N. Shahsavari pour; M.H. Abolhasani Ashkezari; H. Sheikhi; H. Mohammadi Andargoli; H. Abolhasani Ashkezari
Volume 3, Issue 4 , December 2014, , Pages 1-12
Abstract
Various procedures, methods, constraints and objectives are studied in a flow shop problem during the past decades. In order to adapt the problem to the reality form, its parameters are considered as a fuzzy model. In this problem, we consider the processing time as the trapezoidal fuzzy numbers. The ...
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Various procedures, methods, constraints and objectives are studied in a flow shop problem during the past decades. In order to adapt the problem to the reality form, its parameters are considered as a fuzzy model. In this problem, we consider the processing time as the trapezoidal fuzzy numbers. The purpose of this problem is to find an optimum sequence in a way that the makespan or the completing time of jobs to be minimized. In order to solve this problem, in this paper, the Random-Elitist Genetic Algorithm (REGA) is presented in this regard. Observing the performance and the efficiency of this algorithm, we code it by the VBA and compare with the other results. We first test the performance of different crossover operators for our algorithm. Next, using a specific example, we examine the performance of our algorithm. The results indicated that due to very good searching; this algorithm has the good performance in finding the optimal solution and reaching the optimum solution in a very short time.
N. Shahsavari Pour; M.H. Abolhasani Ashkezari; H. Mohammadi Andargoli
Volume 2, Issue 1 , February 2013, , Pages 20-29
Abstract
Considering flow shop scheduling problem with more objectives, will help to make it more practical. For this purpose, we have intended both the makespan and total due date cost simultaneously. Total due date cost is included the sum of earliness and tardiness cost. In order to solve this problem, a genetic ...
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Considering flow shop scheduling problem with more objectives, will help to make it more practical. For this purpose, we have intended both the makespan and total due date cost simultaneously. Total due date cost is included the sum of earliness and tardiness cost. In order to solve this problem, a genetic algorithm is developed. In this GA algorithm, to further explore in solution space a Tabu Search algorithm is used. Also in selecting the new population, is used the concept of elitism to increase the chance of choosing the best sequence. To evaluate the performance of this algorithm and performing the experiments, it is coded in VBA. Experiments results and comparison with GA is indicated the high potential of this algorithm in solving the multi-objective problems.