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Showing 2 results for Rezaeian

Mir Saber Salehi Mir, Javad Rezaeian,
Volume 27, Issue 1 (IJIEPR 2016)

This paper considers identical parallel machines scheduling problem with past-sequence-dependent setup times, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of the processing times of the jobs already processed and its scheduled position on the corresponding machine. In addition, the setup time of a job on each machine is proportional to the actual processing time of the already processed jobs on the corresponding machine, i.e., the setup time of a job is past- sequence-dependent (p-s-d). The objective is to determine jointly the jobs assigned to each machine and the order of jobs such that the total completion time (called TC) is minimized. Since that the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, an efficient method based on ant colony optimization algorithm (ACO) is proposed to solve the given problem. The performance of the presented model and the proposed algorithm is verified by a number of numerical experiments. The related results show that ant colony optimization algorithm is effective and viable approache to generate optimal⁄near optimal solutions within a reasonable amount of computational time.

Javad Rezaeian, Masoud Shafipour,
Volume 28, Issue 3 (IJIEPR 2017)

This research deals with a hybrid flow shop scheduling problem with parallel batching, machine eligibility, unrelated parallel machine, and different release dates to minimize the sum of the total weighted earliness and tardiness (ET) penalties. In parallel batching situation, it is supposed that number of machine in some stages are able to perform a certain number of jobs simultaneously. Firstly, with respect to the proposed problem a mixed integer linear programming model is developed. Since the problem is NP-hard, for solving large size problems, a hybrid meta-heuristic algorithm which combines artificial immune system and simulated annealing is proposed. The performance of hybrid algorithm is tested by some numerical experiments and the results show its superiority to the other two algorithms.

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