Using interactive multiobjective methods to solve DEA problems with value judgements,”

Abstract Data envelopment analysis (DEA) is a performance measurement tool that was initially developed without consideration of the decision maker (DM)'s preference structures. Ever since, there has been a wide literature incorporating DEA with value judgements such as the goal and target setting models. However, most of these models require prior judgements on target or weight setting. This paper will establish an equivalence model between DEA and multiple objective linear programming (MOLP) and show how a DEA problem can be solved interactively without any prior judgements by transforming it into an MOLP formulation. Various interactive multiobjective models would be used to solve DEA problems with the aid of PROMOIN, an interactive multiobjective programming software tool. The DM can then search along the efficient frontier to locate the most preferred solution where resource allocation and target levels based on the DM's value judgements can be set. An application on the efficiency analysis of retail banks in the UK is examined. Comparisons of the results among the interactive MOLP methods are investigated and recommendations on which method may best fit the data set and the DM's preferences will be made

Multi-criteria Decision Support System for Siemianowka Reservoir under Uncertainties

Protection of biodiversity against climatic variations became recently one of the most important issues in water management politics. In the case of a river system it is necessary to provide desirable water conditions for protected ecosystems. This paper presents an application of a Multiple Criteria Decision Suppport System for optimal management of a reservoir located in NE Poland in the Upper Narew Basin. The proposed system allows tradeoff between different reservoir users, including protected wetland ecosystems of the Narew Nation Park to be found. The most challenging task was to take the account of inherent uncertainties related to the model structure. It was done using stochastic formulation of the reservoir control problem. Optimisation was carried out for several criteria:wetland water requirements, agricultural, energy production, flood protection, fishery and reservoir storage. The goal was to achieve a desirable water management regime within the defined safety levels. These highly nonliniear constrains were met through the minimisation of convex functions by solving a linear programming problem within Multiple Criteria Analysis

DIAGNOSTIC ASSESSMENT AND ADVANCEMENT OF MULTI-OBJECTIVE RESERVOIR CONTROL UNDER UNCERTAINTY

This dissertation contributes to the assessment of new scientific developments for multi-objective decision support to improve multi-purpose river basin management. The main insights of this work highlight opportunities to improve modeling of complex multi-purpose water reservoir systems and opportunities to flexibly incorporate emerging demands and hydro-climatic uncertainty. Additionally, algorithm diagnostics contributed in this work enable the water resources field to better capitalize on the rapid growth in computational power. This opens new opportunities to increase the scope of the problems that can be solved and contribute to the robustness and sustainability of water systems management worldwide. This dissertation focuses on a multi-purpose reservoir system that captures the contextual and mathematical difficulties confronted in a broad range of global multi-purpose systems challenged by multiple competing demands and uncertainty. The first study demonstrates that advances in state of the art multiobjective evolutionary optimization enables to reliably and effectively find control policies that balance conflicting tradeoffs for multi-purpose reservoir control. Multiobjective evolutionary optimization techniques coupled with direct policy search can reliably and flexibly find suitable control policies that adapt to multi-sectorial water needs and to hydro-climatic uncertainty. The second study demonstrates the benefits of cooperative parallel MOEA architectures to reliably and effectively find many objective control policies when the system is subject to uncertainty and computational constraints. The more advanced cooperative, co-evolutionary parallel search expands the scope of problem difficulty that can be reliably addressed while facilitating the discovery of high quality approximations for optimal river basin tradeoffs. The insights from this chapter should enable water resources analysts to devote computational efforts towards representing reservoir systems more accurately by capturing uncertainty and multiple demands when properly using parallel coordinated search. The third study extended multi- purpose reservoir control to better capture flood protection. A risk-averse formulation contributed to the discovery of control policies that improve operations during hydrologic extremes. Overall this dissertation has carefully evaluated and advanced the Evolutionary Multiobjective Direct Policy Search (EMODPS) framework to support multi-objective and robust management of conflicting demands in complex reservoir systems

Trade-off Curves and Elasticity Analysis in Multi Fuel Options System and Combined Problem

Recent environmental concerns and policies have reformulated the traditional economic dispatch problem by including the emission impacts in the mathematical model.  The combined economic and emission dispatch problem is a multi-objective non-linear optimization problem. This paper presents a method to consider the fuel costs and environmental emissions simultaneously. The -constraint method for bi-objective optimization has been used to generate Pareto front. Furthermore, trade-off curves have been developed for different types of emission. The elasticity of cost with respect to the emission (say, emission elasticity) has been estimated for all Pareto optimal points and different types of emissions that provides invaluable information for the system operator to run the system with sufficient flexibility subject to technical constraints while the operator has multi fuel options. Moreover, the emission elasticity is effective tool for competition in the electricity market. The Iranian Electricity Market is considered as empirical evidence. Keywords: Combined economic-emission dispatch, Emission elasticity, Iranian electricity market, Multi objective optimization model, ε-Constraint. JEL Classifications:  C6, F64, P48,

Multiple Criteria Decision Making and Multiattribute Utility Theory

T his paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields

Measuring the Risk of Shortfalls in Air Force Capabilities

The U.S. Air Force seeks to measure and prioritize risk as part of its Capabilities Review and Risk Assessment (CRRA) process. The goal of the CRRA is to identify capability shortfalls, and the risks associated with those shortfalls, to influence future systems acquisition. Many fields, including engineering, medicine and finance, seek to model and measure risks. This research utilizes various risk measurement approaches to propose appropriate risk measures for a military context. Specifically, risk is modeled as a non-negative random variable of severity. Four measures are examined: simple expectation, a risk-value measure, tail conditional expectation, and distorted expectation. Risk measures are subsequently used to weight the objective function coefficients in a system acquisition knapsack problem

Multiobjective Problems of Mathematical Programming; Proceedings of an International Conference, Yalta, USSR, October 26 - November 2, 1988

IIASA's approach to research in Multiple Objective Decision Support, Multiple Criteria Optimization (MCO) and related topics assumes a high level of synergy between three main components: methodological and theoretical backgrounds, computer implementation and decision support systems and real life applications. This synergy is reflected in the subjects of papers presented at the Conference as well as in the structure of the Proceedings which is divided into three main sections. In the first section, "Theory and Methodology of Multiple Criteria Optimization," 21 papers discussing new theoretical developments in MCO are presented. The second section, "Applications of Multiple Criteria Optimization, " contains nine papers dealing with real-life applications of MCO. Five papers on the application of MCO in the development of Decision Support Systems are included in the final section, "Multiple Criteria Decision Support." Among the important outcomes of this Conference were conclusions regarding further directions of research for Multiple Criteria Optimization, in particular, in the context of cooperation between scientists from Eastern and Western countries

Interactive Multicriteria Approach to Facility Location-Allocation Models Under Stochastic Demand

Industrial Engineering and Managemen