Multi-criteria optimisation-based dynamic scheduling for controlling FMS

Shnits, Boris

doi:10.1080/00207543.2012.657805

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Fattahi and Fallahi, Li et al [44,45] discussed the balance between efficiency and the stability of dynamic scheduling problems. Shnits [46] emphasized that the decision criteria for the optimization problem should be changed as the system conditions change. Na and Park [47] dealt with a scheduling problem with multi-level job structures and also pointed out that the scheduling is a short-term planning and controlling activity, and could change frequently in accordance with the production shop status.…”

Section: Related Workmentioning

confidence: 99%

“…Na and Park [47] dealt with a scheduling problem with multi-level job structures and also pointed out that the scheduling is a short-term planning and controlling activity, and could change frequently in accordance with the production shop status. In this study, the feedback control method adopts similar decision support frameworks as those of [46][47][48][49][50]. As in most of the above research, a genetic algorithm is used in this paper to develop a solution for the scheduling problem.…”

Section: Related Workmentioning

confidence: 99%

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A Feedback Control Method for Addressing the Production Scheduling Problem by Considering Energy Consumption and Makespan

Wang

2017

Sustainability

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Due to various factors of uncertainty within production, the key performance indicators connected to production plans are difficult to fulfil. This problem becomes especially serious as emission regulations are enforced, which discourage manufacturers from high emission output and high energy consumption. Thus, this paper proposes a feedback control method for the production scheduling problem by considering energy consumption and makespan to help manufacturers keep production implementations in pace with production plans. The proposed method works in a rolling horizon framework, which establishes planned energy consumption and makespan, and adjusts the weights of the multiple scheduling optimization objectives for the next period, based on the feedback of the actual energy consumption and makespan in previous periods. A job shop scheduling case study is provided to illustrate the proposed method. The experiment results demonstrate the effectiveness of the proposed feedback control method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Feedback Control Method for Addressing the Production Scheduling Problem by Considering Energy Consumption and Makespan

Wang

2017

Sustainability

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“…Schnits [12] studied the multi-criteria dynamic scheduling problem for FMS. Gao [13] reported harmony search algorithm to solve the multi-objective flexible job shop scheduling problem.…”

Section: Fig 1 a Typical Scheduling Problem Relationshipsmentioning

confidence: 99%

Multicriteria Dynamic Scheduling by Topsis and Goal Programming

Amol¹

2016

DAAAM Proceedings

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The present research work presents an algorithm for dynamic scheduling problems that optimize several criteria simultaneously. This algorithm is based on dispatching rules, TOPSIS and goal programming and integrates them to solve the multi-criteria dynamic job shop scheduling problems. The selection of dispatching rules is made by identifying the worst performing criterion. A rule, which can improve system performance for the worst performing criterion is selected to dispatch the part under consideration. The proposed multi-criteria dynamic scheduling algorithm is evaluated and compared with best reported scheduling rules. The results obtained demonstrate that the suggested algorithm proves it superiority over individual dispatching rules under multi-criteria dynamic job shop environment.

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“…For the three-objective scheduling problem, a three-phase decision-making method is constructed, and finally, a data envelopment analysis technique is applied to determine the preferred schedule [26]. Dynamic scheduling is proposed to solve the optimization problem of selecting the next job for each schedule [27]. In multi-objective flow shop scheduling, optimization algorithms are used which consider changes in material requirements, inventory control, and delivery dates [23].…”

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confidence: 99%

Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems

et al. 2022

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In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective.

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Multi-criteria optimisation-based dynamic scheduling for controlling FMS

Cited by 5 publications

References 21 publications

A Feedback Control Method for Addressing the Production Scheduling Problem by Considering Energy Consumption and Makespan

A Feedback Control Method for Addressing the Production Scheduling Problem by Considering Energy Consumption and Makespan

Multicriteria Dynamic Scheduling by Topsis and Goal Programming

Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems

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