Performance Analysis integrating Data Envelopment Analysis and Multiple Objective Linear Programming

  • Layla Ashoor Khalil

Student thesis: Phd


Firms or organisations implement performance assessment to improve productivity but evaluating the performance of firms or organisations may be complex and complicated due to the existence of conflicting objectives. Data Envelopment Analysis (DEA) is a non-parametric approach utilized to evaluate the relative efficiencies of decision making units (DMUs) within firms or organizations that perform similar tasks. Although DEA measures the relative efficiency of a set of DMUs the efficiency scores generated do not consider the decision maker's (DM's) or expert preferences. DEA is used to measure efficiency and can be extended to include DM's and expert preferences by incorporating value judgements. Value judgements can be implemented by two techniques: weight restrictions or constructing an equivalence Multiple Objective Linear Programming (MOLP) model. Weight restrictions require prior knowledge to be provided by the DM and moreover the DM cannot interfere during the assessment analysis. On the other hand, the second approach enables the DM to interfere during performance assessment without prior knowledge whilst providing alternative objectives that allow the DM to reach the most preferred decision subject to available resources.The main focus of this research was to establish interactive frameworks to allow the DM to set targets, according to his preferences, and to test alternatives that can realistically be measured through an interactive procedure. These frameworks are based on building an equivalence model between extended DEA and MOLP minimax formulation incorporating an interactive procedure. In this study two frameworks were established. The first is based on an equivalence model between DEA trade-off approach and MOLP minimax formulation which allows for incorporating DM's and expert preferences. The second is based on an equivalence model between DEA bounded model and MOLP minimax formulation. This allows for integrating DM's preferences through interactive steps to measure the whole efficiency score (i.e. best and worst efficiency) of individual DMU. In both approaches a gradient projection interactive approach is implemented to estimate, regionally, the most preferred solution along the efficient frontier. The second framework was further extended by including ranking based on the geometric average. All the frameworks developed and presented were tested through implementation on two real case studies.
Date of Award1 Aug 2014
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorJian-Bo Yang (Supervisor) & Dong Xu (Supervisor)


  • Data Envelopment Analysis (DEA), Multiple Objectives Optimization (MOLP)

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