A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies th...

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Vydané v:	International journal of production research Ročník 50; číslo 2; s. 551 - 566
Hlavní autori:	Azadeh, A., Negahban, A., Moghaddam, M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Abingdon Taylor & Francis Group 15.01.2012 Taylor & Francis Taylor & Francis LLC
Predmet:	Algorithms Applied sciences Artificial intelligence artificial neural network Computer science; control theory; systems Computer simulation Connectionism. Neural networks Exact sciences and technology Inventory control, production control. Distribution job shop scheduling Job shops makespan minimisation meta-modelling Neural networks Operational research and scientific management Operational research. Management science Operations research Optimization algorithms Production scheduling Scheduling, sequencing Simulation Software stochastic processing time Studies artificial neural network simulation Job shop Processing time Makespan Uncertain system stochastic processing time Production factor Metamodel Execution time Probabilistic approach Computer simulation Task scheduling Routing Scheduling Neural network Workload Stochastic programming makespan minimisation meta-modelling Optimal solution Selection rule Hybrid computer Random event job shop scheduling Hybrid simulation
ISSN:	0020-7543, 1366-588X
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Popis
Shrnutí:	Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature.
Bibliografia:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14
ISSN:	0020-7543 1366-588X
DOI:	10.1080/00207543.2010.539281