The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure

Pongcharoen, Pupong; Hicks, C; Braiden, PM

doi:10.1016/s0377-2217(02)00645-8

Cited by 72 publications

(35 citation statements)

References 11 publications

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“…Genetic Algorithms (GAs) has several advantages; multiple directional searches, problem coding instead of decision variables and using stochastic transition rules [5] . It has therefore been widely used to solve Production And Operation Management (POM) problems such as supply chain and logistics [6,13] , production scheduling [12] , facility layout [7] and university course timetabling [1] . However, the GA applications on some POM problem areas such as transportation within logistics chain network [3] , quality planning, short/long term forecasting and short-term capacity planning have rarely been found [4] .…”

Section: Methodsmentioning

confidence: 99%

Solving the Traveling Salesman Problem Using New Operators in Genetic Algorithms

Rahedi¹,

Atoum²

2009

American J. of Applied Sciences

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Problem statement: Genetic Algorithms (GAs) have been used as search algorithms to find near-optimal solutions for many NP problems. GAs require effective chromosome representations as well as carefully designed crossover and mutation operators to achieve an efficient search. The Traveling Salesman Problem (TSP), as an NP search problem, involves finding the shortest Hamiltonian Path or Cycle in a graph of N cities. The main objective of this study was to propose a new representation method of chromosomes using upper triangle binary matrices and a new crossover operator to be used as a heuristic method to find near-optimum solutions for the TSP. Approach: A proposed genetic algorithm, that employed these new methods of representation and crossover operator, had been implemented using DELPHI programming language on a personal computer. Also, for the purpose of comparisons, the genetic algorithm of Sneiw had been implemented using the same programming language on the same computer. Results: The outcomes obtained from running the proposed genetic algorithm on several TSP instances taken from the TSPLIB had showed that proposed methods found optimum solution of many TSP benchmark problems and near optimum of the others. Conclusion: Proposed chromosome representation minimized the memory space requirements and proposed genetic crossover operator improved the quality of the solutions in significantly less time in comparison with Sneiw's genetic algorithm.

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Section: Methodsmentioning

confidence: 99%

Solving the Traveling Salesman Problem Using New Operators in Genetic Algorithms

Rahedi¹,

Atoum²

2009

American J. of Applied Sciences

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“…For example; algorithm to find a protein structure from large number of amino acids and algorithms to find fluctuation in financial markets. Some of the main advantages of GA's can be listed as ( [11], [13] and [14]); 1) GA provides effective use of parallelism i.e. different possibility can be explored simultaneously by using chromosomes.…”

Section: B Multi-objective Optimizationmentioning

confidence: 99%

“…Such a GA cycle is repeated until a desired termination criterion is reached (for example, a predefined number of generations are produced or objective function has been met). If all goes well throughout this process of simulated evolution, the best chromosome in the final population can become a highly evolved solution to the problem ( [11], [13], [14] and [15]). …”

Section: B Multi-objective Optimizationmentioning

confidence: 99%

A Multi-Objective Optimization Approach Using Genetic Algorithms for Quick Response to Effects of Variability in Flow Manufacturing

Khalil¹,

Stockton²,

Kang³

et al. 2012

IJACSA

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Abstract-This paper exemplifies a framework for development of multi-objective genetic algorithm based job sequencing method by taking account of multiple resource constraints. Along this, Theory of Constraints based Drum-Buffer-Rope methodology has been combined with genetic algorithm to exploit the system constraints. This paper introduces the Drum-Buffer-Rope to exploit the system constraints, which may affect the lead times, throughput and higher inventory holding costs. Multi-Objective genetic algorithm is introduced for job sequence optimization to minimize the lead times and total inventory holding cost, which includes problem encoding, chromosome representation, selection, genetic operators and fitness measurements, where Queuing times and Throughput are used as fitness measures. Along this, paper provides a brief comparison of proposed approach with other optimisation approaches. The algorithm generates a sequence to maximize the throughput and minimize the queuing time on bottleneck/Capacity Constraint Resource (CCR). Finally, Results are analysed to show the improvement by using current research framework.

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“…3 The best chromosome in the population (i.e., with the highest fitness) represents a solution (or an approximated solution) to the problem. The authors refer the reader to a recent feature articles from Sevaux et al (2003), Pongcharoen et al (2003), Vijande et al (2003), and Reeves (1997) for an extensive description of genetic algorithm operations.…”

Section: Gentic Algorithmsmentioning

confidence: 99%

A New Optimization Model for 3d Well Design

Shokir¹,

Emera²,

Eid³

et al. 2004

Oil & Gas Science and Technology - Rev. IFP

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Résumé -Nouveau modèle pour l'optimisation de la conception 3D des puits -Ce travail utilise un logiciel se servant d'un algorithme génétique pour déterminer la profondeur optimale de puits directionnels et horizontaux dans un espace 3D. Nous utilisons une fonction spécifique de pénalité, la mutation, les probabilités croisées et un critère de terminaison pour obtenir le minimum global de profondeur de forage. Ce minimum est atteint aux valeurs minimales du point de démarrage, de l'angle de forage et des taux d'augmentation et de déclin de production. Les valeurs minimales de ces paramètres amoindrissent la sévérité des virages, entraînant une réduction des problèmes en opération. La conception optimisée est comparée pour deux puits à la conception conventionnelle (à base d'une méthode d'essai et d'erreur) et au logiciel WELLDES (à base d'une technique de minimisation séquentielle sans contraintes). La conception optimisée permet de réduire la longueur totale de forage des deux puits, tout en maintenant les autres paramètres opérationnels dans les contraintes imposées. Dans la conception conventionnelle et le logiciel WELLDES, quelques variables sortent de leurs limites de contrainte. Oil & Gas Science and Technology -Rev. IFP, Vol. 59 (2004) total drilling measured depth, m T dogleg severity, °/30.48 m ANG1U upper limit on inclination angle after first build section, m ANG2U upper limit on inclination angle after drop section, m ANG3U upper limit on inclination angle after second build section, m ANG1L lower limit on inclination angle after first build, m ANG2L lower limit on inclination angle after drop section, m ANG3L lower limit on inclination angle after second build section, m DKMAX upper limit on depth of kickoff point, m DKMIN lower limit on depth of kickoff point, m CASD1U upper limit on casing setting depth after first build, m CASD2U upper limit on casing setting depth after drop section, m CASD3U upper limit on casing setting depth after second build section, m CASD1L lower limit on casing setting depth after first build, m CASD2L lower limit on casing setting depth after drop section, m CASD3L lower limit on casing setting depth after second build section, m D v2 true vertical depth after at end of first hold section, m D v3 true vertical depth after at end of drop section, m AANG1U upper limit on azimuth angle at kickoff point, degrees AANG2U upper limit on azimuth angle after first build, degrees AANG3U upper limit on azimuth angle after first hold section, degrees AANG4U upper limit on azimuth angle after drop section, degrees AANG5U upper limit on azimuth angle after second hold section, degrees AANG6U upper limit on azimuth angle after second build section, degrees AANG1L lower limit on azimuth angle at kickoff point, degrees AANG2L lower limit on azimuth angle after first build, degrees AANG3L lower limit on azimuth angle after first hold section, degrees AANG4L lower limit on azimuth angle after drop section, degrees AANG5L lower limit on azimuth angle after second hold section, degrees AAN...

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The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure

Cited by 72 publications

References 11 publications

Solving the Traveling Salesman Problem Using New Operators in Genetic Algorithms

Solving the Traveling Salesman Problem Using New Operators in Genetic Algorithms

A Multi-Objective Optimization Approach Using Genetic Algorithms for Quick Response to Effects of Variability in Flow Manufacturing

A New Optimization Model for 3d Well Design

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