Variable Neighborhood Genetic Algorithm for the Flexible Job Shop Scheduling Problems

Zhang, Guo Hui; Gao, Liang; Li, Xinyu; Li, Peigen

doi:10.1007/978-3-540-88518-4_54

Cited by 21 publications

(7 citation statements)

References 10 publications

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“…Computational time is determined to be 120 s and the convergence time of algorithm for each problem is reported. The results of execution of the algorithms on the problems are presented in Tables 10 where the following notations are Tables 11 and 12 compare VNS and KBVNS algorithms with LEGA of Ho et al [13], GA of Pezzella et al [22], VNGA of Zhang et al [34], and KBACO of Xing et al [33]. Table 11 indicates that the makespan obtained by KBVNS is equal or smaller than that of other algorithms for all problems except problem MK01 by KBACO.…”

Section: Resultsmentioning

confidence: 93%

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An efficient knowledge-based algorithm for the flexible job shop scheduling problem

Karimi¹,

Rahmati²,

Zandieh³

2012

Knowledge-Based Systems

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Section: Resultsmentioning

confidence: 93%

“…Pezzella et al [22] proposed a GA for FJSP in which different types of genetic operators are mixed. Zhang et al [34] proposed a Variable Neighborhood Genetic Algorithm (VNGA) for FJSP with the objective to minimize total completion time of jobs (makespan). VNGA is a combination of the variable neighborhood search and genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

An efficient knowledge-based algorithm for the flexible job shop scheduling problem

Karimi¹,

Rahmati²,

Zandieh³

2012

Knowledge-Based Systems

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“…According to this definition, we can consider that the main difference between flexible job shop scheduling problem and job shop scheduling problem (JSP) is that flexible job shop problem dictates operation flexibility. The FJSP could be separated into two sub-problems: routing (assigning operations to machines) and scheduling (sequencing the assigned operations on each machine) [30]. Hence, the modified iterated greedy must deal with both of the sub-problems.…”

Section: Presented Algorithm (Mig)mentioning

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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“…In [11], a hierarchical algorithm hybridizing the machine selection module and the operation scheduling module was proposed for solving the FJSP with parallel machines and reentrant process. In [12], a variable neighborhood genetic algorithm was presented for the FJSP with makespan criterion. In [13], a multistage operation-based GA was proposed to deal with the problem from the point of view of dynamic programming.…”

Section: Introductionmentioning

confidence: 99%

An effective artificial bee colony algorithm for the flexible job-shop scheduling problem

Wang

Zhou

et al. 2011

Int J Adv Manuf Technol

169

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An effective artificial bee colony (ABC) algorithm is proposed in this paper for solving the flexible jobshop scheduling problem with the criterion to minimize the maximum completion time (makespan). The ABC algorithm stresses the balance between global exploration and local exploitation. First, multiple strategies are utilized in a combination to generate the initial solutions with certain quality and diversity as the food sources. Second, crossover and mutation operators are well designed for machine assignment and operation sequence to generate the new neighbor food sources for the employed bees. Third, a local search strategy based on critical path is proposed and embedded in the searching framework to enhance the local intensification capability for the onlooker bees. Meanwhile, an updating mechanism of population by generating the scout bees with the initialing strategy is proposed to enrich the searching behavior and avoid the premature convergence of the algorithm. In addition, a well-designed leftshift decoding scheme is employed to transform a solution to an active schedule. Numerical simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed ABC algorithm.

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Variable Neighborhood Genetic Algorithm for the Flexible Job Shop Scheduling Problems

Cited by 21 publications

References 10 publications

An efficient knowledge-based algorithm for the flexible job shop scheduling problem

An efficient knowledge-based algorithm for the flexible job shop scheduling problem

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

An effective artificial bee colony algorithm for the flexible job-shop scheduling problem

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