Solving the Flexible Job Shop Scheduling Problem With Makespan Optimization by Using a Hybrid Taguchi-Genetic Algorithm

Chang, Hao‐Chin; Chen, Yeh-Peng; Liu, Tung-Kuan; Chou, Jyh–Horng

doi:10.1109/access.2015.2481463

Cited by 64 publications

(34 citation statements)

References 19 publications

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“…We compared the results with 11 algorithms from literature; HA, HTGA, GATS, TABC, Heuristic, AMMA, HHS, hGA, TS, and IACO. These algorithm were proposed by Li and Gao [1], Chang et al [50], Nouri et al [41], Gao et al [42], Ziaee [51], Zuo et al [52] and Yuan et al [43]. Results of AIA and TS are taken from Yuan et al [43].…”

Section: Methodsmentioning

confidence: 99%

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Aqel

Gao

2019

Chin. J. Mech. Eng.

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The flexible job shop scheduling problem (FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them difficult to code and not easy to reproduce. This paper proposes a modified iterated greedy (IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an effective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.

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Section: Methodsmentioning

confidence: 99%

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Aqel

Gao

2019

Chin. J. Mech. Eng.

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“…To put it plainly, the brilliant transformation and the proficient FEDA calculation offered a successful and practical approach to viably handle the dynamic and adaptable fluffy employment shop booking issue. Chang et al (2015) proposed concept provides correct solutions for the makespan problem using Hybrid Taguchi-genetic Algorithm (HTGA) correct jobs at the correct time [22]. Marichelvam et al (2018) presented the HLOA compare with two benchmark data sets,and the results to be improved in the FJSSP.…”

Section: Ma Et Al (2014) Targeted Hybrid Particle Swarmmentioning

confidence: 99%

A Hybrid Algorithm using Grey Wolf and Chicken Swarm for Flexible Job Shop Scheduling

2019

IJRTE

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The Job Shop Scheduling Problem (JSSP) is one of the major inconvenience models because it is the hardest combinatorial change in nature. So far number of algorithms developed to solve JSSP and still there is scope to develop an efficient algorithm. Hence, this paper considered a unique hybrid algorithm which is the combination of the Grey Wolf and Chicken Swarm Optimization algorithm with the objective of minimization of makespan time and cost. The most extraordinary compilation (makespan), and the tardiness is considered in the meantime. The hybrid algorithm count relies upon the reenactment of the swarming behavior of wolves’ individuals. The base objectives of each wolf and swarms from most bewildering thicknesses of the group are considered as the objective work for the improvement. The results show that the proposed hybrid Grey Wolf and Chicken Swarm Optimization algorithm can obtain better results in terms of various iteration levels. The accuracy and robustness are also applied in real industrial conditions and for large size problems. The proposed method results show that very less time duration when compared with the Genetic Algorithm (GA) and Ants Colony Optimization (ACO) algorithm.

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“…In this study, the intelligent hybrid Taguchi-genetic algorithm (IHTGA), which is proposed by modifying the works given by Tsai et al [6], Chang et al [7] and Tsai et al [8]- [10] is presented to optimize bearing offsets for shaft alignment in a marine vessel propulsion system to obtain acceptable limits for shear forces, bending moments, and normal stresses in the shaft and for reaction forces and stresses in the bearings under cold and hot conditions.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hybrid Taguchi-Genetic Algorithm for Multi-Criteria Optimization of Shaft Alignment in Marine Vessels

Tsai

Chou

et al. 2016

IEEE Access

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An intelligent hybrid Taguchi-genetic algorithm (IHTGA) is used to optimize bearing offsets and shaft alignment in a marine vessel propulsion system. The objectives are to minimize normal shaft stress and shear force. The constraints are permissible reaction force, bearing stress, shear force, and bending moment in the shaft thrust flange under cold and hot operating conditions. Accurate alignment of the shaft for a main propulsion system is important for ensuring the safe operation of a vessel. To obtain a set of acceptable forces and stresses for the bearings and shaft under operating conditions, the optimal bearing offsets must be determined. Instead of the time-consuming classical local search methods with some trial-and-error procedures used in most shipyards to optimize bearing offsets, this paper used IHTGA. The proposed IHTGA performs Taguchi method between the crossover operation of the conventional GA. Incorporating the systematic reasoning ability of Taguchi method in the crossover operation enables intelligent selection of genes used to achieve crossover, which enhances the performance of the IHTGA in terms of robustness, statistical performance, and convergence speed. A penalty function method is performed using the fitness function as a pseudo-objective function comprising a linear combination of design objectives and constraints. A finite-element method is also used to determine the reaction forces and stresses in the bearings and to determine normal stresses, bending moments, and shear forces in the shaft. Computational experiments in a 2200 TEU container vessel show that the results obtained by the proposed IHTGA are significantly better than those obtained by the conventional local search methods with some trial-and-error procedures.INDEX TERMS Marine vessel propulsion system, bearing offsets, shaft alignment, optimal design, genetic algorithm.

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Solving the Flexible Job Shop Scheduling Problem With Makespan Optimization by Using a Hybrid Taguchi-Genetic Algorithm

Cited by 64 publications

References 19 publications

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

A Hybrid Algorithm using Grey Wolf and Chicken Swarm for Flexible Job Shop Scheduling

Intelligent Hybrid Taguchi-Genetic Algorithm for Multi-Criteria Optimization of Shaft Alignment in Marine Vessels

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