Hybrid genetic algorithms with dispatching rules for unrelated parallel machine scheduling with setup time and production availability

Joo, Cheol Min; Kim, Byung Soo

doi:10.1016/j.cie.2015.02.029

Cited by 73 publications

(32 citation statements)

References 27 publications

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“…Literatürde NP-zor problemlere önerilen matematiksel modellere her zaman süre kısıtı konulmaktadır. Bu süre genel olarak makalelerde 3600 sn olmaktadır [16,17]. Modelin eniyiye daha yakın sonuç vermesi istendiğinden süre kısıtı 10000 sn olarak belirlenmiştir.…”

Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

İki̇ Paralel Enjeksi̇yon Maki̇nasinin Kreyn Kisiti Altinda Çi̇zelgelenmesi̇

Bektur¹,

Saraç²

2016

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Bu çalışmada iki paralel enjeksiyon makinasının kreyn kısıtı altında çizelgelenmesi problemi ele alınmıştır. Enjeksiyon makinalarıyla üretimin hazırlık aşamasında, üretilecek ürüne ait kalıbın makinaya bağlanması gerekmektedir. Kalıplar ilgili makinalara kreyn ile taşınmaktadır. Makinalar tek bir kreyni paylaştığı için hazırlık zamanları çakışmamalıdır. Ayrıca bir işin hazırlık zamanı kendisinden önceki işe bağlı olarak farklılık göstermektedir ve her iş her makinada üretilememektedir. Amaç, son işin tamamlanma zamanının en küçüklenmesidir. Bu nedenle problem P 2 , S 1 | ST sd , M j | C enb sınıfında yer almaktadır. Ele alınan problem için bir matematiksel model önerilmiştir. Ayrıca problemin NP-zor problemler sınıfında yer alması nedeniyle büyük boyutlu problemlerin çözümü için bir genetik algoritma geliştirilmiştir. Önerilen algoritmanın başarısı rassal türetilen test problemleri kullanılarak matematiksel model ve elde edilen alt sınırlar üzerinden gösterilmiştir.

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Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

İki̇ Paralel Enjeksi̇yon Maki̇nasinin Kreyn Kisiti Altinda Çi̇zelgelenmesi̇

Bektur¹,

Saraç²

2016

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…A hybrid genetic algorithms with three dispatching rules for large-sized problems was used by Joo and Kim (2015). Hassanpour et al (2015) applied simulated annealing (SA), genetic algorithm (GA) and a bottleneck based heuristic (BB) algorithms for solving the problem of minimizing makespan of the jobs in no-wait reentrant flow shop production environment.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid algorithm for unrelated parallel machines scheduling

Nikabadi¹,

Naderi²

2016

10.5267/j.ijiec

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In this paper, a new hybrid algorithm based on multi-objective genetic algorithm (MOGA) using simulated annealing (SA) is proposed for scheduling unrelated parallel machines with sequencedependent setup times, varying due dates, ready times and precedence relations among jobs. Our objective is to minimize makespan (Maximum completion time of all machines), number of tardy jobs, total tardiness and total earliness at the same time which can be more advantageous in real environment than considering each of objectives separately. For obtaining an optimal solution, hybrid algorithm based on MOGA and SA has been proposed in order to gain both good global and local search abilities. Simulation results and four well-known multi-objective performance metrics, indicate that the proposed hybrid algorithm outperforms the genetic algorithm (GA) and SA in terms of each objective and significantly in minimizing the total cost of the weighted function.

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“…. , } is a permutation which requires values of all genes to be different; then the one-point crossover and swap mutation are adopted [1]. Subset { 1 , .…”

Section: Comparison Algorithm and Parametermentioning

confidence: 99%

“…However, in reality, especially in the assemble lines with multivariety production, one machine is only eligible to process specified orders, which is called the machine eligibility constraint. Recently, some scholars have considered this constraint in the unrelated parallel machine scheduling models (see [1][2][3]). Moreover, in the assemble line with machine eligibility constraints, processing technologies used in machines are different; thus production costs of one order on different machines are often different and do not depend on the processing times.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

Wang

2018

Mathematical Problems in Engineering

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This paper studies the order acceptance and scheduling problem on unrelated parallel machines with machine eligibility constraints. Two objectives are considered to maximize total net profit and minimize the makespan, and the mathematical model of this problem is formulated as multiobjective mixed integer linear programming. Some properties with respect to the objectives are analysed, and then a classic list scheduling (LS) rule named the first available machine rule is extended, and three new LS rules are presented, which focus on the maximization of the net profit, the minimization of the makespan, and the trade-off between the two objectives, respectively. Furthermore, a list-scheduling-based multiobjective parthenogenetic algorithm (LS-MPGA) is presented with parthenogenetic operators and Pareto-ranking and selection method. Computational experiments on randomly generated instances are carried out to assess the effectiveness and efficiency of the four LS rules under the framework of LS-MPGA and discuss their application environments. Results demonstrate that the performance of the LS-MPGA developed for trade-off is superior to the other three algorithms.

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Hybrid genetic algorithms with dispatching rules for unrelated parallel machine scheduling with setup time and production availability

Cited by 73 publications

References 27 publications

İki̇ Paralel Enjeksi̇yon Maki̇nasinin Kreyn Kisiti Altinda Çi̇zelgelenmesi̇

İki̇ Paralel Enjeksi̇yon Maki̇nasinin Kreyn Kisiti Altinda Çi̇zelgelenmesi̇

A hybrid algorithm for unrelated parallel machines scheduling

Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

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