Use of genetic algorithms in operations management: Part 1: Applications

Stockton, David; Quinn, L.; Khalil, R. A.

doi:10.1243/095440504322984867

Cited by 17 publications

(3 citation statements)

References 48 publications

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“…Its idea is to simulate the mechanics of natural evolution and genetics. 17 It is known that genetic algorithm has the characteristic of its potential for parallelization and global optimization. Since genetic algorithm is first proposed as an optimization tool for maintenance scheduling activities by Munoz, it began to be widely applied in maintenance field.…”

Section: Simulation and Analysismentioning

confidence: 99%

Research on maintenance optimal policy based on product quality control

Xiao-mei

Zeng

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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According to the correlation between product quality and equipment degradation state, an equipment maintenance policy is designed by integrating periodic equipment inspection and product quality control. In this policy, np-chart is used to monitor the abnormal shift of the product quality characteristic based on periodic inspection of the equipment. By considering the inspection result of the product quality shift and equipment degradation state, the corresponding maintenance action is chosen. Furthermore, the optimal maintenance model based on product quality control is proposed and is solved with genetic algorithm. The experimental results validated the feasibility of this model.

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Section: Simulation and Analysismentioning

confidence: 99%

Research on maintenance optimal policy based on product quality control

Xiao-mei

Zeng

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Some of them worked on chromosome rep-resentation[e.g. 18,[19][20]; some of them have presented modified GAs on fitness function as the basis of objective function[e.g. 11,21].…”

Section: Alb Problem Classes and Their Objectivesmentioning

confidence: 99%

A Cellular-rearranging of Population in Genetic Algorithms to Solve Assembly-Line Balancing Problem

Cheshmehgaz¹,

Desa²,

Kazemipour³

2012

JMEA

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Assembly line balancing problem (ALBP) is the allocating of assembly tasks to workstations with consideration of some criteria such as time and the number of workstations. Due to the complexity of ALB, finding the optimum solutions in terms of the number of workstations in the assembly line needs suitable meta-heuristic techniques. Genetic algorithms have been used to a large extent. Due to converging to the local optimal solutions to the most genetic algorithms, the balanced exploration of the new area of search space and exploitation of good solutions by this kind of algorithms as a good way can be sharpened with some meta-heuristic. In this paper, the modified cellular (grid) rearranging-population structure is developed. The individuals of the population are located on cells according to the hamming distance value among individuals as neighbours before regenerations and a family of cellular genetic algorithms (CGAs) is defined. By using the cellular structure and the rearrangements, some of the family members can find better solutions compared with others in the same iterations, and they behave much more reasonably in order to acquire the solution in terms of the number of workstations and the smoothly balanced task assignment on criteria conditions.

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“…Several versions of GAs were proposed by many researchers [4][5][6][33][34][35][36]. GAs are very flexible meta-heuristics which can be integrated with many other tools or methodologies.…”

Section: Pso Algorithm Ga and Ahpmentioning

confidence: 99%

Development of simulation-based AHP-DPSO algorithm for generating multi-criteria production–distribution plan

Varthanan¹,

Murugan²,

Kumar

et al. 2011

Int J Adv Manuf Technol

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Integrated production-distribution plan considering three major objectives, viz., total cost minimization, change in labor level reduction, and underutilization minimization, is developed for a renowned bearing manufacturing industry in India. The total cost minimization objective minimizes the regular, overtime, and outsourced production costs along with inventory holding, backorder, hiring/layingoff, and trip-wise distribution costs. The multi-criteria model is solved using a novel simulation-based analytic hierarchy process (AHP)-discrete particle swarm optimization (DPSO) algorithm. The solutions of the AHP-DPSO algorithm are verified using the AHP-binary-coded genetic algorithm solutions. The proposed simulation-based AHP-DPSO solutions are found to be superior. Demand is assumed to vary uniformly, and the simulation-based AHP-DPSO algorithm is used for obtaining the best production-distribution plan that serves as a trade-off between holding inventory and backordering products. In addition to bearing manufacturing industry dataset, two other test datasets are also solved.

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Use of genetic algorithms in operations management: Part 1: Applications

Cited by 17 publications

References 48 publications

Research on maintenance optimal policy based on product quality control

Research on maintenance optimal policy based on product quality control

A Cellular-rearranging of Population in Genetic Algorithms to Solve Assembly-Line Balancing Problem

Development of simulation-based AHP-DPSO algorithm for generating multi-criteria production–distribution plan

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