An improved heuristic to minimize total flow time for scheduling in the m-machine no-wait flow shop

Laha, Dipak; Sapkal, Sagar U.

doi:10.1016/j.cie.2013.08.026

Cited by 26 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Problemi en büyük tamamlanma zamanı ve toplam akış zamanı olmak üzere çok amaçlı ele almıştır. Laha ve Sapkal [30] toplam akış zamanını en küçükleme amaçlı sezgisel bir algoritma, Tseng ve Lin [31] en büyük tamamlanma zamanını en küçükleme amaçlı çözüm için yerel arama ve genetik algoritmadan elde edilmiş hibrit bir algoritma, Chaundry ve Mahmood [32] genetik algoritma sunmuşlardır. Czogalla ve Fink [33] aynı problemin gecikme süresini en küçükleme amaçlı çözümü için yerel arama ve parçacık sürü optimizasyonu algoritmalarını birleştirdikleri hibrit bir algoritma sunmuşlardır.…”

Section: Literatür Taramasıunclassified

The effects of initial populations in the solution of flow shop scheduling problems by hybrid firefly and particle swarm optimization algorithms

Kaya¹,

Karaçizmeli²,

Aydilek³

et al. 2020

Pamukkale J Eng Sci

View full text Add to dashboard Cite

Klasik akış tipi çizelgeleme problemi, birbiri ardına sıralanmış makinelerin bulunduğu ve her iş için aynı makine sırasının takip edilmesi prensibine dayalıdır. İş ve makine sayılarının artmasıyla akış tipi çizelgeleme problemleri çok karmaşık hale dönüşmektedir. Bu karmaşık problemleri çözmek üzere birçok meta sezgisel yöntem kullanılmaktadır. Meta sezgisel yöntemlerle optimum çözüm aranırken başlangıç popülasyonlarının etkisi çok büyük önem arz etmektedir. Bu çalışmada hibrit ateşböceği parçacık sürü optimizasyonu algoritması kullanılarak literatürdeki akış tipi çizelgeleme problemlerinde, farklı başlangıç popülasyonlarının etkisinin gözlemlenmesi amaçlanmaktadır. Bu amaçla beş farklı başlangıç popülasyonu oluşturma yöntemi ele alınarak, karşılaştırma testleri yapılmıştır. Nawaz-Enscore-Ham algoritmasını içeren yöntemlerin ortalama göreli sapma değerlerinin daha iyi olduğu belirlenmiştir. Nawaz-Enscore-Ham algoritmasının farklı parçacık sayısı düzeyleri için başarısı test edilmiş ve sonuçlar sunulmuştur.The classical flow shop scheduling problem is based on the principle that the machines are sequenced sequentially and that the same machine sequence is followed for each job. The flow shop scheduling problems become very complex, with the increase in the number of jobs and machines. Many meta-heuristic methods are used to solve these complex problems. The effect of initial populations has great importance for searching optimal solutions by meta-heuristics methods. In this study, it is aimed to observe the effect of different initial populations in flow shop scheduling problems in the literature by using hybrid firefly particle swarm optimization algorithm. For this purpose, 5 different initial population generation methods were set and comparison tests were performed. The mean relative deviation values of the methods including the Nawaz-Enscore-Ham algorithm were determined to be better. The success of the Nawaz-Enscore-Ham algorithm for different particle count levels has been tested and the results are presented.Anahtar kelimeler: Akış tipi çizelgeleme, Meta sezgisel yöntemler, Hibrit ateşböceği parçacık sürü optimizasyonu, Başlangıç popülasyonu, NEH

show abstract

Section: Literatür Taramasıunclassified

The effects of initial populations in the solution of flow shop scheduling problems by hybrid firefly and particle swarm optimization algorithms

Kaya¹,

Karaçizmeli²,

Aydilek³

et al. 2020

Pamukkale J Eng Sci

View full text Add to dashboard Cite

show abstract

“…The problem with no-wait constraint in flow shop scheduling with minimization of total flow time as criterion has been studied by Van Deman and Baker (1974), Rajendran and Chaudhuri (1990), Chen et al (1996), Allahverdi (1998, 2004), Allahverdi and Aldowaisan (2000), Bertolissi (2000), Gao et al (2013), Akhshabi et al (2014) and Laha and Sapkal (2014). The problem of scheduling of jobs with the objective as makespan with no-wait constraint in flow shop has been studied by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Heuristics for no-wait flow shop scheduling problem

Nailwal¹,

Gupta²,

Jeet³

2016

10.5267/j.ijiec

View full text Add to dashboard Cite

No-wait flow shop scheduling refers to continuous flow of jobs through different machines. The job once started should have the continuous processing through the machines without wait. This situation occurs when there is a lack of an intermediate storage between the processing of jobs on two consecutive machines. The problem of no-wait with the objective of minimizing makespan in flow shop scheduling is NP-hard; therefore the heuristic algorithms are the key to solve the problem with optimal solution or to approach nearer to optimal solution in simple manner. The paper describes two heuristics, one constructive and an improvement heuristic algorithm obtained by modifying the constructive one for sequencing n-jobs through m-machines in a flow shop under no-wait constraint with the objective of minimizing makespan. The efficiency of the proposed heuristic algorithms is tested on 120 Taillard's benchmark problems found in the literature against the NEH under no-wait and the MNEH heuristic for no-wait flow shop problem. The improvement heuristic outperforms all heuristics on the Taillard's instances by improving the results of NEH by 27.85%, MNEH by 22.56% and that of the proposed constructive heuristic algorithm by 24.68%. To explain the computational process of the proposed algorithm, numerical illustrations are also given in the paper. Statistical tests of significance are done in order to draw the conclusions.

show abstract

“…For some of them the processing of a job cannot be interrupted once started. Some examples of such production systems are chemical, metal, food processing industries and printed circuit board manufacturing (Adiri and Pohoryles, 1982;Hall and Sriskandarajah, 1996;Nagano et al, 2012;Laha and Sapkal, 2014;Nagano et al, 2015). In addition, modern manufacturing environments where robots and industrial machines provide a highly coordinated process, can frequently be modeled as a no-wait scheduling problem (Bertolossi, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Allahverdi (2016) published a comprehensive survey of the no-wait in process scheduling problems. The no-wait flow shop scheduling problems with more than two machines belong to the class of NP-hard (Papadimitriou and Kanelakis, 1980;Rock, 1984;Van Der Veen et al, 1991;Fink and Voβ, 2003;Laha and Sapkal, 2014). Because of exponential computational effort requirements, heuristic methods have been proposed for the problem with m-machines in order to provide nearoptimal solution in small computational effort (Laha et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A High Quality Solution Constructive Heuristic for No-Wait Flow Shop Scheduling Problem

Nagano¹,

Miyata²

2016

Industrial Engineering and Management Systems

View full text Add to dashboard Cite

This paper deals with the no-wait flow shop scheduling problem in order to minimize the total time to complete the schedule or makespan. It is introduced a constructive heuristic which builds the production schedule from job partial sequences by using an appropriate mechanism of insertion. An extensive computational experiment has been performed to evaluate the performance of proposed heuristic. Experimental results have clearly shown that the presented heuristic provides better solutions than those from the best heuristics existing.

show abstract

An improved heuristic to minimize total flow time for scheduling in the m-machine no-wait flow shop

Cited by 26 publications

References 27 publications

The effects of initial populations in the solution of flow shop scheduling problems by hybrid firefly and particle swarm optimization algorithms

The effects of initial populations in the solution of flow shop scheduling problems by hybrid firefly and particle swarm optimization algorithms

Heuristics for no-wait flow shop scheduling problem

A High Quality Solution Constructive Heuristic for No-Wait Flow Shop Scheduling Problem

Contact Info

Product

Resources

About