D. Diakoulaki

Determining objective weights in multiple criteria problems: the critic method

Abstract The association of weights in multiple criteria problems is a critical stage of the whole decision making process. In some decision situations the extraction of subjective preferences is either difficult or undesirable. This paper proposes a method for the determination of objective weights which is based on the quantification of two fundamental notions of MCDM: the contrast intensity and the conflicting character of the evaluation criteria. The latter notion is of great importance in interfirm comparisons because the financial indices used are often highly correlated. The method developed is applied to a sample of industrial firms. The results are compared to those obtained by other sets of objective weights and show this method ensures a better compromise of the criteria examined.

Design and implementation of a group DSS for sustaining renewable energies exploitation

The large scale exploitation of Renewable Energy Sources (RES) has to be based on a completely different conception of the energy planning procedure. The shift towards small-scale and dispersed units in the energy system brings up significant difficulties in the problems analysis and in the decision making process. This paper presents the basic structural characteristics of a group DSS designed for assisting decision makers in the promotion of RES. Specific attention is paid to the description of the decision making procedure which basically consists in the multicriteria analysis of alternative RES penetration scenarios by means of the PROMETHEE II outranking method. Scenarios are evaluated by a group of actors directly or indirectly involved in energy decisions which arrive, through a systematic negotiations procedure, at a wide consensus. Results from a real-world application of the DSS in Greece are presented and conclusions about the systems effectiveness are drawn.

A branch and bound algorithm for mixed zero-one multiple objective linear programming

Abstract In this paper, a branch and bound algorithm for the generation of the efficient set in mixed zero-one multiple objective linear programming problems is presented. The algorithm is developed as to take account of the multiple objectives in the node fathoming procedure. In order to extend the algorithms applicability to large sized problems from real life, an interactive procedure is introduced which systematically reduces the number of efficient points and thus saves considerable computational effort without losing essential information. The algorithm is tested in randomly generated problems along with a case study conceming the power generation sector

Multicriteria analysis vs. externalities assessment for the comparative evaluation of electricity generation systems

Attributing costs to the environmental impacts of energy production and use could greatly assist in environmental planning because monetary values are capable of directly influencing energy decisions without changing the rules of the market. Despite the considerable progress made recently in the valuation of environmental externalities there is still an open debate about the reliability of the estimated damage costs. This paper is aimed at comparing the external costs calculated for a number of power plants using different energy sources with the outcome of a multicriteria analysis where environmental impacts are expressed in physical terms or on a qualitative scale. Similarities and disparities in the obtained rankings are identified and clarified on the basis of the fundamental principles of the two approaches. It is concluded that, although external costs do not accurately reflect the traditional value system of individual decision makers, they give suitable price signals and thus help in eliminating distortions of the current energy market.

Energy planning in buildings under uncertainty in fuel costs: The case of a hotel unit in Greece

Energy planning for individual large energy consumers becomes increasingly important due to several supply options competing and/or complementing each other and the high uncertainty associated with fuel prices. Hotel units are among the largest energy consumers in the building sector, where energy planning may greatly facilitate investment decisions for efficiently meeting energy demand. The present paper presents a linear programming model, including both continuous and integer variables, which represent energy flows and discrete energy technologies, respectively. Furthermore, the model comprises fuzzy parameters in order to handle adequately the uncertainties regarding energy costs. The obtained fuzzy linear programming model is then translated into the equivalent multiple objective linear programming model, which provides a set of efficient solutions, each one characterized by quantification of the risk associated with the uncertain energy costs. The proposed methodology is illustrated with a case study referring to a large hotel unit located nearby Athens.

Combination of geographical information system and efficient routing algorithms for real life distribution operations

Abstract This paper presents a decision support system (DSS) employing a metaheuristic algorithm called BoneRoute, for solving the open vehicle routing problem (OVRP). The OVRP deals with the problem of finding a set of vehicle routes, for a fleet of capacitated vehicles to satisfy the delivery requirements of customers, without returning to the distribution centre. The computational performance of the BoneRoute algorithm for the OVRP was found to be very efficient, producing new best solutions over a set of well-known published case studies examined. Technical and managerial issues aroused from the ad hoc connections between the geographical information system (GIS), the routing technique used for calculating shortest paths and the BoneRoute algorithm for finding the optimal sequence of customers, were faced successfully.

Project prioritization under policy restrictions. A combination of MCDA with 0-1 programming

Abstract Multiple Criteria Decision Aid methods are increasingly used in financial decision making in order to capture the multifaceted character of modern enterprises activated in a complex and versatile market environment. This paper presents a multiple criteria approach for the selection of firms applying for financial support from public funds. Besides the budget constraint, the specific decision situation imposes the consideration of additional policy constraints that prevent from directly exploiting rankings provided by a multiple criteria method. In such a case the problem solution is to find a set of alternatives satisfying the constraints and at the same time maximizing a measure of global performance. The proposed procedure relies on the PROMETHEE V method which belongs to the well-known PROMETHEE family of multiple criteria outranking methods and is combined with an integer programming formulation capable to effectively deal with the problem’s combinatorial character. This method is modified in order to avoid any bias in the selection of the optimal set that may arrive because of the apparent contradiction between the rate of resources consumption and the coefficients of the alternatives in the additive objective function.

Selection among ranked projects under segmentation, policy and logical constraints

Abstract The paper proposes a method for project selection under a specific decision situation, where a final selection is guided by two aspects: (1) satisfaction of certain segmentation, policy and/or logical constraints, and (2) assurance that the individual evaluation of the projects is respected to the maximum degree. This approach is somewhat different than the usual portfolio optimization, where combinations of projects are compared without special concern on respecting the project’s ranking. The entire process is implemented in two phases: the projects are first ranked, usually through a multicriteria approach. The obtained complete preorder of the projects is then used in an integer programming module in order to effectively drive the final selection that satisfies the segmentation and/or logical constraints. The innovative part of the proposed approach is the way it overcomes the well-known bias towards low cost projects which is caused by the knapsack formulation commonly used in the integer programming phase. Actually this is the main source of divergence between the final selection and the initial complete preorder of the projects. The proposed method improves an agreement between the final selection of projects obtained from the integer programming model and the ranking obtained from the multicriteria approach.

MCDA and Energy Planning

The growing environmental awareness and the apparent conflict between economic and environmental objectives was the main impetus that pushed energy planners during the early eighties towards the use of MCDA methods. Thereafter, the rapid changes and the increasing complexity of the energy market gave rise to further methodological developments. Although the energy market restructuring and ongoing liberalization seemed to restrict the purpose for centralized energy decisions, they added new dimensions in energy planning. Increasing competition along with the prerequisite for sustainability have broadened the energy application field by bringing out new challenges for the development of integrated multicriteria and multi-stakeholders approaches also taking uncertainty into consideration. This paper aims at illustrating the evolution of MCDA approaches, in the context of the emerging problems faced by energy planners and other stakeholders involved in energy-related decision situations, one of the most active and exciting areas of application of MCDA models and methods.

Multi-criteria branch and bound: A vector maximization algorithm for Mixed 0-1 Multiple Objective Linear Programming

The paper describes the Multi-Criteria Branch and Bound (MCBB) algorithm, a vector maximization algorithm capable of deriving all efficient extreme points, for small- and medium-sized Mixed 0-1 Multiple Objective Linear Programming (Mixed 0-1 MOLP). Particular emphasis is given to computational aspects aiming principally at accelerating the solution procedure. For facilitating the decision makers search toward the most preferred efficient solution, the notion of efficient combinations of the binary variables is further exploited. It is also shown that the MCBB algorithm can be used in single objective problems (Mixed Integer LP problems) in order to determine all alternative optima, as well as in Mixed Integer MOLP problems and Pure 0-1 MOLP problems that frequently arise in practice. A computational experiment is included in the paper in order to illustrate the performance of the algorithm.