Correlation of job-shop scheduling problem features with scheduling efficiency

Mirshekarian, Sadegh; Šormaz, Dušan

doi:10.1016/j.eswa.2016.06.014

Cited by 39 publications

(16 citation statements)

References 36 publications

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“…Table 1 presents production scheduling classifications made by Graham et al (1979), which have made the production scheduling classification in three fields: Shop environment, job characteristics and optimality criteria. Planning and scheduling in the production systems are based on mathematical and heuristic methods (Meolic & Brezocnik, 2018), which enable the proper distribution of limited production capacities according to the necessary production activities (Mirshekarian & Šormaz, 2016). Production activities must be carried out in such a way that the company optimizes its performance while achieving the set goals (Alghazi, 2017).…”

Section: Notationmentioning

confidence: 99%

Multi-objective optimization of production scheduling with evolutionary computation: A review

Ojsteršek¹,

Brezočnik²,

Buchmeister³

2020

10.5267/j.ijiec

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Multi-Objective (MO) optimization is a well-known research field with respect to the complexity of production planning and scheduling. In recent years, many different Evolutionary Computation (EC) methods have been applied successfully to MO production planning and scheduling. This paper is focused on making a review of MO production scheduling methods, starting from production scheduling presentation, notation and classification. The research field of EC methods is presented, then EC algorithms` classification is introduced for the purpose of production scheduling optimization. As a main goal, MO optimization is focused on hybrid EC methods, and presenting their advantages and limitations. Finally, a survey of five scientific databases is presented, with the analysis of the scientific publications the terminology development of the scientific field is presented. Using the citation analysis of the scientific publications, the application for the MO optimization in manufacturing scheduling is discussed.

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Section: Notationmentioning

confidence: 99%

Multi-objective optimization of production scheduling with evolutionary computation: A review

Ojsteršek¹,

Brezočnik²,

Buchmeister³

2020

10.5267/j.ijiec

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“…Shiue et al [21] extended the previous work by considering both the input control and the dispatching rule, such as those in a wafer fabrication manufacturing environment. In a novel recent work by Mirshekarian and Sormaz [22], a statistical study of the relationship between JSSP feature and optimal MAKESPAN was conducted. Ramanan et al [23] proposed an artificial neural network-based heuristic method.…”

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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“…Finally, the optimization model and the proposed algorithm were validated by examples. The optimization targets of Mirshekarian and Šormaz () are the makespan, total load, and the maximum load of machines, and a discrete free search optimization algorithm based on Pareto was proposed. Finally, the optimization model and proposed algorithm were validated by examples.…”

Section: Related Workmentioning

confidence: 99%

A scheduling algorithm based on the singular value decomposition heuristic method in a distributed manufacturing system

Shao

2019

Expert Systems

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Because distributed manufacturing technology is the foundation of modernized production and traditional heuristic methods exhibit problems of high complexity and low efficiency, this paper designs a scheduling algorithm based on the singular value decomposition heuristic (SVDH) method. The algorithm uses the device distribution and the transportation relationship between devices in a distributed manufacturing system. The algorithm takes the sequence relationship between tasks and the distance between devices as the implicit relationship between the task and the device. The algorithm makes use of the implicit relationship to amend the processing time matrix of the task and corrects the processing time matrix that contains the transportation relationship. Singular value decomposition principal component analysis is performed on the corrected processing time to find the most suitable processing device for each process, and an initial solution matrix is established. The heuristic solution is used to optimize the initial solution to find the optimal scheduling result based on the initial solution matrix. The establishment of the initial solution can effectively reduce the computational complexity of the heuristic solution, realize a parallelizing solution, and improve the efficiency of the heuristic solutions. In addition, the SVDH scheduling result has a lower transfer time between devices due to the consideration of the topology of tasks and devices, that is, the transit time. In this paper, the experiments are conducted on the heuristic performance, scheduling results, and transportation time. The experimental results show the advantages of SVDH over general heuristic algorithms in terms of efficiency and transit time.

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Correlation of job-shop scheduling problem features with scheduling efficiency

Cited by 39 publications

References 36 publications

Multi-objective optimization of production scheduling with evolutionary computation: A review

Multi-objective optimization of production scheduling with evolutionary computation: A review

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

A scheduling algorithm based on the singular value decomposition heuristic method in a distributed manufacturing system

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