and having integer variables. Since the problem under study is NP-hard, an artificial bee colony approach is proposed. The penalty function method is used to convert the constrained problem to unconstrained problem, which is then solved by the ABC algorithm. A procedure to generate initial solutions is presented, which is based on filling partially filled batches first. The analysis in the article shows that the colony size, the value of the penalty parameter, the penalty function iteration, the ABC iteration, and maximum trials per food source all have a significant effect on the performance of the ABC algorithm; however, no pattern can be established.

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