Minimizing total tardiness for the machine scheduling and worker assignment problems in identical parallel machines using genetic algorithms

Chaudhry, Imran Ali; Drake, Paul R.

doi:10.1007/s00170-008-1617-z

Cited by 66 publications

(26 citation statements)

References 39 publications

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“…Chaari et al adopted a genetic algorithm (GA) for a robust HFSSP under demand uncertainty [7]. Chaudhry and Drake applied GA for machine scheduling and worker assignment to minimize total tardiness [9]. Chan et al presented a GA-based job shop scheduler for a flexible, multi-product, parallel-machine sheet-metal job shop [8].…”

Section: Hfssp With Parallel Machinesmentioning

confidence: 99%

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

Lin

Chen

2015

Simulation Modelling Practice and Theory

101

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Section: Hfssp With Parallel Machinesmentioning

confidence: 99%

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

Lin

Chen

2015

Simulation Modelling Practice and Theory

101

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“…Chaari et al adopted GA for demand uncertainty of robust HFSSP [3]. Chaudhry and Drake used GA for machine scheduling and worker assignment to minimize total tardiness [5]. Chan et al presented a GA-based job-shop scheduler for a flexible multi-product, parallel machine sheet metal job shop [4].…”

Section: Simulation Optimization and Optimal Computingmentioning

confidence: 99%

Simulation optimization with GA and OCBA for semiconductor back-end assembly scheduling

Lin

Chen

2015

2015 International Conference on Industrial Engineering and Operations Management (IEOM)

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This paper presents a simulation optimization with genetic algorithm and optimal computing budget allocation for semiconductor back-end assembly scheduling problem to achieve minimal average order flow time. In particular, this research explores characteristics of hybrid flow shop scheduling problem in complicated identical and unrelated parallel machines, orders with specific product and demand scheduled with different release time, order split for parallel and merges for batch processing during manufacturing under product-machine dedication with stochastic processing times and sequencedependent setup times. As this is a real-life stochastic event system and discrete simulation is usually the only resort for performance. Coupling genetic algorithms in simulation optimization as the solution space is large. Optimal computing budget allocation was used to reduce simulation budget usage while stochastic situation. Numerical result shows it is effective to improve performance better than practical heuristics and efficient to generate few replications which conquer the barriers utilize the advantages of simulation-based approach. This work can be as solution architecture for providing superior scheduling decision on allocation of orders and subsequent jobs to machines in a complex hybrid flow shop.

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“…However, different skill levels were considered. Chaudhry and Drake (2009) also developed a search algorithm based on genetic algorithm for identical parallel machines with worker assignment. They too did not consider sequence dependent setup time, dynamic machine availability times and dynamic job release times.…”

Section: Introductionmentioning

confidence: 99%

Non-permutation flowshop scheduling with dual resources

Mehravaran

Logendran

2013

Expert Systems with Applications

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Minimizing total tardiness for the machine scheduling and worker assignment problems in identical parallel machines using genetic algorithms

Cited by 66 publications

References 39 publications

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

Simulation optimization with GA and OCBA for semiconductor back-end assembly scheduling

Non-permutation flowshop scheduling with dual resources

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