مشخصات پژوهش

عنوان	A Genetic Programming Guided Search Designed for Production Scheduling When Batch Processing Machines are Available
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Production Management, Batch-Processing Machines, Flow Shop, Hybrid Genetic Algorithm, Makespan Measure.
چکیده	Flow shop problem which is known as a well-known combinatorial optimization problem, is very important scheduling decision in field of production management. This paper aims at minimizing the maximum completion time (makespan) in a flow shop production environment when batch-processing machines are available. A batch-processing machine is a type of machine that can process a batch of jobs (orders) as long as its capacity is not exceeded. The batch processing time is also the longest processing time of all of the jobs in that particular batch. In suggested model, the processing times and the job sizes are assumed to be non-identical. Such problem have many applications in different industrial environments such as the burn-in operation in the manufacture of semiconductors and the aging test operation in the manufacture of thin film transistor-liquid crystal displays. A mixed integer programming model is suggested to formulate the problem mathematically. Since the current problem is known to be computationally complex, hybrid genetic algorithms (HGAs) are devised to achieve optimal/near optimal solutions. In order to prove the high effectiveness of proposed algorithms, a set of random instances are generated, and then HGAs are compared with traditional algorithm. All the experimental results show that HGAs are more effective than traditional algorithm for the considered problem.
پژوهشگران	عیسی نخعی کمال آبادی (نفر سوم)، هادی مختاری (نفر دوم)، آسیه نوروزی (نفر اول)