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Optimization of job scheduling in a machine shop using genetic algorithm

Adhikari, A. and Biswas, C.K. and Adhikari, N. (2002) Optimization of job scheduling in a machine shop using genetic algorithm. Journal of the Institution of Engineers (India), Part PR: Production Engineering Division, 83 (SEP.). pp. 15-19.

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Abstract

As job scheduling involves allocation of jobs to machines to reduce the idle time of machines, the aim of this work emphasises on minimizing the cycle time by using genetic algorithm (GA). Each job has a pre-determined process sequence and the sequences are decided according to metal cutting theory and technological constraints. A modified version of GA known as string GA has been used to get the near optimal cycle time for permutation analysis. An experiment has been carried out with 2 iv 5 resolution to find the significance of five parameters of GA, namely population size, maximum generation, probability of crossing, probability of mutation and crossover operators. Computer runs were carried out with these parameters at various levels and the results indicated that, only probability of mutation, the combined effect of maximum generation and probability of crossing are significant at 10. It is suggested that the minimum values of these parameters be used for scheduling problems.

Item Type:Article
Subjects:T Technology > TS Manufactures
Academic Subject One:scheduling
Academic Subject Two:genetic algorithm
Academic Subject Three:job shop
Departments / MOR / COE:Departments > Mechanical Engineering
ID Code:10073
Deposited By: Dr Chandan Kumar Biswas
Deposited On:25 Oct 2013 01:56
Last Modified:25 Oct 2013 01:56

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