A clustering-based modified variable neighborhood search algorithm for a dynamic job shop scheduling problem

Adibi, Mohammad Amin; Shahrabi, Jamal

doi:10.1007/s00170-013-5354-6

Cited by 38 publications

(14 citation statements)

References 25 publications

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“…On one hand, the results are in line with Farid's conclusion which had a good classification accuracy in Adibi and Shahrabi. 32 On the other hand, the algorithm has also been proved to be well applied into job shop.…”

Section: Resultsmentioning

confidence: 99%

Data mining–based disturbances prediction for job shop scheduling

Qiu

Sawhney

Zhang

et al. 2019

Advances in Mechanical Engineering

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In real production manufacturing process, there are many disturbances (e.g. machine fault, shortage of materials, tool damage) which can greatly interfere the original scheduling. These interventions will cost production managers extra time to schedule orders, which increase much workload and cost of maintenance. On account of this phenomenon, a novel system of data mining-based disturbances prediction for job shop scheduling is proposed. It consists of three modules: data mining module, disturbances prediction module, and manufacturing process module. First, in data mining module, historical data and new data are acquired by radio frequency identification or cable from database, and a hybrid algorithm is used to build a disturbance tree which is utilized as a classifier of disturbances happened before manufacturing. Then, in the disturbances prediction module, a disturbances pattern is built and a decision making will be determined according to the similarity between testing data attributes and mined pattern. Finally, in the manufacturing process module, scheduling will be arranged in advance to avoid the disturbances according to the results of decision making. Besides, an experiment is conducted at the end of this article to show the prediction process and demonstrate the feasibility of the proposed method.

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Section: Resultsmentioning

confidence: 99%

Data mining–based disturbances prediction for job shop scheduling

Qiu

Sawhney

Zhang

et al. 2019

Advances in Mechanical Engineering

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“…Whereas the two algorithms achieve similar performance, tabu search generates a larger number of solutions, thus requiring a higher computational time. Adibi et al [17] combined a variable neighborhood search with a clustering approach. The authors introduced a clustering analysis to improve the efficiency of the algorithm by grouping a set of jobs on the basis of the processing time.…”

Section: Literature Reviewmentioning

confidence: 99%

Sales Kit Automated Production: An Integrated Procedure for Setup Reduction in Case of High Products Variety

et al. 2021

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Nowadays, the demand for personalized goods is increasing, with small batches of customized products. Companies are asked to cope with this need to be more competitive; thus, it is crucial to optimally set up the production process to cope with the demand. Among the products composed of several parts, sales kits occupy a significant role. Sales kits are sets of different components, supplied as a single unit. Automation is usually used to reduce the unit direct production costs for assembled products, and this is especially true for kitting since it has minimum added value, compared to other technologies. The wide range of components leads to frequent reconfiguration of the feeding devices between different products, increasing the setup time. The aim of this work is to provide a model that minimizes the setup times of automatic kitting systems when working with a high variety of products. To reach this goal, we propose to apply the traveling salesman problem (TSP) to the production process by integrating a multi-clustering model, thus increasing its appeal to several applications. This method is applied both to simulations and to a real case study, and proves to be able to provide a good solution in a reasonable time.

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“…This method utilized ANN to generate a solution of JSSP and then took it as the initial sequence to a heuristic proposed by Suliman. Adibi et al [24] used a trained artificial neural network (ANN) to update parameters of a metaheuristic method at any rescheduling point in a dynamic JSSP according to the problem condition. Maroosi et al [25] proposed an approach which utilizes the parallel membrane computing method and the harmony search method to solve flexible job shop problems.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation

Zang

Wang

Song

et al. 2019

Computational Intelligence and Neuroscience

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In this paper, a hybrid deep neural network scheduler (HDNNS) is proposed to solve job-shop scheduling problems (JSSPs). In order to mine the state information of schedule processing, a job-shop scheduling problem is divided into several classification-based subproblems. And a deep learning framework is used for solving these subproblems. HDNNS applies the convolution two-dimensional transformation method (CTDT) to transform irregular scheduling information into regular features so that the convolution operation of deep learning can be introduced into dealing with JSSP. The simulation experiments designed for testing HDNNS are in the context of JSSPs with different scales of machines and jobs as well as different time distributions for processing procedures. The results show that the MAKESPAN index of HDNNS is 9% better than that of HNN and the index is also 4% better than that of ANN in ZLP dataset. With the same neural network structure, the training time of the HDNNS method is obviously shorter than that of the DEEPRM method. In addition, the scheduler has an excellent generalization performance, which can address large-scale scheduling problems with only small-scale training data.

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A clustering-based modified variable neighborhood search algorithm for a dynamic job shop scheduling problem

Cited by 38 publications

References 25 publications

Data mining–based disturbances prediction for job shop scheduling

Data mining–based disturbances prediction for job shop scheduling

Sales Kit Automated Production: An Integrated Procedure for Setup Reduction in Case of High Products Variety

Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation

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