Estimation of Weighting Factors for Multi-Objective Scheduling Problems using Input-Output Data

Asanuma, Kohei; Nishi, Tatsushi

doi:10.5687/iscie.35.1

Cited by 1 publication

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“…First, the features used for the small-scale problems with 2 objectives are explained [28]. In the 2 objectives case, the maximum completion time and the sum of setup costs are used as objective functions.…”

Section: Small Scale Problems With 2 Objectives Optimization Problemsmentioning

confidence: 99%

“…However, this method has not been applied to the actual scheduling problem in the literature. In reference [28], a method for estimating the weighting factors from past scheduling data has been proposed for small-scale scheduling problems. However, the scale of production processes in the conventional study is not so large because the exact optimal solutions are used to find the weighting factors by using commercial solvers.…”

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confidence: 99%

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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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Section: Small Scale Problems With 2 Objectives Optimization Problemsmentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Estimation of Weighting Factors for Multi-Objective Scheduling Problems using Input-Output Data

Cited by 1 publication

References 10 publications

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

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

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