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Interactive minimax optimisation for integrated performance analysis and resource planning
Affiliation:1. Manchester Business School, The University of Manchester, Manchester M15 6PB, UK;2. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, Anhui, PR China;1. Northwestern University, Medill School of Journalism, Media, Integrated Marketing Communications, 1845 Sheridan Road, Evanston, IL 60208-2101, United States;2. Cornell University, Samuel Curtis Johnson Graduate School of Management, 452 Sage Hall, 14853 Ithaca, NY, United States;1. Industrial Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India;2. Chemistry Research Laboratory, Organic Chemistry Division, School of Advanced Sciences, VIT University, Vellore 632014, Tamil Nadu, India;1. Department of Finance, Faculty of Economics, Technical University of Kosice, Bozeny Nemcovej 32, 04200, Kosice, Slovak Republic;2. Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Letna 9, 04200, Kosice, Slovak Republic;1. School of Information Science and Technology at University of Science and Technology of China, Hefei, Anhui, China;2. Institute of Rehabilitation Engineering, University of Rehabilitation, Qingdao, Shandong, China;1. Informatics Department, University of Oviedo, Gijón, Spain;2. Electrical and Computing Engineering Department, Spanish University for Distance Education (U.N.E.D), Madrid, Spain;3. ADN Mobile Solutions, Gijón, Spain;4. Student at PhD program of Industrial Technologies at U.N.E.D, Spain
Abstract:Analysing performances for future improvement and resource planning is a key management function. Data Envelopment Analysis (DEA) provides an analytical mean for performance modelling without assuming parametric functions. Multiple Objective Optimisation (MOO) is well-suited for resource planning. This paper reports an investigation in exploring relationships between DEA and MOO models for equivalent efficiency analysis in a MOO process. It is shown that under certain conditions minimax reference point models are identical to input-oriented dual DEA models for performance assessment. The former can thus be used for Hybrid Efficiency and Trade-off Analyses (HETA). In this paper, these conditions are first established and the equivalent models are explored both analytically and graphically to better understand HETA. Further investigation in the equivalence models leads to the modification of efficiency measures and the development of a minimax reference point approach for supporting integrated performance analysis and resource planning, with the Decision Maker’s (DM) preferences taken into account in an interactive fashion. Both numerical and case studies are conducted to demonstrate the proposed approach and its potential applications.
Keywords:Minimax reference point approach  Interactive multiple objective optimisation  Performance analysis  Resource planning  Data envelopment analysis
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