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An Efficient Bi-Objective Genetic Algorithm for the Single Batch- Processing Machine Scheduling Problem with Sequence-DependentFamily Setup Time and Non-Identical Job Sizes

عنوان مقاله: An Efficient Bi-Objective Genetic Algorithm for the Single Batch- Processing Machine Scheduling Problem with Sequence-DependentFamily Setup Time and Non-Identical Job Sizes
شناسه ملی مقاله: JR_JOIE-11-2_007
منتشر شده در شماره 2 دوره 11 فصل Summer and Autumn در سال 1397
مشخصات نویسندگان مقاله:

Javad Rezaeian - Department of Industrial Engineering, Mazandaran University of Science and Technology
Yaser Zarook - Department of Industrial Engineering, Mazandaran University of Science and Technology

خلاصه مقاله:
This paper considers the problem of minimizing make-span and maximum tardiness simultaneously for scheduling jobs under non-identical job sizes, dynamic job arrivals, incompatible job families, and sequence-dependent family setup time on the single batch- processor, where split size of jobs is allowed between batches. At first, a new Mixed Integer Linear Programming (MILP) model is proposed for this problem; then, it is solved by constraint method. Since this problem is NP-hard, a bi-objective genetic algorithm (BOGA) is offered for real-sized problems. The efficiency of the proposed BOGA is evaluated to be compared with many test problems by -constraint method based on performance measures. The results show that the proposed BOGA is found to be more efficient and faster than the -constraint method in generating Pareto fronts in most cases.

کلمات کلیدی:
Batch Processing; Incompatible Job Family; Release Date; Split Job Size; Family Setup Time

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/791068/