An integrated data envelopment analysis and mixed integer non-linear programming model for linearizing the common set of weights

The problem of ranking efficient decision making units (DMUs) is of interest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP) model to find the most efficient DMU using a common set...

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Bibliographic Details
Published in:Central European journal of operations research Vol. 27; no. 4; pp. 887 - 904
Main Authors: Toloo, Mehdi, Tavana, Madjid, Santos-Arteaga, Francisco J.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019
Springer
Springer Nature B.V
Subjects:
Business and Management
Data envelopment analysis
Decision making
Decision theory
Facilities management
Integer programming
Linear programming
Management science
Mathematical research
Mixed integer
Nonlinear programming
Operations research
Operations Research/Decision Theory
Original Paper
Czech Republic
Mixed integer linear programming
Most efficient DMU
Common set of weights
Data envelopment analysis
ISSN:1435-246X, 1613-9178
Online Access:Get full text
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Summary:The problem of ranking efficient decision making units (DMUs) is of interest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP) model to find the most efficient DMU using a common set of weights. We linearize the MINLP model to an equivalent mixed integer linear programming (MILP) model by eliminating the non-linear constraints in which the products of variables are incorporated. The formulated MILP model is simpler and computationally more efficient. In addition, we introduce a model for finding the value of epsilon, since the improper choice of the non-Archimedean epsilon may result in infeasible conditions. We use a real-life facility layout problem to demonstrate the applicability and exhibit the efficacy of the proposed model.
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ISSN:1435-246X
1613-9178
DOI:10.1007/s10100-017-0510-y