A real-world scheduling problem using genetic algorithms

Gilkinson, John C.; Rabelo, Luis; Bush, Brian O.

doi:10.1016/0360-8352(95)00067-b

Cited by 15 publications

(9 citation statements)

References 5 publications

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“…The size of population (P) and the number of generation (G) are set to be larger as the number of department (n) is increased. That is, n is set to 6,8,12,15,20,30, and 50, and the corresponding P is 50, 100, 200, 500, 1000, 2000 and 3000. The corresponding G is 50, 100, 200, 500, 1000, 2000 and 3000.…”

Section: Equal Area Departmentmentioning

confidence: 99%

“…When the number of departments is less than 15, QAP, applying branch-and-bound and cutting plane algorithms [3,4], is able to reach minimal MHC. However, as the number of departments is larger than 15, QAP has been validated to be an NP-complete problem and its computational time is exponentially increased (i.e. 2 n ) [5].…”

Section: Introductionmentioning

confidence: 99%

“…The possibilities for reaching global optimalisation are increased. GAs have been widely implemented to solve combinatorial optimisation problems [15,16,17,18] and are considered to be a robust approach by accompanying them with artificial intelligence [19].…”

Section: Introductionmentioning

confidence: 99%

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Using genetic algorithms on facilities layout problems

Wang

2004

The International Journal of Advanced Manufacturing Technology

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This article utilises a scheme to solve both equal and unequal area department problems in facilities layout by using genetic algorithms (GAs) for achieving the minimal total layout cost (TLC). With regards the equal area department problems, the objective function is mainly developed according to the measurement of the material flow factor cost (MFFC). However, the objective function for unequal area department problems in this study is a multiple objective function involving MFFC, shape ratio factor, and area utilisation factor to reach minimal TLC. In addition, a rule-based expert system is implemented to create space-filling curves for connecting each unequal area department to be continuously placed without discontinuity (partition). There is no gap between each unequal area department. The experimental results show that the proposed approach achieves lower TLC compared with existing algorithms. Under the practical limitations, the proposed approach in this study is much more feasible for dealing with the facilities layout problems in the real world.

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Section: Equal Area Departmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using genetic algorithms on facilities layout problems

Wang

2004

The International Journal of Advanced Manufacturing Technology

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“…The genetic algorithm (GA) has been used to solve many problems, such as dispatch problem [18], scheduling [19], and TSP problem [20]. GA can derive an optimal solution by executing three operators: repro duction, crossover, and mutation [21,22].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Investigation of maintenance float problem using genetic algorithms

Ding

2001

Journal of Information and Optimization Sciences

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Maintenance float problems in a flexible manufacturing system tend to analyti cally intractable while the assumptions of their models become more complex and prac tical; consequently, they are too complex to solve by traditional optimization theories; and therefore, a heuristic algorithm or an enumerative search method is always used to solve them. To tackle the pr'Jblems, in this study, we solve them by a genetic algorithm which is often implemented to search the global optimization in a complex . s.ea.rch space. The numerical example shows that our approach can find the optimal solution of these problems with less effort.

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“…To cope with the contradictory and over-constrained problem, the researchers have developed a model to describe the gradual satisfaction of given constraints considered explicitly. Gilkinson et al (1995) present a GA application to solve the real-world scheduling problem of a company that produces laminated paper and foil products. The manufacturing system is composed of workcell groups (stages with one or more parallel machines).…”

Section: Genetic Algorithm Applications In Realistic Schedulingmentioning

confidence: 99%

Heuristics and metaheuristics for heavily constrained hybrid flowshop problems

Urlings¹

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Es ist nicht genug zu wissen, man muss auch anwenden.Es ist nicht genug zu wollen, man muss auch tun.Knowing is not enough, we must apply.Willing is not enough, we must do.Johann Wolfgang von Goethe, Wilhelm Meisters Wanderjahre, 1821. I ABSTRACT HEURISTICS AND METAHEURISTICS FOR HEAVILY CONSTRAINED HYBRID FLOWSHOP PROBLEMSDue to the current trends in business as the necessity to have a large catalogue of products, orders that increase in frequency but not in size, globalisation and a market that is increasingly competitive, the production sector faces an ever harder economical environment. All this raises the need for production scheduling with maximum efficiency and effectiveness.The first scientific publications on production scheduling appeared more than half a century ago. However, many authors have recognised a gap between the literature and the industrial problems. Most of the research concentrates on optimisation problems that are actually a very simplified version of reality. This allows for the use of sophisticated approaches and guarantees in many cases that optimal solutions are obtained. Yet, the exclusion of real-world restrictions harms the applicability of those methods. What the industry needs are systems for optimised production scheduling that adjust exactly to the conditions in the production plant and that generates good solutions in very little time. This is exactly the objective in this thesis, that is, to treat more realistic scheduling problems and to help closing the gap between the literature and practice.The considered scheduling problem is called the hybrid flowshop problem, which consists in a set of jobs that flow through a number of production stages. At each of the stages, one of the machines that belong to the stage is visited. III IVA series of restriction is considered that include the possibility to skip stages, non-eligible machines, precedence constraints, positive and negative time lags and sequence dependent setup times. In the literature, such a large number of restrictions has not been considered simultaneously before. Briefly, in this thesis a very realistic production scheduling problem is studied.Various optimisation methods are presented for the described scheduling problem. A mixed integer programming model is proposed, in order to obtain optimal solutions for limited cases and in order to analyse the complexity of each of the problem restrictions. In continuation, seven constructive heuristics are presented with the purpose of obtaining fast solutions to the problem in more general cases. Advanced metaheuristic methods are studied in detail, starting with five genetic algorithms that allow studying the effect of the solution representation. Three local search based algorithms are proposed, which is very novel if the elevated complexity of the problem is taken into account. In addition, novel methods are presented that shift the solution representation during the search process in order to acquire near-optimal solutions. The obtained results endorse the use of thes...

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A real-world scheduling problem using genetic algorithms

Cited by 15 publications

References 5 publications

Using genetic algorithms on facilities layout problems

Using genetic algorithms on facilities layout problems

Investigation of maintenance float problem using genetic algorithms

Heuristics and metaheuristics for heavily constrained hybrid flowshop problems

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