A genetic algorithm methodology for complex scheduling problems

Norman, Bryan A.; Bean, James C.

doi:10.1002/(sici)1520-6750(199903)46:2<199::aid-nav5>3.0.co;2-l

Cited by 109 publications

(46 citation statements)

References 23 publications

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“…One notable exception is the work done by Norman and Bean (1999) in the application of a random keys genetic algorithm to a complex production problem, which is in many respects similar to the problem faced by Optimatix. Multiple machines, ready times, sequence-dependent set-up times, machine down time and scarce tools are addressed.…”

Section: The Problem Contextmentioning

confidence: 99%

“…The P-PSO is, therefore, benchmarked against four other algorithms of which the first two algorithms are existing Optimatix algorithms currently in use. For the third algorithm Norman and Bean's (1999) RKGA was identified as the most promising algorithm identified from existing literature. Finally, the use of alternative metaheuristics within the priority-based algorithm framework was investigated through the implementation of a priority-based differential evolution (DE) algorithm .…”

Section: Comparison Of the P-pso Algorithm Against Alternative Solutimentioning

confidence: 99%

“…This greatly increases the time to solution, while a fast solution time is considered to be an important requirement in the South African manufacturing industry. For these reasons, the random keys genetic algorithm (RKGA) of Norman and Bean (1999) was identified as a more suitable alternative for benchmarking purposes. This algorithm was developed for a more highly constrained problem and does not make use of a local search mechanism.…”

Section: An Existing Algorithm Selected From Literaturementioning

confidence: 99%

“…As the name implies, the RKGA of Norman and Bean (1999) applies a genetic algorithm to the random keys representation used for the RKPSO in the previous section. Elitism is incorporated by automatically including the α best individuals from the parent population into the new population.…”

Section: An Existing Algorithm Selected From Literaturementioning

confidence: 99%

“…, (n s − α − β)} random selection is used to select two individuals from the current population, namely x r 1 (t) and x r 2 (t), where x r k j (t) denotes the j th dimension of the k th vector of individual r k of generation t . Then, Bernoulli crossover (Norman and Bean 1999) is applied such that for all dimensions,…”

Section: An Existing Algorithm Selected From Literaturementioning

confidence: 99%

See 4 more Smart Citations

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Grobler

Engelbrecht²,

Kok³

et al. 2009

Ann Oper Res

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This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage.

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Section: The Problem Contextmentioning

confidence: 99%

Section: Comparison Of the P-pso Algorithm Against Alternative Solutimentioning

confidence: 99%

Section: An Existing Algorithm Selected From Literaturementioning

confidence: 99%

Section: An Existing Algorithm Selected From Literaturementioning

confidence: 99%

Section: An Existing Algorithm Selected From Literaturementioning

confidence: 99%

See 3 more Smart Citations

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Grobler

Engelbrecht²,

Kok³

et al. 2009

Ann Oper Res

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A Genetic Algorithm Heuristic for Finite Horizon Partially Observed Markov Decision Problems

Lin¹,

Bean²,

White³

2003

International Series in Operations Research &Amp; Management Science

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Spare Capacity Planning for Survivable Mesh Networks

Al-Rumaih

Tipper

Liu

et al. 2000

Networking 2000 Broadband Communications, High Performance Networking, and Performance of Communication Networks

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Abstract. The design of survivable mesh based STM networks has received considerable attention in recent years and is a complex multi-constraint optimization problem.In this paper, a new spare capacity planning methodology is proposed utilizing genetic algorithms. The method is based on forcing flows/traffic which are on paths that are disjoint to share backup spare capacity. The major advantages of the new approach are a polynomial time complexity and the capability of incorporating nonlinear variables such as nonlinear cost functions into the solution algorithm. Numerical results illustrating the form of the genetic algorithm solution and comparing the proposed methodology to existing techniques from the literature are presented.

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A genetic algorithm methodology for complex scheduling problems

Cited by 109 publications

References 23 publications

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

A Genetic Algorithm Heuristic for Finite Horizon Partially Observed Markov Decision Problems

Spare Capacity Planning for Survivable Mesh Networks

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