زمان‌بندی خط مونتاژ جریان کارگاهی دومرحله‌ای با درنظرگرفتن اثر کهولت در زمان پردازش، محدودیت دسترسی به کارها و نگهداری و تعمیرات پیشگیرانه
زمان‌بندی خط مونتاژ جریان کارگاهی دومرحله‌ای با درنظرگرفتن اثر کهولت در زمان پردازش، محدودیت دسترسی به کارها و نگهداری و تعمیرات پیشگیرانه
عنوان فارسی :
زمان‌بندی خط مونتاژ جریان کارگاهی دومرحله‌ای با درنظرگرفتن اثر کهولت در زمان پردازش، محدودیت دسترسی به کارها و نگهداری و تعمیرات پیشگیرانه
عنوان انگلیسی :
Two Stage Assembly Flow Shop Scheduling Problem with Aging Effect, Limit Access to Work, and Preventive Maintenance
چکیده انگلیسی:
This paper studies the two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities. At the first stage, m-1 parallel machines process parts of each jobs, and at the second stage, related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs, their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions, in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time. However, the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is NP-hard, in order to solve the problem in medium and large scale two meta-heuristic algorithms, hybrid genetic algorithm (HGA) and hybrid particle swarm optimization (HPSO) are proposed. These algorithms are the hybrid version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by using Taguchi method, and are used to solve many numerical examples. Finally, the statistical analysis illustrates that the performance of HPSO is better than HGA.
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