The double row layout problem (DRLP) is an NP-hard and has many applications in the industry. The problem concerns on arranging the position of machines on the two rows so that the material handling cost is minimized. Although several mathematical programming models and local heuristics have been previously proposed, there is still a requirement to develop an approach that can solve the problem efficiently. Here, a genetic algorithm is proposed, which is aimed to solve the DRLP in a reasonable and applicable time. The performances of the proposed method, both its obtained objective values and computational time, are evaluated by comparing it with the existing mathematical programming model. The results demonstrate that the proposed GA can find relatively high-quality solutions in much shorter time than the mathematical programming model, especially in the problem with large number of machines.
This study develops an improved hybrid genetic algorithm-simulated annealing (IGASA) algorithm to solve the reentrant flow-shop scheduling problem with permutation characteristics. The reentrant permutation flow-shop (RPFS) allows the jobs to visit certain machines more than once and has been proven to be an -hard problem. The proposed improved hybrid algorithm integrates the simulated annealing (SA) and genetic algorithm (GA) to obtain the near-optimal solutions by considering three objectives: minimizing the makespan, the average completion time, and total tardiness. The multioperator mechanism is proposed for the crossover and mutation operations to improve and maintain the diversity of individuals throughout the generation. The effectiveness and robustness of the proposed method are examined in the data sets of various-sized instances with different degrees of complexity. The results highlight that the proposed hybrid algorithm is a promising alternative in solving the RPFS scheduling problem.
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