{"title":"Data Envelopment Analysis as a Kaizen Tool: SBM Variations Revisited","authors":"K. Tone","doi":"10.18052/WWW.SCIPRESS.COM/BMSA.16.49","DOIUrl":null,"url":null,"abstract":"Slacks-based measure (SBM) (Tone (2001), Pastor et al. (1999)) has been widely utilized as a representative non-radial DEA model. In Tone (2010), I developed four variants of the SBM model where main concerns are to search the nearest point on the efficient frontiers of the production possibility set. However, in the worst case, a massive enumeration of facets of polyhedron associated with the production possibility set is required. In this paper, I will present a new scheme, called SBM-Max , for this purpose which requires a limited number of additional linear program solutions for each inefficient DMU. Although the point thus obtained is not always the nearest point, it is acceptable for practical purposes and from the point of computational loads. Inefficient DMUs can be improved to the efficient status with less input- reductions and less output-enlargement. Thus, this model proposes a Kaizen (improvement) tool by DEA.","PeriodicalId":252632,"journal":{"name":"Bulletin of Mathematical Sciences and Applications","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of Mathematical Sciences and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18052/WWW.SCIPRESS.COM/BMSA.16.49","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
Abstract
Slacks-based measure (SBM) (Tone (2001), Pastor et al. (1999)) has been widely utilized as a representative non-radial DEA model. In Tone (2010), I developed four variants of the SBM model where main concerns are to search the nearest point on the efficient frontiers of the production possibility set. However, in the worst case, a massive enumeration of facets of polyhedron associated with the production possibility set is required. In this paper, I will present a new scheme, called SBM-Max , for this purpose which requires a limited number of additional linear program solutions for each inefficient DMU. Although the point thus obtained is not always the nearest point, it is acceptable for practical purposes and from the point of computational loads. Inefficient DMUs can be improved to the efficient status with less input- reductions and less output-enlargement. Thus, this model proposes a Kaizen (improvement) tool by DEA.
基于松弛测度(SBM) (Tone (2001), Pastor et al.(1999))作为一种代表性的非径向DEA模型被广泛使用。在Tone(2010)中,我开发了四种SBM模型的变体,其中主要关注的是在生产可能性集的有效边界上搜索最近的点。然而,在最坏的情况下,与生产可能性集相关的多面体面的大量枚举是必需的。在本文中,我将为此提出一种称为SBM-Max的新方案,该方案需要为每个低效DMU提供有限数量的额外线性程序解决方案。虽然这样得到的点并不总是最近的点,但从实际目的和计算负荷的角度来看,这是可以接受的。低效率的决策单元可以通过减少投入和增加产出来提高效率。因此,该模型提出了一种基于DEA的改善工具。