Representations in Genetic Algorithm for the Job Shop Scheduling Problem: A Computational Study

Abdelmaguid, Tamer F.

doi:10.4236/jsea.2010.312135

Cited by 19 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our experimentation, GA is programmed with different reproduction and mutation operators'. Instead of selecting operators randomly as in [17], we have built-in reproduction operators and are being used across the representations and the benchmark instances. The benchmark problems used in this paper are taken from OR library [26] available in World Wide Web.…”

Section: Results and Analysismentioning

confidence: 99%

“…PMX and OX crossover techniques use either single point or two point crossover. Different mutation operators used are swap mutation, inversion mutation and insertion or shift mutation reported in the literature [17].…”

Section: Methodsmentioning

confidence: 99%

“…Further to that, T. F. Abdelmaguid [17] in his paper classified GA representations into Model based and Algorithm based. In our opinion, all representations are algorithm based though they appear to be model based.…”

Section: Representation Of the Problem In Ga And Ga Operatorsmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of Different Representations in Genetic Algorithms for Job Shop Scheduling Problem

Jorapur¹,

Puranik²,

Deshpande³

et al. 2014

JSEA

View full text Add to dashboard Cite

Due to NP-Hard nature of the Job Shop Scheduling Problems (JSP), exact methods fail to provide the optimal solutions in quite reasonable computational time. Due to this nature of the problem, so many heuristics and meta-heuristics have been proposed in the past to get optimal or near-optimal solutions for easy to tough JSP instances in lesser computational time compared to exact methods. One of such heuristics is genetic algorithm (GA). Representations in GA will have a direct impact on computational time it takes in providing optimal or near optimal solutions. Different representation schemes are possible in case of Job Scheduling Problems. These schemes in turn will have a higher impact on the performance of GA. It is intended to show through this paper, how these representations will perform, by a comparative analysis based on average deviation, evolution of solution over entire generations etc.

show abstract

Section: Results and Analysismentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Study of Different Representations in Genetic Algorithms for Job Shop Scheduling Problem

Jorapur¹,

Puranik²,

Deshpande³

et al. 2014

JSEA

View full text Add to dashboard Cite

show abstract

“…First, the conventional genetic algorithms for the JSP generally require relatively long processing times [14]. Since the optimal schedules belong to active schedules, the genetic algorithms for solving JSP typically concentrate on producing active schedules [15].…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Job Shop Scheduling Game with GA-based Evaluation

Kim¹

2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

Abstract:The job shop scheduling problem is one of the well-known hardest combinatorial optimization problems, and solving the problem can be complex and time-consuming task. Hence, many undergraduates can not fully experience the scheduling procedures. This paper aims to introduce a computer game designed to enable the players to create and solve the job shop scheduling problems, and the players can learn the topics such as scheduling and optimization via a sense-making experiences provided by the game playing. The game program provides a simple graphic user interface similar with traditional board games played by manipulating blocks, and the players can conveniently search the good schedules in a trial-and-error manner. Moreover, the completed schedules are evaluated by taking the schedules generated by the genetic algorithm into account. For reasonable evaluation, the game program adopts two search strategies called forward and backward search to generate schedules with a wide range of makespans. The integrated job shop scheduling game introduced is well-organized to support overall procedures for job shop scheduling, and the genetic algorithm based evaluation can make the players to compete for better schedules. As a result, both purposes, entertainment and education, can be simultaneously served by the game playing.

show abstract

“…Attempts to explain different available representations and explore the use of a better representation scheme for the job shop scheduling problems while using genetic algorithms was done and the study was conducted over 66 bench mark instances. An attempt was made in [21] to classify the representations as direct and indirect type which was further classified as model based and algorithm based in [22]. Giffler and Thompson proposed an algorithm way back in 1960 [23] which makes use of priority rules and have enough potential to provide feasible or good solutions.…”

Section: Representation Of the Problem In Ga And Ga Operatorsmentioning

confidence: 99%

A Promising Initial Population Based Genetic Algorithm for Job Shop Scheduling Problem

Jorapur¹,

Puranik²,

Deshpande³

et al. 2016

JSEA

View full text Add to dashboard Cite

Job shop scheduling problem is typically a NP-Hard problem. In the recent past efforts put by researchers were to provide the most generic genetic algorithm to solve efficiently the job shop scheduling problems. Less attention has been paid to initial population aspects in genetic algorithms and much attention to recombination operators. Therefore authors are of the opinion that by proper design of all the aspects in genetic algorithms starting from initial population may provide better and promising solutions. Hence this paper attempts to enhance the effectiveness of genetic algorithm by providing a new look to initial population. This new technique along with job based representation has been used to obtain the optimal or near optimal solutions of 66 benchmark instances which comprise of varying degree of complexity.

show abstract

Representations in Genetic Algorithm for the Job Shop Scheduling Problem: A Computational Study

Cited by 19 publications

References 20 publications

Comparative Study of Different Representations in Genetic Algorithms for Job Shop Scheduling Problem

Comparative Study of Different Representations in Genetic Algorithms for Job Shop Scheduling Problem

A Job Shop Scheduling Game with GA-based Evaluation

A Promising Initial Population Based Genetic Algorithm for Job Shop Scheduling Problem

Contact Info

Product

Resources

About