Maker's Preferences Research Articles

Incremental Weight Elicitation for Multiobjective State Space Search

This paper proposes incremental preference elicitation methods for multiobjective state space search. Our approach consists in integrating weight elicitation and search to determine, in a vector-valued state-space graph, a solution path that best fits the Decision Maker's preferences. We first assume that the objective weights are imprecisely known and propose a state space search procedure to determine the set of possibly optimal solutions. Then, we introduce incremental elicitation strategies during the search that use queries to progressively reduce the set of admissible weights until a nearly-optimal path can be identified. The validity of our algorithms is established and numerical tests are provided to test their efficiency both in terms of number of queries and solution times.

Contingent rerouting for enhancing supply chain resilience from supplier behavior perspective

AbstractThis paper examines contingent rerouting strategy for enhancing supply chain resilience taking a supplier's point of view. We consider a supply chain with multiple suppliers at each stage and establish a mathematical model for product allocation behavior among different suppliers. The allocation model is based on each supplier's production capacity, product quality, production cost, as well as possible decision maker's preferences. As a performance measure for rerouting strategy, we use the total outflow of the supply chain. We propose an optimization model and its solution determines the rerouting strategy for product flow through the supply chain under disruptions. Numerical examples demonstrate the effect of the rerouting strategy and show the resilience of the supply chain.

An Extended Fuzzy PROMETHEE based on Fuzzy Rule based System for Supplier Selection Problem

Background/Objectives: Supplier Selection Problem (SSP) has become critical issue for purchasing department because of its significant effect toward successful logistic and Supply Chain Management (SCM). Due to the fact that various conflicted criteria must be considered, SSP is inherently Multiple Criteria Decision Making (MCDM) problem. Methods/Statistical Analysis: The aim of this paper is to solve SSP under group decision making and fuzzy environment. To do so, as main contribution of this paper, a hybrid approach which employs both Fuzzy Rule Based System (FRBS) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), is proposed to select suitable supplier. Findings: One advantage of MCDM techniques is providing Decision Maker's (DM) preferences and utilities. To do so, we have modeled DM's utilities using fuzzy logic and then implemented in PROMETHEE method. Improvement/Application: Ultimately, numerical illustration and sensitivity analyses are performed to demonstrate the applicability of the proposed method for a supplier selection problem.

Integrating LINMAP and TOPSIS methods for hesitant fuzzy multiple attribute decision making

The linear programming technique for multidimensional analysis of preference (LINMAP) and the technique for order preference by similarity to ideal solution (TOPSIS) are two well-known methods for dealing with the multiple attribute decision making (MADM) problems. In this paper, we further extend the LINMAP to accommodate hesitant fuzzy environment and propose a new approach to solve the MADM problems with hesitant fuzzy information. Then, an integrated method that combines the LINMAP and TOPSIS is developed. In this methodology, we take advantage of the LINMAP to determine the attribute weights objectively and thus overcome the drawbacks of the TOPSIS. Meanwhile the TOPSIS that considers both the positive ideal solution (PIS) and the negative ideal solution (NIS) can make up the disadvantages of the LINMAP. In order to measure consistency and inconsistency between the ranking order of alternatives and the decision maker's preference, two indices based on PIS and NIS are defined. Moreover, a linear programming model that can be used for minimizing the total inconsistency is constructed to determine the attribute weights. By using the TOPSIS, we obtain the most appropriate alternative. Finally, two numerical examples are given to demonstrate the implementation process of the developed approach.

A novel weight allocation and decision making method for space launch vehicle design concept selection

One of the most important issues in multi–attribute decision making is the determination of the criteria weights. Traditionally, criteria weights are allocated as per the expert's judgement or are objectively determined using mathematical models. While the weights allocated as per the expert's judgement are criticised for its subjectivity, the objective weights lack the ability to include the decision maker's preferences. To overcome these difficulties, a new integrated weight allocation approach has been proposed which has the ability to accept both subjective and objective information. A novel way of eliciting decision maker's subjective information has been presented. Moreover, a new multiple attribute decision making method is proposed in which the performance index of alternatives is used as a basis for ranking and decision making. The usefulness of the proposed weight allocation and decision making approach has been demonstrated with its successful application to the practical problem of space launch vehicle design concept selection.

Interactive environment WebOptim for solving multiple-objective problems using scalarising and evolutionary approaches

A web-based decision support system (DSS) is presented in this paper. It is an interactive environment for solving multiple-objective problems with continuous and/or integer decision variables. The system is targeted at different types of users - researchers, educators and business people. The system supports a number of fifteen interactive methods. They are incorporated into original generalised scalarising interactive method (GENS-IM). The choice of a method is organised in an implicit way on the base of decision maker's (DM) preferences. An evolutionary method is also included in the system. The DM can switch the methods in one solution process.

Integrating Preference by Means of Desirability Function with Evolutionary Multi-objective Optimization

Desirability function is a mathematically simple description of decision maker's preference. A desirability function transforms objective function to a scale-free desirability value, which actually measures the decision maker's satisfaction with the objective value. In this paper, we utilize desirability functions to express decision maker's preference to specific regions with an objective. These desirability functions are integrated into evolutionary multi-objective algorithms to generate a uniformly distributed set of Pareto solutions in desirability space. The corresponding images in objective space of this set of solutions are exactly the decision maker's preferred solutions. The experimental results show the effectiveness of this approach.

Disaggregation of energy-saving targets for China's provinces: modeling results and real choices

Starting with the 11th Five-Year Plan (FYP) and continuing in the 12th FYP, quantitative and binding targets have been set for energy-efficiency improvement in China. Drawing on international experience in burden-sharing on climate change, this paper presents a framework for provincial-level disaggregation of energy-saving targets in China. Based on principles of equity and efficiency, four scenarios have been established by weighting different choice preferences of responsibility, capacity, and potential. In addition, nonlinear and linear allocation models have been developed by considering or ignoring marginal energy-saving cost. When this framework was applied to the disaggregation of the national energy saving target of 16% during the 12th FYP, the results show that the final allocation schemes are largely determined by the policy maker's choice preferences. The extreme reduction target of 37.26% fell to Shanghai under responsibility preferring (RP) using the linear allocation method, while the capability preferring (CP) scenario considering marginal energy-saving cost is the closest to the actual scheme accepted by the 30 provinces. Development of such a framework may serve as a feasible policy instrument to help China achieve its conservation targets in a cost-effective way and in accordance with its regional development strategies.

A novel interactive preferential evolutionary method for controller tuning in chemical processes

In response to many multi-attribute decision-making (MADM) problems involved in chemical processes such as controller tuning, which suffer human's subjective preferential nature in human–computer interactions, a novel affective computing and preferential evolutionary solution is proposed to adapt human–computer interaction mechanism. Based on the stimulating response mechanism, an improved affective computing model is introduced to quantify decision maker's preference in selections of interactive evolutionary computing. In addition, the mathematical relationship between affective space and decision maker's preferences is constructed. Subsequently, a human–computer interactive preferential evolutionary algorithm for MADM problems is proposed, which deals with attribute weights and optimal solutions based on preferential evolution metrics. To exemplify applications of the proposed methods, some test functions and, emphatically, controller tuning issues associated with a chemical process are investigated, giving satisfactory results.

Progressive Multi-Objective Optimization

This paper introduces progressive multi-objective optimization (PMOO), a novel technique to include the decision maker's preferences into the multi-objective optimization process. PMOO integrates a well-known method for multi-criteria decision making (PROMETHEE) into a simple multi-objective metaheuristic by maintaining and updating a small reference archive of nondominated solutions throughout the search. By applying this novel technique to a set of instances of the multi-objective knapsack problem, the superiority of PMOO over the commonly accepted sequential approach of generating a Pareto set approximation first and selecting a single solution afterwards is demonstrated.

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

Project portfolio selection is one of the most important problems faced by any organization. The decision process involves multiple conflicting criteria, and has been commonly addressed by implemen ting a two-phase procedure. The first step identifies the efficient solution set; the second step supports the decision maker in se lecting only one portfolio solution from the efficient set. However, several recent studies show the advantages gained by optimizing towards a region of interest (according to the decision maker's preferences) instead of approximating the complete Pareto se t. However, these works have not faced synergism and its variants, such as cannibalization and redundancy. In this paper we introduce a new approach called Non-Outranked Ant Colony Optimization , which optimizes interdependent project portfolios with a priori articulation of decision-maker preferences based on an outranking model. Several experimental tests show the advantages of our proposal over the two-phase approach, providing reasonable evidence of its potential for solving real-world high-scale problems with many objective s.

Two-Decision-Maker Conflict Resolution with Fuzzy Preferences

A unique fuzzy approach is developed to model uncertainties in the preferences of a decision maker involved in a conflict. Human judgments, including expressing preferences over a set of feasible outcomes or states in a conflict, are usually imprecise. Situations characterized by vagueness, impreciseness, incompleteness and ambiguity, are often reflected in the decision maker's preferences. When modeling a conflict, it is assumed that the decision makers, the courses of actions available for each, and the preferences of each decision maker are known. When the preferences of the decision maker over a certain set of actions are not known with certainty, this could affect the overall equilibria which are predicted in an analysis. Hence, fuzzy logic is used to handle imprecise or vague preference information so that realistic equilibria can be found. The well-known game of Prisoner's Dilemma, in which one must decide whether or not to cooperate, is employed as an illustrative application to demonstrate how the fuzzy preference methodology works in practice.

Defining Optimum Treatment of Patients With Pancreatic Adenocarcinoma Using Regret-Based Decision Curve Analysis

To use regret decision theory methodology to assess three treatment strategies in pancreatic adenocarcinoma. Pancreatic adenocarcinoma is uniformly fatal without operative intervention. Resection can prolong survival in some patients; however, it is associated with significant morbidity and mortality. Regret theory serves as a novel framework linking both rationality and intuition to determine the optimal course for physicians facing difficult decisions related to treatment. We used the Cox proportional hazards model to predict survival of patients with pancreatic adenocarcinoma and generated a decision model using regret-based decision curve analysis, which integrates both the patient's prognosis and the physician's preferences expressed in terms of regret associated with a certain action. A physician's treatment preferences are indicated by a threshold probability, which is the probability of death/survival at which the physician is uncertain whether or not to perform surgery. The analysis modeled 3 possible choices: perform surgery on all patients; never perform surgery; and act according to the prediction model. The records of 156 consecutive patients with pancreatic adenocarcinoma were retrospectively evaluated by a single surgeon at a tertiary referral center. Significant independent predictors of overall survival included preoperative stage [P = 0.005; 95% confidence interval (CI), 1.19-2.27], vitality (P < 0.001; 95% CI, 0.96-0.98), daily physical function (P < 0.001; 95% CI, 0.97-0.99), and pathological stage (P < 0.001; 95% CI, 3.06-16.05). Compared with the "always aggressive" or "always passive" surgical treatment strategies, the survival model was associated with the least amount of regret for a wide range of threshold probabilities. Regret-based decision curve analysis provides a novel perspective for making treatment-related decisions by incorporating the decision maker's preferences expressed as his or her estimates of benefits and harms associated with the treatment considered.

Scale Dependence and Ranking Intervals in Additive Value Models Under Incomplete Preference Information

A multiattribute additive value function that has been built from a complete specification of the decision maker's (DM's) preferences gives scale-independent decision recommendations, which do not depend on how the value function is normalized. In this paper, we show that if the preference specification is incomplete, many widely employed decision rules for comparing alternatives give scale-dependent decision recommendations in which the relative ranking of alternatives depends not only on the DM's preferences but also on the normalization of the value function. But because normalization does not involve preference statements, the recommendations should be scale independent so that they do not depend on the chosen normalization. To provide such recommendations, we propose ranking intervals, which show how a given alternative compares with all other alternatives for all value functions that are consistent with the stated preference information. These intervals can be computed from mixed integer linear optimization problems that are constrained by inequalities implied by the DM's preference statements. We illustrate the use of ranking intervals by analyzing university rankings and discuss their uses in project portfolio selection.

Multidimensional Risk Assessment of Manhole Events as a Decision Tool for Ranking the Vaults of an Underground Electricity Distribution System

Even though an underground electricity distribution system is safer than an overhead system, several accidents have occurred in them. Assessing the risk of hundreds or even thousands of underground vaults is a hard task. Furthermore, given the large variability in external and internal environments and, hence, there being a wide range of possible consequences when an accident occurs, an approach to risk assessment under a multidimensional view is required. Moreover, in terms of decision making, the aggregation of the decision maker's preferences in modeling, by multiple-criteria decision-making methods, is more complete, comprehensive, and, in particular, includes considering the decision maker's desires. Therefore, this study puts forward a multidimensional assessment of the risks from underground vaults by generating a decision tool, which ranks the vaults in a risk hierarchy. Multiattribute utility theory was used to achieve this ranking. An application was generated to demonstrate the applicability of the model, under the following aspects of consequences: those that are human, financial, and operational; and disruptions to local vehicular traffic. The use of information arising from analysis of the differences between risks enabled the decision maker to make an in-depth analysis of the range of possibilities over which alternatives may be chosen in order to implement preventive actions.

Preference Reversals in Judgment and Choice: The Prominence Effect

According to normative decision theory there exists a principle of procedure invariance which states that a decision maker's preference order should remain the same, independently of which response mode is used. For example, the decision maker should express the same preference independently of whether he or she has to judge or decide. Nevertheless, previous research in behavioral decision making has suggested that judgments and choices yield different preference orders in both the risky and the riskless domain. In the latter, the prominence effect has been demonstrated. The main purpose of the present series of experiments was to test cognitive explanations which account for the prominence effect. One of the explanations provided a psychological account based primarily on decision-strategy compatibility. Two other explanations built on information structuring approaches. In the first one, the general idea was that decision makers differentiate between alternatives by value and belief restructuring. In the second approach, violations of invariance were assumed to be attributed to the information structure of the task which in many cases demand problem simplification. A prominence effect was in most experiments found for both choices and preference ratings. This finding spoke against the strategy compatibility explanation. Instead, the different forms of cognitive restructuring provided a better account. However, none of these provided a single explanation. Yet, the structure compatibility explanation appeared to be the more viable one, in particular of the relation between experimental manipulations and response mode outcomes. The predictions of the value-belief restructuring explanation, on the other hand, seemed to be more valid for the prominence effect found in choice than for preference ratings.

Ranking of Distribution System's Redesign Scenarios Using Stochastic MCDM/A Procedure

The paper presents the original procedure of solving a multiple criteria stochastic ranking problem consisting in the evaluation of different variants of the distribution system. The problem originates from the analysis and construction of the redesign scenarios of the existing distribution system. The authors develop a computational procedure being a combination of a traditional – deterministic multiple criteria ranking method (e.g. Electre III/IV) and a classification algorithm (e.g. Bayes classifier). The proposed method is composed of six steps, including: stochastic data collection, random selection of deterministic numbers using simulation technique, solving a multiple criteria ranking problem with an application of a deterministic multiple criteria decision aiding/making (MCDM/A) method, the classification of deterministic relations between redesign scenarios (variants) to predefined classes using classification algorithm, the construction of a final ranking of redesign scenarios with an application of a spreadsheet, the recommendation of the compromise solution based on stochastic final ranking of redesign scenarios. The proposed approach is verified on the real-world analysis of the distribution system of goods which operates at the Polish electro-technical market. The results of computational experiments, including: ranking generation, classification and sensitivity analysis are demonstrated. The analysts’ final recommendation of the compromise solution selection is presented. It is based on a comprehensive analysis of the current state of the system, the perspectives of its development, decision maker's preferences, results of the computational experiments and sensitivity analysis.

Decision Analysis with Geographically Varying Outcomes: Preference Models and Illustrative Applications

This paper presents decision analysis methodology for decisions based on data from geographic information systems. The consequences of a decision alternative are modeled as distributions of outcomes across a geographic region. We discuss conditions that may conform with the decision maker's preferences over a specified set of alternatives; then we present specific forms for value or utility functions that are implied by these conditions. Decisions in which there is certainty about the consequences resulting from each alternative are considered first; then probabilistic uncertainty about the consequences is included as an extension. The methodology is applied to two hypothetical urban planning decisions involving water use and temperature reduction in regional urban development, and fire coverage across a city. These examples illustrate the applicability of the approach and the insights that can be gained from using it.

Many-Objective Evolutionary Algorithm Based on Bipolar Preferences Dominance

高维目标优化是目前多目标优化领域的研究热点和难点.提出一种占优机制,即双极偏好占优用于处理高维目标优化问题.该占优机制同时考虑决策者的正偏好和负偏好信息,在非支配解之间建立了更加严格的占优关系,能够有效减少种群中非支配解的比例,引导算法向靠近正偏好同时远离负偏好的Pareto最优区域收敛.为检验该方法的有效性,将双极偏好占优融入NSGA-Ⅱ中,形成算法2p-NSGA-Ⅱ,并在2到15目标标准测试函数上进行测试,得到了良好的实验结果.同时,将所提出的占优机制与目前该领域的两种占优机制g占优和r占优进行性能对比,实验结果表明,2p-NSGA-Ⅱ算法无论是在求解精度还是运行效率上,整体上均优于g-NSGA-Ⅱ和r-NSGA-Ⅱ.;Many-Objective optimization is a difficulty for classical multi-objective evolutionary algorithm and has gained great attention during the past few years. In this paper, a dominance relation named bipolar preferences dominance is proposed for addressing many-objective problem. The proposed dominance relation considers the decision maker's positive preference and negative preference simultaneously and creates a strict dominance relation among the non-dominated solutions, which has ability to reduce the proportion of non-dominated solutions in population and lead the race to the Pareto optimal area, which is close to the positive preference and far away from negative preference. To demonstrate its effectiveness, the proposed approach was integrated into NSGA-Ⅱ to be a new algorithm denoted by 2p-NSGA-Ⅱ and tested on a benchmark of two to fifteen-objective test problems. Good results were obtained. The proposed dominance relation was also compared to g-dominance and r-dominance which was the most recently proposed dominance relation, the results of comparative experiment showed 2p-NSGA-Ⅱ was superior to g-NSGA-Ⅱ and r-NSGA-Ⅱ on a whole, no matter the accuracy of obtained solutions or the efficiency of algorithm.

Intuitionistic multiplicative aggregation operators with their application in group decision making

Today, preference relations are among the most common types of expressing decision maker's preference information found in uncertainty research. Intuitionistic multiplicative preference relation can reflect our intuition more objectively and it was proposed by Xia et al. (20) recently. In this article, we first develop some aggregation operators for intuitionistic multiplicative numbers, such as the intuitionistic multiplicative weighted averaging operator and intuitionistic multiplicative ordered weighted averaging operator. Their desirable properties are also investigated in detail. Furthermore, based on the proposed aggregation operators, we further introduce a group decision making method based on intuitionistic multiplicative preference relations. Finally, a real example about personnel selection is given to illustrate the effectiveness of our method, the numeric comparison between our group decision making method and the ones of Xia et al. (20) are also provided.