Decision-making on prioritization of projects in higher education institutions using the analytic network process approach

The prioritization of projects in higher education institutions is a complex decision-making problem. In this paper we deal with two scenarios within Higher Education Institutions. The first scenario is a need to prepare an action plan for activities that will result in the implementation of a portfolio of projects at the institutional level. The second scenario is making a decision on whether to start a new project application, and if so, which project to choose in a situation where project teams have several project ideas and limited resources. The purpose of the paper is to show how to include corporate strategy in the decision-making process and use the Analytic Network Process as a multiple criteria decision-making methodology which can be used in solving project selection problems.     

A new decision support model in multi-criteria decision making with intuitionistic fuzzy sets based on risk preferences and criteria reduction

In this paper, we propose a new model for decision support to address the ‘large decision table’ (eg, many criteria) challenge in intuitionistic fuzzy sets (IFSs) multi-criteria decision-making (MCDM) problems. This new model involves risk preferences of decision makers (DMs) based on the prospect theory and criteria reduction. First, we build three relationship models based on different types of DMs’ risk preferences. By building different discernibility matrices according to relationship models, we find useful criteria for IFS MCDM problems. Second, we propose a technique to obtain weights through discernibility matrix. Third, we also propose a new method to rank and select the most desirable choice(s) according to weighted combinatorial advantage values of alternatives. Finally, we use a realistic voting example to demonstrate the practicality and effectiveness of the proposed method and construct a new decision support model for IFS MCDM problems.     

An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation

Supplier selection problem, considered as a multi-criteria decision-making (MCDM) problem, is one of the most important issues for firms. Lots of literatures about it have been emitted since 1960s. However, research on supplier selection under operational risks is limited. What’s more, the criteria used by most of them are independent, which usually does not correspond with the real world. Although the analytic network process (ANP) has been proposed to deal with the problems above, several problems make the method impractical. This study first integrates the fuzzy cognitive map (FCM) and fuzzy soft set model for solving the supplier selection problem. This method not only considers the dependent and feedback effect among criteria, but also considers the uncertainties on decision making process. Finally, a case study of supplier selection considering risk factors is given to demonstrate the proposed method’s effectiveness.     

基于实验设计的概率语言权重信息多准则决策方法

作为模糊语言集的一种,概率语言集能够反映决策问题中评价准则权重重要程度及其概率,这在决策问题中较为实用。本文运用实验设计(Design of Experiment,DOE)的方法,对权重信息以概率语言给出的多准则决策问题进行研究:通过多次模拟权重重要程度及其概率,设计出多组实验权重,并用GRA法求得每组实验数据的得分,获得描述该决策问题准则值与评价结果相互影响关系的最适用回归方程,将决策问题的实际数据带入方程进行运算,得出方案排序结果。通过快速成型过程、柔性制造系统过程两个案例,进行对比验证。结果表明所提出的方法能较好地处理基于概率语言权重信息的多准则决策问题,计算简单且速度快,可重复性强且颇为实用。     

A fuzzy multidimensional multiple-choice knapsack model for project portfolio selection using an evolutionary algorithm

Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems with precise data. However, the project data in real-world problems are often imprecise or ambiguous. We propose a fuzzy Multidimensional Multiple-choice Knapsack Problem (MMKP) formulation for project portfolio selection. The proposed model is composed of an Efficient Epsilon-Constraint (EEC) method and a customized multi-objective evolutionary algorithm. A Data Envelopment Analysis (DEA) model is used to prune the generated solutions into a limited and manageable set of implementable alternatives. Statistical analysis is performed to investigate the effectiveness of the proposed approach in comparison with the competing methods in the literature. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.     

A new multi-criteria approach dealing with dependent and heterogeneous criteria for end-of-life product strategy

Strict regulations, technological growth and environmental impacts are influent factors in the choice of the best strategy for end-of-life (EOL) product. In fact, EOL is one stage of the life cycle having gained the attention of the market.This study proposes an integrated methodology that relies on multi-criteria analysis and takes into account both quantitative and qualitative criteria while respecting mutual interactions between them. This has been performed by introducing, on the one hand, the 2-tuple linguistic representation model dealing with non-homogeneous information data, and on the other hand, the Choquet integral interaction modeling between criteria into the PROMETHEE method. Although it has been developed for EOL selection problems, the proposed method fits to all kinds of decision-making problems with heterogeneous information. As this work demonstrates, this multi-criteria analysis approach can offer a technical-scientific decision making support tool in the EOL product sector. We also perform a sensitivity to reveal the effect of the subjective parameter variations on the originally resulted ranking.     

Robust portfolio modeling with incomplete cost information and project interdependencies

Robust portfolio modeling (RPM) [Liesiö, J., Mild, P., Salo, A., 2007. Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research 181, 1488–1505] supports project portfolio selection in the presence of multiple evaluation criteria and incomplete information. In this paper, we extend RPM to account for project interdependencies, incomplete cost information and variable budget levels. These extensions lead to a multi-objective zero-one linear programming problem with interval-valued objective function coefficients for which all non-dominated solutions are determined by a tailored algorithm. The extended RPM framework permits more comprehensive modeling of portfolio problems and provides support for advanced benefit–cost analyses. It retains the key features of RPM by providing robust project and portfolio recommendations and by identifying projects on which further attention should be focused. The extended framework is illustrated with an example on product release planning.     

Multi-criteria decision making approaches for supplier evaluation and selection: A literature review

Supplier evaluation and selection problem has been studied extensively. Various decision making approaches have been proposed to tackle the problem. In contemporary supply chain management, the performance of potential suppliers is evaluated against multiple criteria rather than considering a single factor-cost. This paper reviews the literature of the multi-criteria decision making approaches for supplier evaluation and selection. Related articles appearing in the international journals from 2000 to 2008 are gathered and analyzed so that the following three questions can be answered: (i) Which approaches were prevalently applied? (ii) Which evaluating criteria were paid more attention to? (iii) Is there any inadequacy of the approaches? Based on the inadequacy, if any, some improvements and possible future work are recommended. This research not only provides evidence that the multi-criteria decision making approaches are better than the traditional cost-based approach, but also aids the researchers and decision makers in applying the approaches effectively.     

IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection

A fundamental principle of modern portfolio theory is that comparisons between portfolios are generally made using two criteria, corresponding to the first two moments of return distributions, namely the expected return and portfolio variance. According to this model and according to most of the portfolio models derived from the stochastic dominance approach, the group of portfolios open to comparisons is divided into two parts: on the one hand there are the efficient portfolios (those that are not dominated by any other portfolio in the group), and on the other, those that are dominated. In other words, these models do not solve for one optimal portfolio, but rather solve for an efficient set of portfolios, among which the investor must choose, given his preference system. One criticism over these models, which has often been addressed both by practitioners and academics, is that they fail to embody the objectives of the decision maker (DM), through the various stages of the decision process. Our purpose in this article is to present an integrated and innovative methodological approach for the construction and selection of equity portfolios, which will take into account the inherent multidimensional nature of the problem, while allowing the DM to incorporate his preferences in the decision process. The proposed approach, which grounds its basis on the field of multiple criteria decision making (MCDM) and more specifically on multiobjective mathematical programming (MMP), is implemented in the IPSSIS (Integrated Portfolio Synthesis and Selection Information System) decision support system (DSS). The validity of the proposed approach is tested through an illustrative application in the Athens Stock Exchange (ASE).     

带有模糊系数的投资组合模型研究

在证券市场,由于各种不确定因素的存在,证券的预期收益率是难以精确估算的。本文采用模糊数来处理不确定性,提出了一种基于模糊收益率的投资组合模型。为度量投资组合的风险,将绝对偏差扩展到模糊情形。通过引入模糊数绝对值的概念和不等关系的两种占优准则,将该模型转化为相应的确定性线性规划问题,投资者可根据自己的主观态度选择参数和投资策略。最后用一个具体例子验证了模型的合理性和有效性。     

Evaluating and selecting the supplier in detergent production industry using hierarchical fuzzy TOPSIS

Supply chain management has increasingly attracted attention as a systematic approach to integrate the supply chain in order to planning and controlling the materials and information from suppliers to customers. One of the most important issues in supply chain management is selection of the appropriate supplier which has significant effect on purchasing cost decrease and increase in the organization’s competition ability. Selection of the best supplier is naturally complex with no definite structure, and dependent on the type of suppliers’ activity. In the process of decision making about suppliers and many qualitative and quantitative performance indicators such as quality, price, flexibility, and due date should be considered. Then, the supplier selection problem is a multi-criteria decision making problem where numerous methods have been proposed to solve this problem so far. In the current paper, four suppliers of imported raw material “Tripolyphosphate (TPP)” (primary material to produce the detergent powder with a case study in Iran) are evaluated based on 25 effective criteria using the hierarchical fuzzy TOPSIS (HFTOPSIS) approach.     

Pseudo-criteria versus linear utility function in stochastic multi-criteria acceptability analysis

Stochastic multi-criteria acceptability analysis (SMAA) is a multi-criteria decision support method for multiple decision-makers (DMs) in discrete problems. SMAA does not require explicit or implicit preference information from the DMs. Instead, the method is based on exploring the weight space in order to describe the valuations that would make each alternative the preferred one. Partial preference information can be represented in the weight space analysis through weight distributions. In this paper we compare two variants of the SMAA method using randomly generated test problems with 2–12 criteria and 4–12 alternatives. In the original SMAA, a utility or value function models the DMs' preference structure, and the inaccuracy or uncertainty of the criteria is represented by probability distributions. In SMAA-3, ELECTRE III-type pseudo-criteria are used instead. Both methods compute for each alternative an acceptability index measuring the variety of different valuations that supports this alternative, and a central weight vector representing the typical valuations resulting in this decision. We seek answers to three questions: (1) how similar are the results provided by the decision models, (2) what kind of systematic differences exists between the models, and (3) how could one select indifference and preference thresholds of the pseudo-criteria model to match a utility model with given probability distributions?     

An a posteriori decision support methodology for solving the multi-criteria supplier selection problem

This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.     

Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems

Risk and return are interdependent in a stock portfolio. To achieve the anticipated return, comparative risk should be considered simultaneously. However, complex investment environments and dynamic change in decision making criteria complicate forecasts of risk and return for various investment objects. Additionally, investors often fail to maximize their profits because of improper capital allocation. Although stock investment involves multi-criteria decision making (MCDM), traditional MCDM theory has two shortfalls: first, it is inappropriate for decisions that evolve with a changing environment; second, weight assignments for various criteria are often oversimplified and inconsistent with actual human thinking processes.In 1965, Rechenberg proposed evolution strategies for solving optimization problems involving real number parameters and addressed several flaws in traditional algorithms, such as their use of point search only and their high probability of falling into optimal solution area. In 1992, Hillis introduced the co-evolutionary concept that the evolution of living creatures is interactive with their environments (multi-criteria) and constantly improves the survivability of their genes, which then expedites evolutionary computation. Therefore, this research aimed to solve multi-criteria decision making problems of stock trading investment by integrating evolutionary strategies into the co-evolutionary criteria evaluation model. Since co-evolution strategies are self-calibrating, criteria evaluation can be based on changes in time and environment. Such changes not only correspond with human decision making patterns (i.e., evaluation of dynamic changes in criteria), but also address the weaknesses of multi-criteria decision making (i.e., simplified assignment of weights for various criteria).Co-evolutionary evolution strategies can identify the optimal capital portfolio and can help investors maximize their returns by optimizing the preoperational allocation of limited capital. This experimental study compared general evolution strategies with artificial neural forecast model, and found that co-evolutionary evolution strategies outperform general evolution strategies and substantially outperform artificial neural forecast models. The co-evolutionary criteria evaluation model avoids the problem of oversimplified adaptive functions adopted by general algorithms and the problem of favoring weights but failing to adaptively adjust to environmental change, which is a major limitation of traditional multi-criteria decision making. Doing so allows adaptation of various criteria in response to changes in various capital allocation chromosomes. Capital allocation chromosomes in the proposed model also adapt to various criteria and evolve in ways that resemble thinking patterns.     

Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market

We propose a way of using DEA cross-efficiency evaluation in portfolio selection. While cross efficiency is an approach developed for peer evaluation, we improve its use in portfolio selection. In addition to (average) cross-efficiency scores, we suggest to examine the variations of cross-efficiencies, and to incorporate two statistics of cross-efficiencies into the mean-variance formulation of portfolio selection. Two benefits are attained by our proposed approach. One is selection of portfolios well-diversified in terms of their performance on multiple evaluation criteria, and the other is alleviation of the so-called “ganging together” phenomenon of DEA cross-efficiency evaluation in portfolio selection. We apply the proposed approach to stock portfolio selection in the Korean stock market, and demonstrate that the proposed approach can be a promising tool for stock portfolio selection by showing that the selected portfolio yields higher risk-adjusted returns than other benchmark portfolios for a 9-year sample period from 2002 to 2011.     

Genetic algorithms for portfolio selection problems with minimum transaction lots

Conventionally, portfolio selection problems are solved with quadratic or linear programming models. However, the solutions obtained by these methods are in real numbers and difficult to implement because each asset usually has its minimum transaction lot. Methods considering minimum transaction lots were developed based on some linear portfolio optimization models. However, no study has ever investigated the minimum transaction lot problem in portfolio optimization based on Markowitz’ model, which is probably the most well-known and widely used. Based on Markowitz’ model, this study presents three possible models for portfolio selection problems with minimum transaction lots, and devises corresponding genetic algorithms to obtain the solutions. The results of the empirical study show that the portfolios obtained using the proposed algorithms are very close to the efficient frontier, indicating that the proposed method can obtain near optimal and also practically feasible solutions to the portfolio selection problem in an acceptable short time. One model that is based on a fuzzy multi-objective decision-making approach is highly recommended because of its adaptability and simplicity.     

An interactive sorting approach based on the assignment examples of multiple decision makers with different priorities

Although group decision-making is often adopted by many organizations in today??s highly complicated business environment, the multiple criteria sorting (MCS) problem in the context of group decision-making has not been studied sufficiently. To this end, we propose a new interactive method to assist a group of decision makers (DMs) with different priorities. With the goal of relieving the cognitive effort exerted by DMs, this method uses the assignment examples provided by the DMs to draw the parameters for the group preference model. In the iterative MCS process that we employ, the DMs are supported from two perspectives. When the assignment examples provided by the DMs are inconsistent, a RINCON algorithm is developed to identify all the possible solutions that the DMs can use to resolve the conflicts. When the examples are consistent, the potential and the fittest assignments of each alternative are deduced using linear programming techniques. These are then presented to the DMs to help them provide more information for the decision-making process. Furthermore, the priority of each DM is objectively and subjectively evaluated, and then progressively updated to reflect the decision-making performance of a DM at each iteration. Meanwhile, the priorities are integrated into the linear programming model to deduce the fittest assignment, as well as into the RINCON algorithm. Hence, the assignment examples of the DMs with higher priorities are emphasized in the fittest assignment, and are less likely to be revised for inconsistency. A practical example featuring MBA programs is also presented to demonstrate the proposed method.     

A multiple criteria decision-making approach for the selection of stocks

A fundamental principle of modern portfolio theory is that portfolio selectiondecisions are generally made using two criteria, corresponding to the first twomoments of return distributions, namely the expected returnportfolio variance.One criticism over this theory, which has often been addressed both bypractitioners and academics, is that it fails to embody all thedecision-maker's objectives, through the various stages of the decisionprocess. The aim of this paper is to present an alternative methodologicalapproach for modeling one of the most crucial phases of the portfolio managementprocess, the security selection phase. The main characteristic of the proposedapproach is that it fully takes into account the inherent multi-dimensionalnature of the problem, although allowing the decision-maker to incorporate hispreferences in the decision process. The validity of the proposed approach istested through an illustrative application in Athens Stock Exchange. Besides, adetailed categorized bibliography is provided, relative to the application ofthe techniques of multiple criteria decision making to the problems and issuesof portfolio management.     

A Multi-criteria Decision Support System for R&D Project Selection

This paper describes the development of a decision support system for the selection of a portfolio of R&D projects, which was carried out for a large electricity utility corporation. The DSS was constructed round a reference point approach for the underlying multi-criteria decision problem. The application of this approach did require a less usual form of scalarizing function as well as a heuristic algorithm for solving a non-linear knapsack problem. Practical aspects of the implementation of the multi-criteria approach in a DSS operating on a PC are also discussed.     

基于交互耦合网络的项目组合决策模型研究

随着项目活动进入“大尺度”时代,复杂性成为现代化项目组合管理中的突出问题。在项目组合决策系统复杂性分析基础上,提出了交互耦合网络视角下的项目组合决策系统表征方法;借鉴非线性动力学建模方法构建项目组合决策系统复杂动力网络模型,结合模型的稳定解和稳定条件将项目组合决策系统划分为竞争型、共生型、强依存型和弱依存型,并通过数值仿真方法对系统的稳定域、分岔和混沌进行分析。研究表明,项目组合决策系统的复杂性和稳定性依赖于系统内交互关系作用,改善协作关系,避免过分竞争,以系统整体为先优化配置有利于项目组合目标实现。