Effective hierarchical optimization by a hierarchical multi-space competitive genetic algorithm for the flexible job-shop scheduling problem

Ishikawa, Shinya; Kubota, Ryosuke; Horio, Keiichi

doi:10.1016/j.eswa.2015.08.003

Cited by 30 publications

(9 citation statements)

References 19 publications

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“…Therefore, the MOFJSP can be divided into two sub-problems: operations assignment and operations sorting. 3,6,11,20,22,23 Specially, the former aims to allocate the machine for each operation, and the latter to sequence the operations on each machine. Accordingly, the coding scheme in TL-HGAPSO consists of two sections: machine allocation (i.e.…”

Section: Ga Modulementioning

confidence: 99%

“…7 As a generalization of the JSP, the FJSP is equally known to be NP-hard and is challenging due to the expanding machine flexibility. [1][2][3][8][9][10][11] The FJSP has important applications in many real industries such as semiconductor manufacturing, automobile assembly, and textile, where a group of machines is capable for each operation. Therefore, from both the theoretical and the practical viewpoint, FJSP is one of the most common scheduling models existing in modern industry environment.…”

Section: Introductionmentioning

confidence: 99%

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An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem

Huang

Guan

Yang

2018

Advances in Mechanical Engineering

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Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into genetic algorithm, and therefore, during the process of evolution, the offspring in genetic algorithm can learn the characteristics of elite chromosomes from the bi-memory learning. For solving multi-objective flexible job-shop scheduling problem, this study proposes a discrete particle swarm optimization algorithm. The population is partitioned into two subpopulations for genetic algorithm module and particle swarm optimization module. These two algorithms simultaneously search for solutions in their own subpopulations and exchange the information between these two subpopulations, such that both algorithms can complement each other with advantages. The proposed algorithm is evaluated on some instances, and experimental results demonstrate that the proposed algorithm is an effective method for multiobjective flexible job-shop scheduling problem.

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Section: Ga Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem

Huang

Guan

Yang

2018

Advances in Mechanical Engineering

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“…Seyed et al implemented the biogeography-based optimisation algorithm [9]. Ishikawa et al devised a multi-space competitive distributed genetic algorithm [10]. Harmony search (HS), particle swarm optimization (PSO) and greedy algorithm were considered by Yuan et al [11], Singh et al [12] and Mati et al [13], respectively.…”

Section: Introductionmentioning

confidence: 99%

Hybrid heuristic algorithm for multi-objective scheduling problem

Jian-gang

Liu

Zhang

et al. 2019

Journal of Systems Engineering and Electronics

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This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search (HS) algorithm, namely the oppositional global-based HS (OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems (MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning (OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory (HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution (TOPSIS), are implemented for solving the MOFJSP. Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies. Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.

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“…We use the five basic structures to improve the GA and IWO, after which we obtain seven algorithms. As we all know, the GA is a well-known, widely used algorithm, and many researchers have used it to solve FJSP [ 17 – 19 ]. Conversely, there are fewer researchers who have used IWO to solve JSP, let alone FJSP.…”

Section: Introductionmentioning

confidence: 99%

Different Performances of Different Intelligent Algorithms for Solving FJSP: A Perspective of Structure

Shi

Long

et al. 2018

Computational Intelligence and Neuroscience

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There are several intelligent algorithms that are continually being improved for better performance when solving the flexible job-shop scheduling problem (FJSP); hence, there are many improvement strategies in the literature. To know how to properly choose an improvement strategy, how different improvement strategies affect different algorithms and how different algorithms respond to the same strategy are critical questions that have not yet been addressed. To address them, improvement strategies are first classified into five basic improvement strategies (five structures) used to improve invasive weed optimization (IWO) and genetic algorithm (GA) and then seven algorithms (S1–S7) used to solve five FJSP instances are proposed. For the purpose of comparing these algorithms fairly, we consider the total individual number (TIN) of an algorithm and propose several evaluation indexes based on TIN. In the process of decoding, a novel decoding algorithm is also proposed. The simulation results show that different structures significantly affect the performances of different algorithms and different algorithms respond to the same structure differently. The results of this paper may shed light on how to properly choose an improvement strategy to improve an algorithm for solving the FJSP.

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Effective hierarchical optimization by a hierarchical multi-space competitive genetic algorithm for the flexible job-shop scheduling problem

Cited by 30 publications

References 19 publications

An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem

An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem

Hybrid heuristic algorithm for multi-objective scheduling problem

Different Performances of Different Intelligent Algorithms for Solving FJSP: A Perspective of Structure

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