An Intelligent Decision Support System for Part Launching in a Flexible Manufacturing System

Shukla, Chetan; Chen, F.F.

doi:10.1007/s001700170052

Cited by 7 publications

(1 citation statement)

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“…ElMekkawy and ElMaraghy [27] utilized flexible routing in order to avoid system deadlock caused by machine breakdowns and downtimes. Shukla and Chen [28] proposed a decision support system framework to assist in the control of an FMS through intelligent part launching. The proposed system makes use of a heuristic based on the pull concept and a neural network model.…”

Section: Literaturementioning

confidence: 99%

Adaptation of Memetic Algorithm with Population Management for the Improvement of the Performances of Flexible Manufacturing Systems

Houari¹,

Houbad²,

Souier³

et al. 2017

Open Autom. Control Syst. J.

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Objective:Solving optimization problems is a key point in the constant improving of productivity in the industries. Introduction:When traditional methods fail, it is natural to look towards some approximate resolution methods. Memetic algorithm with population management is a Metaheuristic that has been conceived in the last years, and proved its power in the resolution of the difficult optimization problems. Material and Methods:In this paper, our interest is focused on the adaptation of an optimization algorithm called memetic Algorithm with Population Management based on the strategy of management of population to avoid slow or premature convergence and to carry out the excellent executions to solve real time alternative routings selection problem in a Flexible Manufacturing System (FMS), that consists of seven machining centres, a loading and an unloading area, and six different part types which have alternative routings. Results:Simulation results based on two performance indicators which are the production rate, cycle time, work in process and machines utilization rates show that the proposed algorithm performs the best compared to the genetic algorithm. Conclusion:Then we will make a comparison between this algorithm and the genetic algorithm previously used to solve the same problem to get an idea on the most efficient methods for this problem and choose the most effective.

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Section: Literaturementioning

confidence: 99%