An effective discrete artificial bee colony algorithm for flow shop scheduling problem with intermediate buffers

Zhang, Sujun; Gu, Xingsheng

doi:10.1007/s11771-015-2887-x

Cited by 19 publications

(8 citation statements)

References 34 publications

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“…Zhang ve Gu [19] maksimum tamamlanma süresini en aza indirmek amacıyla ara tamponlu akış tipi çizelgeleme problemi için ayrık YAKA önermişlerdir. Ekleme ve yer değiştirme operatörlerinin kombinasyonu, işçi arı safhasında komşu bireylerin üretilmesi için uygulamışlardır.…”

Section: Ruiz Ve Arkunclassified

Dağıtık Permütasyon Akış Tipi Çizelgeleme Problemlerinin Yapay Arı Koloni Algoritması İle Çözümü

Küpeli¹,

Sarucan²,

Küpeli³

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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Bu çalışmada amaç fonksiyonu maksimum tamamlanma süresinin minimizasyonu olan dağıtık permütasyon akış tipi çizelgeleme problemleri ele alınmıştır. Bu problemin klasik akış tipi çizelgeleme probleminden farkı, işlerin birden fazla fabrikaya dağıtılmasıdır. Problemin çözümünde doğadaki arıların besin arama davranışını temel alan yapay arı koloni algoritması kullanılmıştır. Algoritmanın başlangıç çözümleri üretmesinde NEH (Nawaz Enscore Ham) sezgiselinden yararlanılmıştır. Algoritmanın evrelerinde (işçi, gözlemci ve kâşif arı evresi) komşu çözümler için yer değiştirme metodu kullanılmıştır. Bu metotta rastgele seçilen iki işin yerleri değiştirilerek farklı iş sıraları elde edilmiştir. Algoritmanın problem üzerindeki başarısı literatürde iyi bilinin Taillard'ın küçük ve büyük boyutlu test problemleri kullanılarak ölçülmüştür. Algoritma, 14 adet sezgisel ile karşılaştırılmıştır ve en iyi sonuçları sunmuştur.

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Section: Ruiz Ve Arkunclassified

Dağıtık Permütasyon Akış Tipi Çizelgeleme Problemlerinin Yapay Arı Koloni Algoritması İle Çözümü

Küpeli¹,

Sarucan²,

Küpeli³

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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“…To obtain good performance, a meta-heuristic algorithm needs to find some strategies to balance the local search capability (exploitation) and global search capability (exploration) of the algorithm [24]. The advantage of meta-heuristic algorithms is that it can combine the both in an optimal way in theory.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

Gao

Wang

et al. 2020

Mathematics

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Job shop scheduling problem (JSSP) has high theoretical and practical significance in academia and manufacturing respectively. Therefore, scholars in many different fields have been attracted to study this problem, and many meta-heuristic algorithms have been proposed to solve this problem. As a meta-heuristic algorithm, particle swarm optimization (PSO) has been used to optimize many practical problems in industrial manufacturing. This paper proposes a hybrid PSO enhanced with nonlinear inertia weight and and Gaussian mutation (NGPSO) to solve JSSP. Nonlinear inertia weight improves local search capabilities of PSO, while Gaussian mutation strategy improves the global search ability of NGPSO, which is beneficial to the population to maintain diversity and reduce probability of the algorithm falling into the local optimal solution. The proposed NGPSO algorithm is implemented to solve 62 benchmark instances of JSSP, and the experimental results are compared with other algorithms. The results obtained by analyzing the experimental data show that the algorithm is better than other comparison algorithms in solving JSSP.

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“…The DABC algorithm uses the effectiveness of the insertion and swap operators to produce neighbourhood solutions at the employed bee phase. From many such articles [40][41][42] it is clearly understood that, swarm intelligence provides a better algorithmic framework inspired by the intelligent behaviour of the animals, birds and social insects.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Multi-Objective Artificial Bee Colony Algorithms and Chaotic-TOPSIS Method for Solving Flowshop Scheduling Problem and Decision Making

Panda

Dehuri

Jagadev

2020

IJCAI

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Retrieval of optimal solution(s) for a Permutation Flow-Shop Scheduling Problem (PFSSP) within a reasonable computational timeframe has been a challenge till yet. The problem includes optimization of various criteria like makespan, total flowtime, earliness, tardiness, etc for obtaining a set of Pareto solutions in the process of Multi-Objective Optimization (MOO). This paper remodels a Discrete Artificial Bee Colony Algorithm (DABC) from a single objective optimization method to a multiobjective optimization one to solve the PFSSP executed and explored through the alternative and combined use of two local search algorithms named as: Iterated Greedy Search Algorithm (IGRS) and Iterated Local Search Algorithm (ILS). The algorithm has been classified into three different scenarios raised with the analysis of time complexity measure of applied local search methods prioritized through the insertion and swap operation of neighborhood structures that intensifies the local optima in the search space. The results of the DABC algorithm are summarized with respect to Total Completion Time (TCT), Mean Weighted Tardiness (MWT), and Mean Weighted Earliness (MWE). Based on the time complexity measure of the obtained results a Multi-Objective Artificial Bee Colony Algorithm (MOABC) has been proposed by adopting the simplest local search method of all in order to reflect the enhanced version of previously remodeled DABC algorithm. Finally, we propose a Chaotic based Technique for Order of Preference by Similarity to Ideal Solution (Chaotic-TOPSIS) using a suitable chaotic map for criteria adaptation in order to enhance the decision accuracy in the multi-Criteria Decision Making (MCDM) domain. Povzetek: Članek se ukvarja z NP problemom večkriterijske optimizacije izdelave urnika z imenom Permutation Flow-Shop Scheduling Problem (PFSSP). Uvede Multi-Objective Artificial Bee Colony Algorithm (MOABC), tj. več-kriterijski algoritem z umetno čebeljo kolonijo in pokaže izboljšane rezultate.

show abstract

An effective discrete artificial bee colony algorithm for flow shop scheduling problem with intermediate buffers

Cited by 19 publications

References 34 publications

Dağıtık Permütasyon Akış Tipi Çizelgeleme Problemlerinin Yapay Arı Koloni Algoritması İle Çözümü

Dağıtık Permütasyon Akış Tipi Çizelgeleme Problemlerinin Yapay Arı Koloni Algoritması İle Çözümü

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

Multi-Objective Artificial Bee Colony Algorithms and Chaotic-TOPSIS Method for Solving Flowshop Scheduling Problem and Decision Making

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