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

Asanuma Kohei, Nishi Tatsushi

doi:10.5687/iscie.35.1

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Other Title

入出力データを用いた多目的スケジューリング問題の重み係数推定
ニュウシュツリョクデータオモチイタタモクテキスケジューリングモンダイノオモミケイスウスイテイ

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Abstract

<p>Scheduling problems are widely used in recent production systems. In order to model a production scheduling problem more effectively, it is necessary to build a mathematical modeling technique that automatically generates an appropriate schedule instead of an actual human operator. This paper addresses two types of model estimation methods for weighting factors in the multi-objective scheduling problems from input-output data. The one is a machine learning-based method, and the other one is the parameter estimation method based on an inverse optimization. These methods are applied to three-objectives parallel machine scheduling problems, whose objective functions consist of makespan, the weighted sum of completion time, the weighted sum of tardiness, the weighted sum of earliness and tardiness, and setup costs. The accuracy of the proposed machine learning and inverse optimization methods is evaluated. A surrogate model that learns input-output data is proposed to reduce the computational efforts. Computational results show the effectiveness of the proposed method for weighting factors in the objective function from the optimal solutions.</p>

Journal

Transactions of the Institute of Systems, Control and Information Engineers

Transactions of the Institute of Systems, Control and Information Engineers 35 (1), 1-9, 2022-01-15

THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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