Weight restrictions in Data Envelopment Analysis: A comprehensive Genetic Algorithm based approach for incorporating value judgments

• Published in: Expert systems with applications Vol. 42; no. 3; pp. 1503 - 1512
• Main Authors: Jain, Vipul, Kumar, Amit, Kumar, Sameer, Chandra, Charu
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.02.2015; Elsevier
• Subjects: Applied sciences; Automotive engineering; Business analytics; Business process; Computer science; control theory; systems; Constrictions; Cross-disciplinary application; Data processing. List processing. Character string processing; Decision analysis; Decision making; Decision theory. Utility theory; Empirical study; Exact sciences and technology; Firm modelling; Flexibility; Genetic Algorithm; Genetic algorithms; Hierarchies; Mathematical models; Mathematics; Memory organisation. Data processing; Methodology and tool; Multi-Criteria Decision Making; Nonparametric inference; Operational research and scientific management; Operational research. Management science; Operations research; Probability and statistics; Problem structuring; Sciences and techniques of general use; Software; Statistics; Business process; Problem structuring; Business analytics; Empirical study; Genetic Algorithm; Methodology and tool; Cross-disciplinary application; Decision analysis; Multi-Criteria Decision Making; Performance evaluation; Motor car; Methodology; Multicriteria analysis; Evolutionary algorithm; Non parametric method; Non parametric estimation; Flexibility; Modeling; Hierarchy; Structuration; Spare part; Minimal distance; Multidisciplinary; Judgment; Data analysis; Decision making; Empirical method; Integrated management; Hierarchized structure; Genetic algorithm; Preference; Problem solving; Firm management; Data envelopment analysis
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2014.09.034

•Hybridizes a GDEA approach with a GA based approach incorporating value judgments.•Uniquely deals with multiple Decision Makers at different hierarchical levels.•Combines weight restrictions into dual role factors assuring DEA feasibility.•A supplier selection application of a well-known automobile spare parts manufacturer. The basic DEA model experiences the weights flexibility problem which is resolved by the method of weight restrictions. The current research incorporating Decision Makers’ (DMs) preferences into weight restrictions is subject to serious limitations such as lacking a framework for dual role factors and not incorporating organizational hierarchy in decision-making. The proposed Genetic Algorithm (GA) based approach for weight restrictions incorporates a dual role factor and organizational hierarchy in decision-making. The approach involves finding a set of weights which are at a minimum distance from all the DMs’ preferences. The approach is flexible and is able to generate a common set of weights and Decision Making Unit (DMU) specific weight restrictions simultaneously. Results from model validation in a well-known automobile spare parts manufacturer in India indicate that the majority of suppliers perceived as highly efficient were actually found to be inefficient in the GA based weight restrictions model. A major contribution of this study is a robust approach to deal with multiple DMs and DEA weights flexibility problem. Another key highlight of the research is translating DMs preferences into a distance function. Using that as a fitness measure within the proposed Evolutionary Algorithms has been done for the first time in the presence of multiple DMs.