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Flexible job-shop scheduling problem (FJSP) is a well-known difficult combinatorial optimization problem. Many algorithms have been proposed for solving the FJSP in the last few decades, including algorithms based on evolutionary techniques. However, there is room for improvement. In this paper, we present a genetic algorithm (GA) for FJSP. The algorithm encodes the individual with parallel machine...
In this paper the problem of scheduling of n jobs on m non-identical parallel machines is considered. All jobs can be processed on all machines and the processing time and cost of each job depend on the machine on which the job is performed. Jobs cannot be split or divided and all jobs are available at time zero. The goal is to minimize cost which is composed of two parts: earliness-tardiness cost...
This research considers scheduling problems with jobs which can be divided into sub-jobs and do not required to be processed immediately following one another. Heuristic algorithms considering how to divide jobs are proposed in an attempt to find near-optimal solutions within reasonable run time. The algorithms contain two stages which are executed recursively. Stage 1 of the algorithm determines...
Scheduling problems are very important for many (research) fields. However only for few instances there are polynomial time optimization algorithms, because the vast majority of scheduling problem instances is NP-hard. In such cases heuristic and/or stochastic algorithm are used which tend toward but do not guarantee the finding of optimal solution. The aim of our paper is to investigate the performance...
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