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多属性决策的兼容性与相关性:以产业转移为例

Compatibility and correlation of multi-attribute decision making: a case of industrial relocation.

作者信息

Martino Neto Jose, Salomon Valerio Antonio Pamplona, Ortiz-Barrios Miguel Angel, Petrillo Antonella

机构信息

Department of Production, Sao Paulo State University, Av. Ariberto P. Cunha 333, Guaratingueta, SP 12516-410 Brazil.

Department of Productivity and Innovation, Universidad de la Costa CUC, 58th Street # 55-66, Barranquilla, Colombia.

出版信息

Ann Oper Res. 2022 Feb 28:1-22. doi: 10.1007/s10479-022-04603-9.

DOI:10.1007/s10479-022-04603-9
PMID:35250141
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8884106/
Abstract

Industrial relocation (IR) is a business strategy consisting of moving operations locations. The purpose of this paper is to present how to assess, with multi-attribute decision-making (MADM), alternatives for IR. With MADM, IR strategies can be assessed not only based on a single attribute, as costs, or profits. This paper presents the application of MADM in a real case of IR. Four leading methods of MADM were applied: analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), multi-attribute value theory (MAVT), and technique of order preference by similarity to ideal solution (TOPSIS). Results of AHP, MAUT, MAVT, and TOPSIS were quite similar, indicating the decision for the company not to relocate. A joint comparison of results with compatibility indices and correlation coefficients is the major novelty presented by this paper to the field of Operations Research, known as MADM.

摘要

产业转移是一种涉及运营地点迁移的商业战略。本文旨在介绍如何运用多属性决策方法来评估产业转移的备选方案。借助多属性决策,产业转移战略的评估不仅可以基于单一属性,如成本或利润。本文展示了多属性决策在一个产业转移实际案例中的应用。应用了四种主要的多属性决策方法:层次分析法(AHP)、多属性效用理论(MAUT)、多属性价值理论(MAVT)以及逼近理想解排序法(TOPSIS)。层次分析法、多属性效用理论、多属性价值理论和逼近理想解排序法的结果颇为相似,表明该公司不应进行迁移的决策。本文通过结果与兼容性指标和相关系数的联合比较,为运筹学领域(即多属性决策)带来了主要的创新点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ef/8884106/b44a8869e078/10479_2022_4603_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ef/8884106/d93523a54918/10479_2022_4603_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ef/8884106/27aa268e87f9/10479_2022_4603_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ef/8884106/b44a8869e078/10479_2022_4603_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ef/8884106/d93523a54918/10479_2022_4603_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ef/8884106/27aa268e87f9/10479_2022_4603_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ef/8884106/b44a8869e078/10479_2022_4603_Fig3_HTML.jpg

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