Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Portfolio decision analysis for robust project selection and resource allocation

Organizations must take decisions on how to allocate resources to 'go/no-go' projects to maximize the value of their project portfolio. Often these decisions are complicated by several value criteria, multiple resource types and exogenous uncertainties that influence the projects' values. Especially when the number of projects is large, the efficiency of the resource allocation and the quality of the decision making process are likely to benefit from systematic use of portfolio decision analysis. This Dissertation develops and applies novel methods to manage uncertainty in decision analytic models for project portfolio selection. These methods capture incomplete information through sets of feasible model parameter values and use dominance relations to compare portfolios. Based on the computation of all non-dominated portfolios, these methods identify i) robust portfolios that perform well across the range of feasible parameter values and ii) projects that should surely be selected or rejected in the light of the incomplete information. These methods have several implications for project portfolio decision support. Explicit consideration of incomplete information contributes to the reliability of analysis, which is likely to increase the use of portfolio decision analysis in new contexts. Furthermore, cost and time savings in data elicitation may be achieved, because these methods can give robust decision recommendations based on incomplete data and identify projects for which additional information is beneficial. Finally, these methods support consensus building within organizations as different views about projects' quality or exogenous uncertainties can be considered simultaneously to identify projects on which further negotiations should be focused

A variable neighborhood search simheuristic for project portfolio selection under uncertainty

With limited nancial resources, decision-makers in rms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash ows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases

A robust fuzzy possibilistic AHP approach for partner selection in international strategic alliance

The international strategic alliance is an inevitable solution for making competitive advantage and reducing the risk in today’s business environment. Partner selection is an important part in success of partnerships, and meanwhile it is a complicated decision because of various dimensions of the problem and inherent conflicts of stockholders. The purpose of this paper is to provide a practical approach to the problem of partner selection in international strategic alliances, which fulfills the gap between theories of inter-organizational relationships and quantitative models. Thus, a novel Robust Fuzzy Possibilistic AHP approach is proposed for combining the benefits of two complementary theories of inter-organizational relationships named, (1) Resource-based view, and (2) Transaction-cost theory and considering Fit theory as the perquisite of alliance success. The Robust Fuzzy Possibilistic AHP approach is a noveldevelopment of Interval-AHP technique employing robust formulation; aimed at handling the ambiguity of the problem and let the use of intervals as pairwise judgments. The proposed approach was compared with existing approaches, and the results show that it provides the best quality solutions in terms of minimum error degree. Moreover, the framework implemented in a case study and its applicability were discussed

Case: Peatland Selection

The importance of environmental decision making is growing. Private companies and public organizations are facing decisions involving multiple objectives. In particular, focusing solely on financial objectives is no longer enough but taking into account the environmental, social and political objectives is needed. The methods used to solve these environmental problems have been based on heuristic approaches. However, these methods lack the capability to provide optimal solutions as most of the environmental decisions are portfolio selection problems. Robust Portfolio Modeling (RPM) is a decision analysis method that combines mathematical optimization in portfolio selection to incomplete preference information. This incomplete information is common in environmental decision making which includes multiple stakeholders with conflicting views. However, RPM has not been applied before to real-life environmental cases. This thesis will first explore the characteristics of environmental decision making, secondly go through different methods used in environmental decision making and finally apply RPM methodology into peatland selection case. The results of RPM are then compared to the results of the heuristic YODA method previously used in the same peatland selection case. Results indicate that RPM and YODA select highly different type of peatlands. RPM takes better into account the cumulative effects related to portfolio selection than YODA. Therefore, it is argued that RPM might be suitable for environmental decision making

Portfolio Selection in Multidimensional General and Partial Moment Space.

This paper develops a general approach for the single period portfolio optimization problem in a multidimensional general and partial moment space. A shortage function is defined that looks for possible increases in odd moments and decreases in even moments. A main result is that this shortage function ensures suffcient conditions for global optimality. It also forms a natural basis for developing tests on the infuence of additional moments. Furthermore, a link is made with an approximation of an arbitrary order of a general indirectutility function. This nonparametric effciency measurement framework permits to dfferentiate mainly between portfolio effciency and allocative effciency. Finally, information can,in principle, be inferred about the revealed risk aversion, prudence, temperance and otherhigher-order risk characteristics of investors.shortage function, efficient frontier, K-moment portfolios

Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection

A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer programming problems