An adaptive Genetic Algorithm for the Flexible Job-shop Scheduling Problem

Pan, Ying; Zhang, W.X.; Gao, Tian; Ma, Qingyue; Xue, Dong Juan

doi:10.1109/csae.2011.5952878

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…In response to genetic algorithm limitations, it is comprehensible to consider the adaptation of parameters within the genetic algorithm that improves the optimization search capabilities. In fact, adaptable genetic algorithms (AGA) can adjust crossover and mutation probability to alleviate Genetic algorithm problems (Pan et al, 2011). AGA has a monitoring and actuator module that, according to current key performance measures, will modify the parameters to improve optimization search.…”

Section: Adaptive Genetic Algorithmmentioning

confidence: 99%

“…The adaptive metaheuristic, one of the most studied mechanisms, is a population-based technique that simulates the evolution process in order to reach an optimal or near-optimal solution regarding a fitness indicator. The AGA, which inherited the characteristics of genetic algorithms (GA), has strong optimization ability, fast calculation, simple principles and operation, robust generality implicit parallelism and global search space ability (Pan et al, 2011). For this reason, we believe that the featured characteristics of AGA can be used in order to control the manufacturing execution framed under a predictive-reactive approach.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive genetic algorithm for a dynamic single-machine scheduling problem

Jiménez¹,

González-Neira²,

Zambrano-Rey³

2018

10.5267/j.msl

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Nowadays, industries cope with a wide range of situations and/or perturbations that endanger the manufacturing productivity. Traditionally, manufacturing control systems are responsible for managing the manufacturing scheduling and execution, as these have the capability of maintaining the production operations regardless of a given perturbation. Still, the challenge of these systems is to achieve an optimal performance after the perturbations occur. For this reason, manufacturing control systems must incorporate a mechanism with intelligent capabilities to look for optimal performance and operation reactivity regardless of any scenario. This paper proposes a generic control strategy for a manufacturing control system for piloting the execution of a dynamic scheduling problem, considering a new job arrival as the manufacturing perturbation. The study explores a predictive-reactive approach that couples a genetic algorithm for the predictive scheduling and an adaptive genetic algorithm for reactivity control aiming to minimize the weighted tardiness in a dynamic manufacturing scenario. The results obtained from this proposal verify that the effectiveness was improved by using adaptive metaheuristic in a dynamic scheduling problem, considering absorbing the degradation caused by the perturbation.

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Section: Adaptive Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive genetic algorithm for a dynamic single-machine scheduling problem

Jiménez¹,

González-Neira²,

Zambrano-Rey³

2018

10.5267/j.msl

View full text Add to dashboard Cite

show abstract

“…AGAs have already been established in the JSP in previous work. The approach proposed in [19] receives information of the evolutionary stage, classified as dynamic control, and different to the adaptive idea proposed here.…”

Section: Introductionmentioning

confidence: 99%

“…Also, there is a constructive heuristic used to generate the initial population, if necessary it may be applied to improve the results. The AGA selects the parameters according to the feedback received in the evolutionary process, whereas in work of [19], the parameters are adjusted according to the number of generations, without any feedback. This proposal differs from the work of [15] as our proposal does not need a neighborhood structure and it selects the parameters (crossover and mutation), while that approach the adaptation scheme only happens in the selection mechanism.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Pursuit Genetic Algorithm for Solving Job-Shop Scheduling Problems

Ferreira¹,

Bernardino²

2018

Proceedings XIII Brazilian Congress on Computacional Intelligence

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When a metaheuristic is used for solving Job-Shop Scheduling Problems (JSPs), ones need to select the correct movement operators and their parameters to improve the results for them. However, to make a correct setup of a problem is a hard work and it is problem-dependent. In this work, we propose the use of an Adaptive Genetic Algorithm (AGA) to automatically control the techniques contained in its framework while it is solving the problem. An Adaptive Pursuit Method with Extreme Credit Assignment is used to select the movement operators and their rates (crossover and mutation techniques), and it is used to select the Local Search rate. The algorithm is tested using instances provided by a well-known generator for JSPs. The results show superior performance and reliability when compared to a standard genetic algorithm.

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“…A genetic algorithm (GA) is a population-based optimisation technique that simulates the evolution process in order to reach an optimal or near-optimal solution according to a fitness function (Holland 1992). It has powerful optimisation ability, fast calculation, simple principles and operations, robust generality, and global search space ability (Pan et al 2011). In this technique, an appropriate encoding, called chromosome, is defined to represent a feasible solution to the problem.…”

Section: Dynamic Characteristics and Switching Mechanismmentioning

confidence: 99%

Pollux: a dynamic hybrid control architecture for flexible job shop systems

Jiménez

Bekrar

Zambrano-Rey

et al. 2016

International Journal of Production Research

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Nowadays, manufacturing control systems can respond more effectively to exigent market requirements and real-time demands. Indeed, they take advantage of changing their structural and behavioural arrangements to tailor the control solution from a diverse set of feasible configurations. However, the challenge of this approach is to determine efficient mechanisms that dynamically optimise the configuration between different architectures. This paper presents a dynamic hybrid control architecture that integrates a switching mechanism to control changes at both structural and behavioural level. The switching mechanism is based on a genetic algorithm and strives to find the most suitable operating mode of the architecture with regard to optimality and reactivity. The proposed approach was tested in a real flexible job shop to demonstrate the applicability and efficiency of including an optimisation algorithm in the switching process of a dynamic hybrid control architecture.

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An adaptive Genetic Algorithm for the Flexible Job-shop Scheduling Problem

Cited by 8 publications

References 9 publications

An adaptive genetic algorithm for a dynamic single-machine scheduling problem

An adaptive genetic algorithm for a dynamic single-machine scheduling problem

An Adaptive Pursuit Genetic Algorithm for Solving Job-Shop Scheduling Problems

Pollux: a dynamic hybrid control architecture for flexible job shop systems

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