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dc.contributor.authorChiu, Min-Chien_US
dc.contributor.authorChen, Tin-Chih Tolyen_US
dc.contributor.authorHsu, Keng-Weien_US
dc.date.accessioned2020-10-05T02:01:10Z-
dc.date.available2020-10-05T02:01:10Z-
dc.date.issued2020-06-01en_US
dc.identifier.urihttp://dx.doi.org/10.3390/math8060998en_US
dc.identifier.urihttp://hdl.handle.net/11536/155216-
dc.description.abstractExisting methods for forecasting the productivity of a factory are subject to a major drawback-the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed binary quadratic programming (MBQP)-ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWA method is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposed methodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposed methodology was also satisfactory.en_US
dc.language.isoen_USen_US
dc.subjectproductivityen_US
dc.subjectlearningen_US
dc.subjectinterval fuzzy numberen_US
dc.subjectmixed binary quadratic programmingen_US
dc.subjectordered weighted averageen_US
dc.titleModeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodologyen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/math8060998en_US
dc.identifier.journalMATHEMATICSen_US
dc.citation.volume8en_US
dc.citation.issue6en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000554695400001en_US
dc.citation.woscount1en_US
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