کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5079250 1477528 2016 39 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An effective discrete differential evolution algorithm for scheduling uniform parallel batch processing machines with non-identical capacities and arbitrary job sizes
ترجمه فارسی عنوان
یک الگوریتم تکاملی دیفرانسیل تکاملی مؤثر برای برنامه ریزی ماشین آلات پردازش یکپارچه موازی با ظرفیت های غیر یکسان و اندازه شغل دلخواه
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی صنعتی و تولید
چکیده انگلیسی
Batch processing machines (BPMs) simultaneously process multiple jobs in a batch, which are commonly used in many industrial systems. This paper studies the scheduling problem of uniform parallel batch processing machines with arbitrary job sizes. These batch processing machines have non-identical capacities and different speeds. The objective is to minimize the makespan (or maximize the machine utilization). We formulate this problem as a mixed integer programming model. Since the problem is strongly NP-hard, an effective differential evolution-based hybrid algorithm is proposed for solving large-scale problems. Firstly, in this algorithm, individuals in the population are represented as discrete job sequences, and novel mutation and crossover operators are designed based on this representation. Next, a heuristic is developed to form batches and schedule the resulting batches on the uniform parallel machines. Then, the performance of the proposed algorithm is evaluated by comparing its results to a commercial solver (CPLEX), a random keys genetic algorithm (RKGA) and a particle swarm optimization (PSO) algorithm. Experimental results demonstrate the superiority of the proposed algorithm in terms of solution quality and robustness, especially for large-scale instances.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Production Economics - Volume 179, September 2016, Pages 1-11
نویسندگان
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