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Top1. Introduction
To maintain competitiveness of the manufacturing industries in the market, their products should be delivered sufficiently with top quality and in minimum time to the customers. In order to do so it is significant that optimize production line will have a minimum manufacturing time. Minimum manufacturing time can be attained through an optimal production time that suits well with the production environment constraints. In flow shop scheduling (FSS), when no intermediate storage is provided for the jobs to wait between stages then the problem of blocking arises. As a result, at the completion of any task, if downstream machine is busy, a job must stay at the upstream machine, occupying it and it block the next job until downstream processor allows and clears flow to resume. Clearly, this production protocol is connected to the no-wait flow shop where waiting time, rather than the waiting space, is necessary to be zero. Of course, since there is never any blocking in a no-wait schedule, the optimal schedule with blocking is either the optimal no-wait schedule or something better. This is true for any number of machines and for any objective. FSS is an extensively used significant process in computer science, management, production, manufacturing, and so on. Appropriate scheduling can diminish material handling costs as well as time. Thus, finding good schedules for given sets of jobs can help factory owner efficiently manage job flows and provides solutions for job sequencing. A flow shop such as an automobile line which is operated by a manufacturing unit is optimized for highest possible production speed and quality.
In the past decades, few research have been carried out on the flow shop problem with blocking. To our knowledge, a very few genetic algorithms were previously applied to solve this type of problem and they are not providing efficient solutions also they are highly complex. The main contribution of the paper is the development of an efficient new genetic algorithm for solving the blocking flow shop scheduling problems to minimization the total completion time.
The rest of this article is organized as follows. Section 2 describes a brief overview and related literature. The problem definition and mathematical modeling is given in section 3. In section 4, notations are described. The description of the proposed new genetic algorithm is specified in section 5. Results and discussion are provided in section 6. Finally, the paper ends with some conclusion and future work in section 7.