A hybrid genetic algorithm for no-wait job shop scheduling problems

Pan, Jason Chao-Hsien; Huang, Han-Chiang

doi:10.1016/j.eswa.2008.07.005

Cited by 52 publications

(12 citation statements)

References 18 publications

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“…The chart shows that the makespan obtained by proposed algorithm was 1324. The optimal schedule we got was [3,17,15,16,8,6,9,18,4,2,14,5,7,11,12,10,1,19,13,20]. In order to further compare the advantages and disadvantages of the proposed algorithm and the other algorithms, three parameters of average error (AE), improvement percentage (IP), and ARPD were introduced to compare the effects of each algorithm.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In order to improve the search efficiency of the algorithm, a new annealing rate method based on the hormone regulation mechanism [27] is used in this paper. The annealing rate can be obtained by Formulas (10)- (12):…”

Section: Annealing Ratementioning

confidence: 99%

“…In [11], three kinds of hybrid discrete artificial bee colony (ABC) algorithm (hDABC1, hDABC2, and hDABC3), with the initial solution generated by MME-A and MME-B and new solutions generated by the adaptation strategy and distribution estimation, were proposed. Pan and Huang [12] developed a hybrid genetic algorithm (GA) based on two different local search strategies (insertion search and the insertion search repair and cut) and an orthogonal-array-based crossover. Gao et al [13] proposed two Bertollisi heuristics [14], which were based on a job pair comparison approach, to generate a sequence of jobs and constructive heuristics combined with standard deviation heuristics.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Wei

Jiang

et al. 2018

Applied Sciences

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Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm’s quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Annealing Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Wei

Jiang

et al. 2018

Applied Sciences

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“…With respect to the exact procedure, there is only one branch and bound algorithm presented in [19]. Moreover, several meta-heuristics such as simulated annealing (SA) [25], tabu search (TS) [18], variable neighborhood search (VNS) [30], hybrid simulated annealing and genetic algorithm (GASA) [34], hybrid genetic algorithm (HGA) [23], and complete local search [10,11,37] have been developed for no-wait job shop (NWJS) problem within recent years. Bożejko and Makuchowski [4] proposed a new systematic parameter adjustment method applied to a genetic algorithm and employed it for solving a no-wait job shop problem.…”

Section: Introductionmentioning

confidence: 99%

A two-stage no-wait job shop scheduling problem by using a neuro-evolutionary variable neighborhood search

Mokhtari

2014

Int J Adv Manuf Technol

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A two-stage order scheduling problem with an additional no-wait condition is studied in this article. All orders must be processed from start to completion without any waiting between operations and any interruption within operations. Examples of such a problem occur in steel production and pharmaceutical, chemical, and construction industries. A new optimization method which is based on a combination of an enhanced variable neighborhood search (VNS) and an artificial neural network (ANN) is devised in this paper. The aim is to simultaneously employ the key characteristics of two approaches to achieve superior solutions in solving the addressed optimization problem. In proposed algorithm, the VNS performs a global search whereas the ANN plays the role of intensive initial solution. The individual obtained by feed forward back-propagation ANN is improved within enhanced VNS iterations. The two techniques complement each other when ANN feeds its best solution to the VNS algorithm. Furthermore, to establish the best sequence of order pairs, a team process algorithm is also embedded into the VNS to enhance the search diversification as well as its intensification. The performance of proposed neuro-evolutionary algorithm is investigated via computational experiments, and the results prove the appropriate performance of suggested VNS-ANN algorithm.

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“…Examples are the inclusions of setup times [13] the adaptation of JSSP to the no-wait job shop [14] the incorporation of alternative objective functions [15] or the extension to the multi-objective JSSP [16].…”

Section:  Preemption Of Operations Is Not Allowedmentioning

confidence: 99%

Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm

Nazif

2015

Journal of Asian Scientific Research

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This paper describes the implementation of an ant colony algorithm (ACA), applied to a combinatorial optimization problem called job shop scheduling problem (JSSP). At first, a rather good solution is generated in negligible computation time and then, the trail intensities areinitiated based on this solution. Moreover, the trail intensities are limited between lower and upper bounds which change dynamically in a new manner. It is noteworthy that in initializing, updating as well as limiting the trail intensities, the goal is to Contribution/ OriginalityThis study is one of the common studies which have investigated to solve the well-known job shop scheduling problem using meta-heuristic algorithms, specially ant colony algorithms.

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A hybrid genetic algorithm for no-wait job shop scheduling problems

Cited by 52 publications

References 18 publications

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

A two-stage no-wait job shop scheduling problem by using a neuro-evolutionary variable neighborhood search

Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm

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