An outranking-based general approach to solving group multi-objective optimization problems

This paper presents a general approach to solving multi-objective programming problems with multiple decision makers. The proposal is based on optimizing a bi-objective measure of “collective satisfaction”. Group satisfaction is understood as a reasonable balance between the strengths of an agreeing and an opposing coalition, considering also the number of decision makers not belonging to any of these coalitions. Accepting the vagueness of “collective satisfaction”, even the vagueness of “person satisfaction”, fuzzy outranking relations and other fuzzy logic models are used.     

The affect heuristic

This paper introduces a theoretical framework that describes the importance of affect in guiding judgments and decisions. As used here, “affect” means the specific quality of “goodness” or “badness” (i) experienced as a feeling state (with or without consciousness) and (ii) demarcating a positive or negative quality of a stimulus. Affective responses occur rapidly and automatically—note how quickly you sense the feelings associated with the stimulus word “treasure” or the word “hate”. We argue that reliance on such feelings can be characterized as “the affect heuristic”. In this paper we trace the development of the affect heuristic across a variety of research paths followed by ourselves and many others. We also discuss some of the important practical implications resulting from ways that this heuristic impacts our daily lives.     

Mean–range based distribution-free procedures to minimize “overage” and “underage” costs

We discuss several mean–range based distribution-free decision procedures to minimize several “overage” and “underage” cost functions. For a general cost function, we identify the most favorable distribution and the least favorable distribution associated with the random variable of interest and determine the upper and lower bounds for the cost function. For the quadratic cost function, we recommend the min–max distribution-free decision. For the linear cost function, we identify the range of potential optimal decisions and recommend a hybrid distribution-free decision that has several desirable properties. We provide several numerical examples to demonstrate the robustness of the proposed distribution-free decisions.     

Comparison of first aggregation and last aggregation in fuzzy group TOPSIS

The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), one of the major multi attribute decision making (MADM) techniques, ranks the alternatives according to their distances from the ideal and the negative ideal solution. In real evaluation and decision making problems, it is vital to involve several people and experts from different functional areas in decision making process. Also under many conditions, crisp data are inadequate to model real-life situations, since human judgments including preferences are often vague and cannot estimate his preference with an exact numerical value. Therefore aggregation of fuzzy concept, group decision making and TOPSIS methods that we denote “fuzzy group TOPSIS” is more practical than original TOPSIS.     

Integrated analytical hierarch process and mathematical programming to supplier selection problem with quantity discount

In this article an integration of analytical hierarchy process and non-linear integer and multi-objective programming under some constraints such as quantity discounts, capacity, and budget is applied to determine the best suppliers and to place the optimal order quantities among them. This integration-based multi-criteria decision making methodology takes into account both qualitative and quantitative factors in supplier selection. While the analytical hierarchy process matches item characteristics with supplier characteristics, non-linear integer programming model analytically determines the best suppliers and the optimal order quantities among the determined suppliers. The objectives of the mathematical models constructed are maximizing the total value of purchase (TVP), minimizing the total cost of purchase (TCP) or maximizing TVP and minimizing TCP simultaneously. In addition, several “what if” scenarios are facilitated and the quality of the resulting models is evaluated on real-life data.     

Strategic and operational decisions in restaurant revenue management

The paper addresses restaurant revenue management from both a strategic and an operational point of view. Strategic decisions in restaurants are mainly related to defining the most profitable combination of tables that will constitute the restaurant. We propose new formulations of the so-called “Tables Mix Problem” by taking into account several features of the real setting. We compare the proposed models in a computational study showing that restaurants, with the capacity of managing tables as renewable resources and of combining different-sized tables, can improve expected revenue performances. Operational decisions are mainly concerned with the more profitable assignment of tables to customers. Indeed, the “Parties Mix Problem” consists of deciding on accepting or denying a booking request from different groups of customers, with the aim of maximizing the total expected revenue. A dynamic formulation of the “Parties Mix Problem” is presented together with a linear programming approximation, whose solutions can be used to define capacity control policies based on booking limits and bid prices. Computational results compare the proposed policies and show that they lead to higher revenues than the traditional strategies used to support decision makers.     

Optimal product design using a colony of virtual ants

The optimal product design problem, where the “best” mix of product features are formulated into an ideal offering, is formulated using ant colony optimization (ACO). Here, algorithms based on the behavior of social insects are applied to a consumer decision model designed to guide new product decisions and to allow planning and evaluation of product offering scenarios.     

Regret aversion and annuity risk in defined contribution pension plans

The high value of the implicit option to choose a retirement date at which interest rates are particularly high and life annuities relatively cheap, leads to the possibility to introduce regret aversion in the retirement investment decision of defined contribution plan participants. As a remedy for regret aversion in retirement investment decisions, this paper develops and prices a lookback option on a life annuity contract. We determine a closed-form option value under the restriction that the option holder invests risklessly during the time to maturity of the option and without the guarantee that the exact amount of retirement wealth is converted into a life annuity at retirement. Thereafter the investment restriction is relaxed and the guarantee of exact conversion is imposed and the option is priced via Monte Carlo simulations in an economic environment with a stochastic discount factor. Option price sensitivities are determined via the pricing of alternative options. We find that the price of a lookback option, with a maturity of three years, amounts to 8%–9% of the wealth at the option issuance date. The option price is highly sensitive to the exercise price of the option, i.e. pricing alternative options (e.g. Asian) substantially lowers the price. Time to maturity and interest rate volatility are other important option price drivers. Asset allocation decisions and initial interest rates hardly affect the option price.     

Extension of VIKOR method for decision making problem based on hesitant fuzzy set

The multiple criteria decision making (MCDM) methods VIKOR and TOPSIS are all based on an aggregating function representing “closeness to the ideal”, which originated in the compromise programming method. The VIKOR method of compromise ranking determines a compromise solution, providing a maximum “group utility” for the “majority” and a minimum of an “individual regret” for the “opponent”, which is an effective tool in multi-criteria decision making, particularly in a situation where the decision maker is not able, or does not know to express his/her preference at the beginning of system design. The TOPSIS method determines a solution with the shortest distance to the ideal solution and the greatest distance from the negative-ideal solution, but it does not consider the relative importance of these distances. And, the hesitant fuzzy set is a very useful tool to deal with uncertainty, which can be accurately and perfectly described in terms of the opinions of decision makers. In this paper, we develop the E-VIKOR method and TOPSIS method to solve the MCDM problems with hesitant fuzzy set information. Firstly, the hesitant fuzzy set information and corresponding concepts are described, and the basic essential of the VIKOR method is introduced. Then, the problem on multiple attribute decision marking is described, and the principles and steps of the proposed E-VIKOR method and TOPSIS method are presented. Finally, a numerical example illustrates an application of the E-VIKOR method, and the result by the TOPSIS method is compared.     

Revealing doctors’ prescribing choice dimensions with multivariate tools: A perceptual mapping approach

This article explores the use of multivariate techniques to build perceptual maps that show the perceived competitive positions of a set of ethical drugs. The data were drawn from a commercial panel of 283 family doctors working for the UK National Health Service. Factor analysis was applied to identify the main dimensions used by doctors to perceive and evaluate antihypertensive brands. Perceptual maps showing the competitive positions of 14 antihypertensives were produced using the factor scores of the medicines. Multiple regression analysis enabled the relative importance of each perceptual dimension to be determined. The results reveal that doctors use a small number of perceptual dimensions to evaluate competitive antihypertensive drugs, namely: “medical support”, “long term efficacy”, and the “additional beneficial effects” provided by the brand. The implications for marketing managers in the ethical pharmaceutical industry are explained and ways in which the perceptual analysis can be used to guide strategic marketing decisions considered.     

Meta-data envelopment analysis: Finding a direction towards marginal profit maximization

This paper discusses a new meta-DEA approach to solve the problem of choosing direction vectors when estimating the directional distance function. The proposed model emphasizes finding the “direction” for productivity improvement rather than estimating the “score” of efficiency; focusing on “planning” over “evaluation”. In fact, the direction towards marginal profit maximization implies a step-by-step improvement and “wait-and-see” decision process, which is more consistent with the practical decision-making process. An empirical study of U.S. coal-fired power plants operating in 2011 validates the proposed model. The results show that the efficiency measure using the proposed direction is consistent with all other indices with the exception of the direction towards the profit-maximized benchmark. We conclude that the marginal profit maximization is a useful guide for determining direction in the directional distance function.     

From decision theory to decision aiding methodology

The paper presents the author’s partial and personal historical reconstruction of how decision theory is evolving to a decision aiding methodology. The presentation shows mainly how “alternative” approaches to classic decision theory evolved. In the paper it is claimed that all such decision “theories” share a common methodological feature, which is the use of formal and abstract languages as well as of a model of rationality. Different decision aiding approaches can thus be defined, depending on the origin of the model of rationality used in the decision aiding process. The concept of decision aiding process is then introduced and analysed. The paper’s ultimate claim is that all such decision aiding approaches can be seen as part of a decision aiding methodology.     

To reveal or not to reveal? Strategic disclosure of private information in negotiation

Within the bargaining literature, it is widely held that negotiators should never reveal information that will lead to disclosure of their reservation prices. We analyze a simple bargaining and search model in which the informed buyer can choose to reveal his cost of searching for an outside price (which determines his reservation price) to the uninformed seller. We demonstrate that buyers can be made better off by revealing their search cost. More interestingly, we also find that, depending on the assumed distribution of search costs, sometimes buyers with relatively low search costs should reveal their private information whereas in other cases buyers with relatively high search costs should do so. We then test our model experimentally and find that subjects’ behavior is not entirely consistent with theoretical predictions. In general, bargainers’ behavior is better explained by a bounded rationality model similar to “fictitious play”.     

Decision principles derived from risk measures

In this paper, we argue that a distinction exists between risk measures and decision principles. Though both are functionals assigning a real number to a random variable, we think there is a hierarchy between the two concepts. Risk measures operate on the first “level”, quantifying the risk in the situation under consideration, while decision principles operate on the second “level”, often being derived from the risk measure. We illustrate this distinction with several canonical examples of economic situations encountered in insurance and finance.Special attention is paid to the role of axiomatic characterizations in determining risk measures and decision principles. Some new axiomatic characterizations of families of risk measures and decision principles are also presented.     

Safety and waste considerations in donated blood screening

We study an important problem faced by Blood Centers, of selecting screening tests for donated blood to reduce the risk of “transfusion-transmitted infectious diseases” (TTIs), including the human immunodeficiency virus (HIV), hepatitis viruses, human T-cell lymphotropic virus, syphilis, West Nile Virus, and Chagas’ Disease. This decision has a significant impact on health care quality in both developed and developing countries. The budget-constrained decision-maker needs to construct a portfolio of screening tests, from a set of available tests, each with given efficacy (sensitivity and specificity) and cost, to administer to each unit of donated blood so as to minimize the “risk” of a TTI for blood classified as “infection-free.” While doing this, it is critical, for a viable blood system, that the decision-maker does not falsely (i.e., through screening error) discard too much of the infection-free blood (“waste”). We construct mathematical models of this decision problem, considering the various objective functions (minimization of the TTI risk or the weighted TTI risk) and various constraints (on budget and wasted blood) relevant in practice. Our work generates insights on the test selection problem. We show, for example, that a reduction in risk does not necessarily come at the expense of an increase in waste. This underscores the importance of considering these different metrics in decision-making through an optimization-based model. Our work also highlights the importance of generating region-specific testing schemes that explicitly take into account the regional prevalence and co-infection rates, along with the impacts of the infections on the society and individuals.     

Applications of bulk queues to group testing models with incomplete identification

A population of items is said to be “group-testable”, (i) if the items can be classified as “good” and “bad”, and (ii) if it is possible to carry out a simultaneous test on a batch of items with two possible outcomes: “Success” (indicating that all items in the batch are good) or “failure” (indicating a contaminated batch). In this paper, we assume that the items to be tested arrive at the group-testing centre according to a Poisson process and are served (i.e., group-tested) in batches by one server. The service time distribution is general but it depends on the batch size being tested. These assumptions give rise to the bulk queueing model M/G^(m,M)/1, where m and M(>m) are the decision variables where each batch size can be between m and M. We develop the generating function for the steady-state probabilities of the embedded Markov chain. We then consider a more realistic finite state version of the problem where the testing centre has a finite capacity and present an expected profit objective function. We compute the optimal values of the decision variables (m, M) that maximize the expected profit. For a special case of the problem, we determine the optimal decision explicitly in terms of the Lambert function.     

Fuzzy linear programming models for NPD using a four-phase QFD activity process based on the means-end chain concept

Quality function deployment (QFD) is a customer-driven approach in processing new product development (NPD) to maximize customer satisfaction. Determining the fulfillment levels of the “hows”, including design requirements (DRs), part characteristics (PCs), process parameters (PPs) and production requirements (PRs), is an important decision problem during the four-phase QFD activity process for new product development. Unlike previous studies, which have only focused on determining DRs, this paper considers the close link between the four phases using the means-end chain (MEC) concept to build up a set of fuzzy linear programming models to determine the contribution levels of each “how” for customer satisfaction. In addition, to tackle the risk problem in NPD processes, this paper incorporates risk analysis, which is treated as the constraint in the models, into the QFD process. To deal with the vague nature of product development processes, fuzzy approaches are used for both QFD and risk analysis. A numerical example is used to demonstrate the applicability of the proposed model.     

Competitive dynamics of e-commerce web sites

The characteristic of “rich gets richer” is much more conspicuous on the Internet, which is the important cause of “power-law” characteristic of the “winner-takes-all” markets. A competitive model of e-commerce web sites is presented in order to explore the effects of competition and the characteristic of “rich gets richer” on Internet economy, and also to investigate how the “winner-takes-all” phenomenon comes forth. Then these ordinary differential equations are qualitatively analyzed and numerically simulated to study the dynamics of competition. The effects of parameters on the dynamic behavior in the model are also discussed.     

规模效应下产品具有可替代性的竞争企业的外包决策

针对产品具有一定替代性的两个竞争企业(分别为企业1和企业2)和存在规模效应的上游供应商的外包决策问题, 构建了企业1 外包前后各方的利润模型,求解了下游企业的外包和自产的最优策略以及供应商的最佳批发价格,分析了企业1 的外包策略对企业2 和供应商的外包决策的影响,比较了产品替代性对外包前后各决策变量的影响。研究发现:当企业的单位生产成本高于外包成本时,企业也可能选择自产;而当企业的单位生产成本低于外包成本时,企业也可能选择产品外包。并对模型进行进一步的拓展,比较了下游企业作顺序和同时外包决策两种情景的异同。     

Average optimality for continuous-time Markov decision processes with a policy iteration approach

This paper deals with the average expected reward criterion for continuous-time Markov decision processes in general state and action spaces. The transition rates of underlying continuous-time jump Markov processes are allowed to be unbounded, and the reward rates may have neither upper nor lower bounds. We give conditions on the system's primitive data and under which we prove the existence of the average reward optimality equation and an average optimal stationary policy. Also, under our conditions we ensure the existence of ?-average optimal stationary policies. Moreover, we study some properties of average optimal stationary policies. We not only establish another average optimality equation on an average optimal stationary policy, but also present an interesting “martingale characterization” of such a policy. The approach provided in this paper is based on the policy iteration algorithm. It should be noted that our way is rather different from both the usually “vanishing discounting factor approach” and the “optimality inequality approach” widely used in the previous literature.