Scheduling job shop associated with multiple routings with genetic and ant colony heuristics

Girish, B S; Jawahar, N.

doi:10.1080/00207540701824845

Cited by 31 publications

(10 citation statements)

References 20 publications

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“…In Table 4 we compare our results on the BRData set with these obtained by Ho et al (2007) using CDR-PopGen and LEGA; and these obtained by Girish and Jawahar (2008) who solved them using Ant Colony Optimization (JSSANT) and Genetic algorithms (JSSGA). Table 4 shows that the proposed hGA either outperforms other algorithms or obtain the same minimum makespan in all cases.…”

Section: Computational Resultsmentioning

confidence: 94%

“…Zribi et al (2007) used a hierarchical GA approach to solve the machine assignment and scheduling of FJSP. Girish and Jawahar (2008) proposed two concurrent metaheuristic approaches, genetic algorithm (JSSGA) and ant colony (JSSANT) to solve job shops with multiple routings. Xu et al (2009), Xing et al (2010 and Ling et al (2010) presented an ant colony optimization algorithm for the FJSP.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem

Al-Hinai

ElMekkawy

2010

Flex Serv Manuf J

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The work presented in this paper proposes hybridized genetic algorithm architecture for the Flexible Job Shop Scheduling Problem (FJSP). The efficiency of the genetic algorithm is enhanced by integrating it with an initial population generation algorithm and a local search method. The usefulness of the proposed methodology is illustrated with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. Results highlight the ability of the proposed algorithm to first obtain optimal or near-optimal solutions, and second to outperform or produce comparable results with these obtained by other best-known approaches in literature.

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Section: Computational Resultsmentioning

confidence: 94%

Section: Literature Reviewmentioning

confidence: 99%

An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem

Al-Hinai

ElMekkawy

2010

Flex Serv Manuf J

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“…From then on, the ACO algorithm received increasing attention and enjoyed great success in solving combinatorial optimisation problems. For instance, telecommunication routing (Schoonderwoerd et al 1997), product level MMAL balancing problem (McMullen 2001b), quadratic assignment problem (Shyu et al 2006), vehicle routing problem (Gagne et al 2006), industry layout problem (Hani et al 2007), dynamic manufacturing scheduling problem (Wang and Lee 2008), DNA sequencing problem (Blum et al 2008), multidimensional Knapsack problem (Kong et al 2008), flow shop scheduling problem (Yagmahan and Yenisey 2008), assembly line workload balancing problem (Vilarinho andSimaria 2006, Sabuncuoglu et al 2009), project management problem (Abdallah et al 2009), distributed supply chain management problem (Silva et al 2009), multiprocessor task scheduling problem (Ying and lin 2006), line balancing and sequencing problem (Agrawal and Tiwari 2008), agile manufacturing system scheduling problem , dynamic job shop scheduling problems (Zhou et al 2009), job shop scheduling problem (Chang et al 2008, Huang and Liao 2008, Girish and Jawahar 2009, and others. It has been proved that the ACO based algorithms might get solutions with better quality than simple heuristics (McMullen 2001b), which is important for practical applications.…”

Section: Aco Algorithmmentioning

confidence: 98%

Ant colony optimisation with elitist ant for sequencing problem in a mixed model assembly line

Zhu

Zhang

2011

International Journal of Production Research

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Optimised sequencing in the Mixed Model Assembly Line (MMAL) is a major factor to effectively balance the rate at which raw materials are used for production. In this paper we present an Ant Colony Optimisation with Elitist Ant (ACOEA) algorithm on the basis of the basic Ant Colony Optimisation (ACO) algorithm. An ACOEA algorithm with the taboo search and elitist strategy is proposed to form an optimal sequence of multi-product models which can minimise deviation between the ideal material usage rate and the practical material usage rate. In this paper we compare applications of the ACOEA, ACO, and two other commonly applied algorithms (Genetic Algorithm and Goal Chasing Algorithm) to benchmark, stochastic problems and practical problems, and demonstrate that the use of the ACOEA algorithm minimised the deviation between the ideal material consumption rate and the practical material consumption rate under various critical parameters about multi-product models. We also demonstrate that the convergence rate for the ACOEA algorithm is significantly more than that for all the others considered.

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“…Tay and Ho [14] proposed a genetic programming-based approach for evolving effective composite dispatching rules to solve the multi-objective FJSP. Girish and Jawahar [15] proposed a GA for the FJSP for minimum makespan time criterion. Ponnambalam et al [16] employed a genetic algorithm (GA)-based heuristics that have adopted the Giffler and Thompson (GT) procedure, an efficient and active feasible schedule for makespan time criterion.…”

Section: Related Workmentioning

confidence: 99%

A new genetic algorithm for flexible job-shop scheduling problems

2015

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Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop scheduling problem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a new chromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series of benchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficient and competitive than some other existing algorithms.

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Scheduling job shop associated with multiple routings with genetic and ant colony heuristics

Cited by 31 publications

References 20 publications

An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem

An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem

Ant colony optimisation with elitist ant for sequencing problem in a mixed model assembly line

A new genetic algorithm for flexible job-shop scheduling problems

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