The Stochastic Goal Programming Model: Theory and Applications

Supported by a network of researchers and practitioners, the goal programming (GP) model is alive today more than ever and is continually fed with theoretical developments and new applications with resounding success. The standard formulation of the GP model was introduced in the earliest of 1960s, and since then, important extensions and numerous applications have been proposed. One of these variants is the stochastic GP model that deals with the uncertainty of some decision‐making situations by using stochastic calculus. In such a situation, the decision maker is not able to assess with certainty the different parameters. However, he or she can provide some information regarding the likelihood of occurrence of the decision‐making parameter values. The aim of this paper is to highlight the main methodological developments of the stochastic GP model and to present an overview of its applications in several domains. Copyright © 2012 John Wiley & Sons, Ltd.     

Portfolio Selection from Multiple Benchmarks: A Goal Programming Approach to an Actual Case

This paper deals with benchmark‐based portfolio choice for buy‐and‐hold strategies of investing. Multiple benchmarks for returns are considered, which is more realistic than taking a unique benchmark – a unique aspiration difficult to select in practice among the various aspirations for returns that the investor has in mind. Portfolio selection with multiple benchmarks leads to a multi‐objective problem, which is addressed by mean value – stochastic goal programming. In particular, two benchmarks are considered, which involves two goals. Weights for goals depend on investor's preferences and Arrow's absolute risk aversion coefficients. An efficient frontier of portfolios is obtained. Advantages of this stochastic method in our context are as follows: (i) Mean value‐stochastic goal programming relies on classical utility theory under uncertainty and Arrow's absolute risk aversion, which ensures soundness and strictness, and (ii) the numerical model is easily solved by using available software such as mean–variance software. Numerical results are tabulated and discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

A Fuzzy Goal Programming Formulation with Multiple Target Levels

The essential activity of a manager is decision making, which is becoming more and more complex, mainly in the multi‐criteria problems. Multi‐choice goal programming (MCGP) is considered as a robust tool in operational research to solve this type of problem. However, in real world problems, determining precise targets for the goals is a difficult task. To deal with such situation, Tabrizi introduced and used in 2012 the concept of membership functions in the MCGP model in order to model the targets fuzziness of each goal. In their model, they considered just only one type of functions (triangular form), which does not reflect adequately the decision maker's preferences that are considered as an essential element for modelling the goal's fuzziness. Their model is called Fuzzy MCGP. In this paper, new ideas are presented to reformulate MCGP model to tackle all types of functions by introducing the (decision maker's) preferences. The concept of indifference thresholds is used in the new formulation for characterizing the imprecision and the preferences associated with all types of the goals. The proposed formulation provides useful insight about the solution of a new class of problems. A numerical example is given to demonstrate the validity and strength of the new formulation. Copyright © 2013 John Wiley & Sons, Ltd.     

Preference Structure Modelling for Multi-objective Decision Making: A Goal–Programming Approach

We propose a new approach for modelling preference structures in multi-objective decision-making (MODM) problems. The basic idea of the approach is to first develop PROMETHEE-influenced objective functions and then to use these to reformulate the problem as a distance-based goal–programming (GP) model. Three basic functional forms are proposed and explicit expressions are developed for them. Among other things, the expressions allow for the straightforward development of an interactive framework while keeping the information requirements from the decision maker (DM) at a minimum. An ‘automatic’ piecewise linear approximation scheme is proposed for solving the GP model. © 1997 by John Wiley & Sons, Ltd. J. Multi-Crit. Decis. Anal. 6 : 150–154 (1997) No. of Figures: 0. No. of Tables: 0. No. of References: 12.     

Model and interactive algorithm of bi‐level multi‐objective decision‐making with multiple interconnected decision makers

Furthering a prior research on two‐person bi‐level multi‐objective decision‐making problems of the leader‐follower Stackelberg game, we present an extended model of bi‐level multi‐objective decision‐making with multiple interconnected decision makers at the lower level. In the model, the upper level decision maker acts as a leader and the lower level decision makers behave as the followers, and inter‐connections and interactions exist among these followers in decision‐making scenarios. Following the rules of leader‐follower Stackelberg game, we develop an interactive algorithm of the model for solving multi‐objective decision‐making problems and reflecting the interactive natures among the decision makers. Finally, the authors exemplify the model and algorithm, and draw a conclusion on points of contributions and the significance of this study in decision‐making and support. Copyright © 2001 John Wiley & Sons, Ltd.     

An Approach to Multi‐Criteria Decision Problems Under Severe Uncertainty

Solving complex decision problems is a demanding task; it requires determining and evaluating the consequences of decision alternatives. To this end, uncertain factors that can only partly be influenced by the decision makers, and their interdependencies need to be considered. Scenarios focus on this part of the decision problem; they enable a systematic exploration of a multitude of possible future developments that are relevant for the decision including external events and decisions made. Scenarios are particularly useful when the problem is pervaded by severe uncertainties that cannot be quantified. For the evaluation of alternatives, multiple objectives and the potentially diverging preferences of the involved actors need to be respected. Multi‐criteria decision analysis aims at structuring the problem, evaluating the alternatives and supporting decision makers pursuing multiple goals. We propose an approach integrating scenarios and multi‐criteria decision analysis that focuses on the robustness of alternatives in complex, dynamic, uncertain and time‐bound situations. In this integrated framework, the scenarios provide the basis for evaluating a set of alternatives. Ideally, the set of scenarios considered captures all possible future developments. To appropriately explore this set, formal or analytical approaches to scenario construction generate a large number of scenarios. This challenges the decision makers' information‐processing capacity. To support them in managing the richness of information, a two‐fold approach that uses selection and aggregation is presented. By using a selection method, the scenarios that are deemed most relevant are identified, and their evaluations are presented in detail to decision makers. This approach is complemented by an aggregation of scenario evaluations on the basis of the decision makers' preferences. We present two approaches to facilitate the preference elicitation process. Copyright © 2013 John Wiley & Sons, Ltd.     

Simulation of Preference Information in an Interactive Reference Point‐Based Method for the Bi‐Objective Inventory Routing Problem

In the research area of multiple criteria decision making, very few publications exist that explicitly design the simulation of a decision maker (DM) in an interactive approach. For this reason, we outline some methods widely used in the literature to identify common assumptions of simulating the DM's responses and the required input preference information. Our paper aims at covering the identified gap by introducing experimental concepts. Such concepts are used for theoretical analyses of a combined search‐and‐decision‐making procedure. Simulating the DM is a fruitful idea because the algorithm can be tested without integrating a human decision maker. Finally, we conduct experiments based on the proposed settings for a multiobjective inventory routing problem, which is a relevant and challenging logistic problem. Copyright © 2014 John Wiley & Sons, Ltd.     

Trade‐off ranking method for multi‐criteria decision analysis

Multi‐criteria decision analysis presumes trade‐off between different criteria. As a result, the optimal solution is not unique and can be represented by the Pareto frontier in the objective space. Each Pareto solution is a compromise between different objectives. Despite a limited number of Pareto optimal solutions, the decision‐maker eventually has to choose only one option. Such a choice has to be made with the use of additional preferences not included in the original formulation of the optimization problem. The paper represents a new approach to an automatic ranking that can help the decision‐maker. In contrast to the other methodologies, the proposed method is based on the minimization of trade‐off between different Pareto solutions. To be realized, the approach presumes the existence of a well‐distributed Pareto set representing the entire Pareto frontier. In the paper, such a set is generated with the use of the directed search domain algorithm. The method is applied to a number of test cases and compared against two existing alternative approaches.     

A Fuzzy Topsis Method for Prioritized Aggregation in Multi‐Criteria Decision Making Problems

Aggregation in a decision making environment requires the fusion of opinions of a group of decision makers. The group of decision makers are required to analyse a set of interrelated criteria that are usually measured on a linguistic scale. This process requires, in many instances, to capture experts experience, intuition and thinking that are traditionally expressed in a linguistic fashion rather than a numerical fashion. Furthermore, the necessity of considering the relationship between the criteria to the overall decision must be considered by the group of decision makers. This paper extends the application of fuzzy numbers, fuzzy relative importance scores (FRIS), fuzzy relative weights (FRW) and the fuzzy technique of order preference by similarity to ideal solution (TOPSIS) in prioritized aggregation. This extension provides a mean to systematically aggregate a group of decision makers' views for a set of interrelated criteria that are measured on a linguistic scale. First, an overview of the application of fuzzy numbers and the characteristics of aggregating fuzzy numbers in multi‐criteria decision making problems are presented. Then, the application of TOPSIS in fuzzy environments is presented. Next, past research is highlighted to present prioritized aggregation and the different aggregation operators' classes. Subsequently, a new prioritized aggregation method is presented. This method utilizes fuzzy TOPSIS with prioritized aggregation in fuzzy environments. Finally, the fuzzy prioritized aggregation method presented in this paper is applied on an actual case study. According to the results, the method presented in this paper provides a systematic approach to capture the uncertainty and imprecision associated with quantifying linguistic measurements in multi‐criteria decision making problems. Furthermore, it considers the relationship between the set of linguistically measured criteria undergoing prioritized aggregation in a fuzzy environment. Lastly, findings, conclusions and future work are presented. Copyright © 2016 John Wiley & Sons, Ltd.     

Distance‐Based Measures of Specialization and Balance in Multi‐Criteria: A DEA‐Integrated Method

Decision makers pursuing several distinct goals may differ in their behaviour. Some try to achieve all goals simultaneously, whereas others concentrate on only certain ones and are therefore specialized. We present a new method of measuring the specialization degree with respect to a given standard balanced relation of the goals. The measurement is integrated into data envelopment analysis (DEA) methodology, uses a non‐compensative distance measure, permits optional convex polyhedral cones as predefined balance sets and guarantees feasible solutions. Geometrically, it is based on the angle between the balance cone and the ray through the point of investigation. The proposed models can be used to support and complement the usual effectiveness and efficiency analysis in multi‐criteria decision analysis and DEA. They are motivated and illustrated by a simple numerical example, compared with an alternative method recently published and applied to real data from the literature. Copyright © 2014 John Wiley & Sons, Ltd.     

A Survey on Development and Applications of the Multi‐criteria Decision Making Method MULTIMOORA

This paper focuses on the MULTIMOORA method. Specifically, it discusses its development as well as extensions alongside with an overview of their applications. Indeed, the MULTIMOORA is a relatively new multi‐criteria decision making method consisting of the three parts. Therefore, it is specific with peculiarities of both complete and partial aggregation techniques. The MULTIMOORA method consists of the three parts, namely the Ratio System, the Reference Point, and the Full Multiplicative Form. The paper discussed the extensions of MULTIMOORA into the fuzzy environment and group decision making. The carried out survey identified the two major areas of application of MOORA and MULTIMOORA, namely engineering decision support for technological development and economic researches. The economic researches can further be classified into those related to micro‐level or macro‐level analyses. Copyright © 2013 John Wiley & Sons, Ltd.     

A Multicriteria Decision‐Making Application to the Conceptual Design of Mechanical Components

Early design is crucial for the success of the final product. In the conceptual design phase, several constraints, criteria, objectives and disciplines have to be considered. To this aim, multidisciplinary optimization has proven effective for the solution of engineering design problems, even in the industrial every‐day practice, to improve and simplify the work of designers in a successful quest of the best compromise solution. In this paper, a multicriteria decision‐making (MCDM)‐based design platform for early optimal design of industrial components is proposed. In a group decision‐making context, the selection of the most suitable component among several possible layouts is performed by means of a group Fuzzy Technique for Order of Preference by Similarity to Ideal Solution approach. Hence, a multi‐objective optimization is performed on the selected component by applying a multi‐objective particle swarm optimization for finding optimal component dimensions. An industrial case study is presented for showing the efficiency of the multicriteria decision‐making‐based design platform, regarding an innovative and low‐cost solution to increase the duration of heel tips in women's shoes. Copyright © 2016 John Wiley & Sons, Ltd.     

A Topsis‐Based Approach for Prioritized Aggregation in Multi‐Criteria Decision‐Making Problems

Aggregation in multi‐criteria decision‐making environments is a process of combining the values of a set of attributes into one representative value for the entire set of attributes. Many aggregation methods—ranging from the simple averaging approach to more sophisticated methods, such as ordered weighted averaging—have been applied in previous research. One challenge in aggregation arises in special cases of prioritized aggregation, wherein the prioritized relationships between attributes must be considered during aggregation. This paper presents a new approach to aggregating attributes with prioritized relationships. First, an overview of past research is conducted to identify different aggregation methods, classes and properties. Next, the concept of prioritized aggregation is explained in detail. A prioritized aggregation method utilizing the technique of order preference by similarity to ideal solution is then presented. Subsequently, the presented prioritized aggregation method is applied on an actual case study. According to the results, the aggregation method presented in this paper is, through the application of technique of order preference by similarity to ideal solution, capable of quantifying and considering the prioritized relationship between a set of attributes undergoing aggregation. Finally, conclusions are stated, and a discussion describing future work pertinent to this paper is presented. Copyright © 2016 John Wiley & Sons, Ltd.     

Individual Goal Orientations,Team Empowerment,and Employee Creative Performance: A Case of Cross‐level Interactions

Intrigued by relationship between team motivational context and individual characteristics in the organizational reality, we developed and tested a cross‐level model to investigate the interactive effects of team empowerment and individual goal orientations on individual creative performance through the mediating mechanism of an individual's creative self‐efficacy. Using multi‐wave multi‐source data from 63 R&D teams in three IT companies, we found that (1) team empowerment, individual learning goal orientation, and individual performance orientation are all positively related to individual creative performance through mediation of creative self‐efficacy; (2) learning orientation and performance approach orientation could both supplement the effects of team empowerment on individual creative self‐efficacy. Our findings point to the importance of individual goal orientation in shaping the effects of team motivation climates and provide insights for both scholars and practitioners. The specific practical implications include but are not limited to (1) individuals with learning and performance approach orientations should be identified during hiring procedures given that they could still thrive in less empowered teams and maintain a relatively high level of creative self‐efficacy and creative outcomes; (2) managers should consider assigning employees who are more learning oriented to more empowering and open‐ended tasks in order to obtain better creative results.     

The Goal Circuit Model: A Hierarchical Multi‐Route Model of the Acquisition and Control of Routine Sequential Action in Humans

Human control of action in routine situations involves a flexible interplay between (a) task‐dependent serial ordering constraints; (b) top‐down, or intentional, control processes; and (c) bottom‐up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top‐down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal‐directed action that goes beyond existing models by operationalizing an interface between two putative systems—a routine and a non‐routine system—thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non‐routine goal‐based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system.     

A parameter analysis method for the weight‐set to satisfy preference orders of alternatives in additive multi‐criteria value models

In the context of the additive multi‐criteria value model, this paper investigates how the set of criteria weights (weight‐set hereafter) can be determined according to the preference orders of alternatives given by the decision maker. A construction method is proposed for the weight‐set for different intervals of β, where β is a differential amount of value between the preference information on two alternatives. The results of this paper are important for sensitivity analysis in multi‐criteria decision making (MCDM) problems and multi‐criteria group decision analysis. Copyright © 2000 John Wiley & Sons, Ltd.     

An Approach for Determining the Mix between Human and Automated Service Providers

With the growing popularity of self‐service technologies (SST), businesses have to determine how to combine traditional human service with SST technologies. These mix problems have not received adequate attention in the research literature. This paper offers a decision model for solving mix problems between human service channels and SSTs. The decision problem cannot be determined as a traditional max or min problem and it is fuzzy. In fuzzy problems, the decision maker (DM) is considered as the focal point with the relevant knowledge and preferences and therefore, the objective is to maximize the DM's preferences. This paper develops a decision support system for eliciting a DM's value function when the service channels are considered to be substitutes, but when the rate of substitution may change and then uses a linear‐fractional model to fit the DM's data and optimize the DM's preferences. A service mix problem for a grocery store with human service providers and SSTs is used to demonstrate how the decision model can be employed to analyze and solve service mix decision making problems. Copyright © 2014 John Wiley & Sons, Ltd.     

Generating consensus priority interval vectors for group decision‐making in the AHP

Many practical and important decision‐making problems are complicated by at least two factors: (1) the qualitative/subjective nature of some criteria often results in uncertainty in the individual ratings; and (2) group decision‐making is involved and some means of aggregating individual ratings is required. Traditionally, both individual and group priorities have been represented as point estimates, but this approach presents severe limitations for accommodating imprecision in the decision‐making process. This paper examines the group decision‐making problem in the context where priorities are represented as numeric intervals. A set of techniques that could be used at some of the phases of an analytic hierarchy process (AHP)‐based group decision‐making process, which has the objective of generating a ‘consensus’ priority that represents the group's opinion with regards to the relative importance of a set of N objects (e.g. criteria, alternatives), is presented. Copyright © 2000 John Wiley & Sons, Ltd.     

Religion,Secular Humanism,and Atheism: Multi‐Institutional Politics and the USAFA Cadets’ Freethinkers Group

The recent emergence of atheist movements despite marginalization and distrust by a majority of Americans has been explained as a successful deployment of identity politics, but scholars have less often considered the importance of how identity and power intersect with political opportunity occurring within organizational and religious fields. Analyzing the case of the U.S. Air Force Academy (USAFA) Cadet Freethinkers Group, we demonstrate that although it encountered opportunity in the organizational shock of the 2004 USAFA proselytism controversy, this opportunity was not a blank check, but instead afforded some possibilities for action and not others. Freethinkers’ actions to secure official recognition were limited by (1) their low placement in the chain of command and (2) a collective identity inclusive of secular humanism and atheism, which did not produce enough unity to take collective actions risking punishment, and created ambiguity vis‐à‐vis religion that allowed USAFA administrators to accept or deny their institutional membership claims through appeal, respectively, to functional or substantive definitions of religion.