Solving the no-wait job-shop problem by using genetic algorithm with automatic adjustment

Bożejko, Wojciech; Makuchowski, Mariusz

doi:10.1007/s00170-011-3297-3

Cited by 17 publications

(3 citation statements)

References 15 publications

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“…(i) For all operators with small, v is a random integer within [u − 4, u + 4] (note that the small swap in [45] means the swap of two adjacent members, and it thus differs from the small swap in LOSAP). (ii) For all operators with medium, v is a random integer within [u − (mn/5), u + (mn/5)].…”

Section: Proposed Lower-level Algorithmmentioning

confidence: 99%

An Enhanced Two-Level Metaheuristic Algorithm with Adaptive Hybrid Neighborhood Structures for the Job-Shop Scheduling Problem

Pongchairerks

2020

Complexity

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For solving the job-shop scheduling problem (JSP), this paper proposes a novel two-level metaheuristic algorithm, where its upper-level algorithm controls the input parameters of its lower-level algorithm. The lower-level algorithm is a local search algorithm searching for an optimal JSP solution within a hybrid neighborhood structure. To generate each neighbor solution, the lower-level algorithm randomly uses one of two neighbor operators by a given probability. The upper-level algorithm is a population-based search algorithm developed for controlling the five input parameters of the lower-level algorithm, i.e., a perturbation operator, a scheduling direction, an ordered pair of two neighbor operators, a probability of selecting a neighbor operator, and a start solution-representing permutation. Many operators are proposed in this paper as options for the perturbation and neighbor operators. Under the control of the upper-level algorithm, the lower-level algorithm can be evolved in its input-parameter values and neighborhood structure. Moreover, with the perturbation operator and the start solution-representing permutation controlled, the two-level metaheuristic algorithm performs like a multistart iterated local search algorithm. The experiment’s results indicated that the two-level metaheuristic algorithm outperformed its previous variant and the two other high-performing algorithms in terms of solution quality.

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Section: Proposed Lower-level Algorithmmentioning

confidence: 99%

An Enhanced Two-Level Metaheuristic Algorithm with Adaptive Hybrid Neighborhood Structures for the Job-Shop Scheduling Problem

Pongchairerks

2020

Complexity

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“…(1) Wait between two consecutive operations of a job: if the no-wait constraint occurs, two successive operations of a job must be processed without any interruption and thus no-wait models are established in different environments: flow shop [55][56][57][58][59][60], hybrid/flexible workshop [61][62][63][64][65], job shop ( [47,[66][67][68]), and open shop [47,54,69].…”

Section: Quantitative Illustrationmentioning

confidence: 99%

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Fei

Shen

2017

Scientific Programming

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Over the past decades, optimization in operations management has grown ever more popular not only in the academic literature but also in practice. However, the problems have varied a lot, and few literature reviews have provided an overview of the models and algorithms that are applied to the optimization in operations management. In this paper, we first classify crucial optimization areas of operations management from the process point of view and then analyze the current status and trends of the studies in those areas. The purpose of this study is to give an overview of optimization modelling and resolution approaches, which are applied to operations management.

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“…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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Solving the no-wait job-shop problem by using genetic algorithm with automatic adjustment

Cited by 17 publications

References 15 publications

An Enhanced Two-Level Metaheuristic Algorithm with Adaptive Hybrid Neighborhood Structures for the Job-Shop Scheduling Problem

An Enhanced Two-Level Metaheuristic Algorithm with Adaptive Hybrid Neighborhood Structures for the Job-Shop Scheduling Problem

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

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

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