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Showing 2 results for Differential Evolution Algorithm

, , ,
Volume 20, Issue 1 (5-2009)

Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrated via real data of a purchase system in order to simulate the system’s behavior.
Hadi Mokhtari , Ashkan Mozdgir,
Volume 26, Issue 2 (7-2015)

Assembly lines are special kinds of production systems which are of great importance in the industrial production of high quantity commodities. In many practical manufacturing systems, configuration of assembly lines is fixed and designing a new line may be incurred huge amount of costs and thereby it is not desirable for practitioners. When some changes related to market demand occur, it is worthwhile to re-balance an existing line rather than balancing a new one. Hence, in this paper we suggest a re-balancing model of an existing assembly line in which a new demand related cycle time (CT) is embedded to the traditional assembly line balancing problem (ALBP) as a new parameter. It does not focus on balancing a new line instead it considers a more realistic problem which is re-balancing an existing line. The objective is to re-schedule the tasks in order to reduce the current CT to the new required one such that two criteria are optimized: (i) minimization of the incurred costs and (ii) minimization of non-smoothing of reconfigured line. To solve the considered problem, an effective differential evolution algorithm is developed. Furthermore, to enhance the performance of algorithm, its parameters are optimized by the use of Taguchi method which is a conventional statistical technique for parameter design. The obtained results from computational experiments on benchmark instances show the effectiveness of suggested algorithm against other methods.


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