scholarly journals Scheduling of Flexible Manufacturing System using Genetic Algorithm (Multiobjective): A Review

2014 ◽  
Vol 86 (19) ◽  
pp. 9-15
Author(s):  
Navnikaa Rajan ◽  
Srishti Jaiswal ◽  
Tanya Kalsi ◽  
Vijai Singh
2011 ◽  
Vol 121-126 ◽  
pp. 1630-1635
Author(s):  
Nai Fei Ren ◽  
Yan Zhao ◽  
Jun Zhang

Aiming at solving scheduling problem of flexible manufacturing system, this paper puts forward a FMS scheduling problem where single AGV with two buffers system is to be considered. Such an AGV with two buffers system was replaced with double-buffer AGV system in the next content. To solve FMS scheduling problem with double-buffer AGV system, a mathematical model which integrated double-buffer AGV and jobs was designed. And an improved genetic algorithm is proposed to sequence processing of jobs and the moving path of double-buffer AGV. The experiments made in simulation FMS production line laboratory realized scheduling integration of jobs and AGV, meanwhile, experiments gained processing sequence of jobs on each machine and moving path of AGV. Contrasting results of double-buffer and no-buffer AGV system verified double-buffer AGV system has higher feasibility and effectiveness.


Author(s):  
K. MALLIKARJUNA ◽  
V. VEERANNA ◽  
K.HEMACHANDRA REDDY

Single row layout is one of the most usually used layout patterns in industries, particularly in flexible manufacturing systems. Here actual sequencing of machine and arrangement of parts, no doubt, have a great influence on the throughput of the flexible manufacturing system i.e., (F.M.S). This paper discusses the single row layout design in flexible manufacturing system (F.M.S). This paper furnishes the design, development and testing of simulated annealing technique and genetic algorithm to solve the single row layout problem by considering multi-objective i.e., minimizing the make span of jobs on all machines and minimizing the total transportation cost. The various line layout problems are tested for performance of objective function with respect to computational time and number of iterations involved in GA and SA. A necessary code is generated in C++ and the code is run by the IDE tool in which C++ compiler used as plug in. This tool has Eclipse based features which affords the competency to figure, correct, steer, and sort out the tasks that use C++ as a programming language using Intel core i3-380M processor. The results of the different optimization algorithms (Genetic Algorithm and simulated annealing method) are compared and finally, we observed that GA provide optimum results than SA.


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