Maker's Preferences Research Articles

A provincial renewable portfolio standards-based distribution strategy for both power plant and user: A case study from Guangdong, China

Renewable portfolio standards have been found to be effective in promoting renewable energy developments; however, in China, the implementation of renewable portfolio standards is not yet fully developed. Based on policy acceptance, generation costs, and renewable power target perspectives, this paper proposes a multi-objective equilibrium model to develop an annual single province power plan. A case study from Guangdong province, China, is presented to demonstrate the practicality and efficiency of the presented model. Comparison analysis with current plans are made, three scenarios which consider the non-hydro renewable targets, market competition and a tougher environmental attitude are designed to adapted to the decision maker's preferences. It was found that: (1) the optimal model reallocated policy costs between power users, distributed the generation capacity between provinces, and could obtain 35.4% renewable electricity and 4.3% of non-hydro renewable electricity consumption in Guangdong; and (2) compared with the current plan, the optimal model effectively maintained power tariffs, decreased generation costs by 2.42%, and decreased Guangdong's electricity generation CO2 emissions by 8.21%. Policy suggestions for provincial governments to better implement provincial renewable portfolio standards are given.

Fuzzy preference ranking organization method for enrichment evaluation‐based node cooperation technique for improving QoS in MANETs

SummaryCooperation between mobile nodes is the predominant factor required for achieving reliable data dissemination in mobile ad hoc networks (MANETs), since non‐cooperative nodes crumbles the routing process and degrades network performance. The degree of non‐cooperation rendered by mobile nodes need to be accurately identified by exploring feasible and comprehensive number of criteria to achieve maximized cooperation in uncertain MANET. In this paper, fuzzy preference ranking organization method for enrichment evaluation (FPROMETHEE)‐based node cooperation technique is proposed with the merits of trapezoidal fuzzy interval numbers that aids in the selection of high trustworthy mobile nodes for attaining maximized data delivery. This FPROMETHEE technique utilizes complex and multiple criteria for potential ranking in the presence of finite number of mobile nodes. It prevented the bias of information that is more common during the judgmental process associated with the cooperation estimation of mobile nodes. It includes information about the relative weights of criteria and decision maker's preferences for estimating the trust of mobile nodes to include or isolate them from the routing path. It is also useful in handling the degree of uncertainly involved in the process of routing without compromising Quality of Service (QoS) level in the network. The simulation experiments of the proposed FPROMETHEE technique conducted using ns‐2 simulator confirmed its predominance in enhancing the packet dissemination rate, throughput, energy consumptions, total overhead, and packet loss rate for different number of mobile nodes, malicious nodes, and pairs of source and destination nodes.

A novel best‐worst‐method two‐stage data envelopment analysis model considering decision makers' preferences: An application in bank branches evaluation

AbstractData envelopment analysis (DEA) model has been applied for evaluating bank branches and recognizing efficient and inefficient branches can help bank managers to provide appropriate strategies to improve the inefficient branches' performance. Conventional DEA models are based on the ‘black box’ approach. However, the process of providing services in banks is made up of interactive and interdependent processes. Additionally, some managers tend to incorporate their preferences in evaluation process. In this paper, best worst method (BWM) is used for incorporating decision maker (DM) preferences in two‐stage DEA model. First, BWM model is applied to obtain the weights of inputs, intermediate measures and outputs based on DM's judgement. Second, generated weights are imposed on two‐stage DEA model as additional constraints and a novel bi‐objective BWM‐two stage DEA model is introduced. Finally, the proposed bi‐objective BWM‐two stage DEA model is solved using min‐max approach. To illustrate the capability of proposed model, 45 Agricultural Bank (Agribank) branches in West Azerbaijan province of Iran are evaluated. The branches' processes are considered as two stages ‘production’ and ‘profitability’ and efficiency of branches are calculated in each stage. According to the efficiencies of each sub‐stage, branches are divided to four groups and recommendations are made for each group.

Optimal design of two-dimensional water trading based on risk aversion for sustainable development of Daguhe watershed, China

Water related problems, including water scarcity and pollution, have become increasingly urgent challenges especially in arid and semiarid regions. Two-dimensional water trading (2DWT) mechanism has been designed to unify the quantity and quality of water for relieving the water crisis. This study aims to develop a risk aversion optimization-two dimensional water trading model (RAO-2DWTM) for planning the regional-scale water resources management system (RWMS). This is the first attempt on planning RWMS through risk aversion optimization within the two-dimensional water trading framework. RAO-2DWTM cannot only support in-depth analysis regarding the effect of decision maker's preferences on system risk in different trading scenarios, but also reflect the interaction between water right trading and effluent trading, as well as disclose the optimal scheme of water resource management under uncertainties. Twenty four scenarios associated with different trading scenarios and robust levels are analyzed. The optimization scheme under the optimal risk control level is determined based on TOPSIS. Results revealed that 2DWT would bring high benefit with reduced risk cost, water deficit and emissions, implying the effectiveness of 2DWT mechanism. The results also disclosed that risk aversion behavior can mitigate water scarcity and pollution, as well as reduce risk cost, but may lead to some losses of system benefit. Consequently, decision makers should make trade-offs between system benefit and risk in identifying desired trading schemes.

Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty

In this paper we propose efficient methods for elicitation of complexly structured preferences and utilize these in problems of decision making under (severe) uncertainty. Based on the general framework introduced in Jansen et al. (2018) [37], we now design elicitation procedures and algorithms that enable decision makers to reveal their underlying preference system (i.e. two relations, one encoding the ordinal, the other the cardinal part of the preferences) while having to answer as few as possible simple ranking questions. Here, two different approaches are followed. The first approach directly utilizes the collected ranking data for obtaining the ordinal part of the preferences, while their cardinal part is constructed implicitly by measuring the decision maker's consideration times. In contrast, the second approach explicitly elicits also the cardinal part of the decision maker's preference system, however, only an approximate version of it. This approximation is obtained by additionally collecting labels of preference strength during the elicitation procedure. For both approaches, we give conditions under which they produce the decision maker's true preference system and investigate how their efficiency can be improved. For the latter purpose, besides data-free approaches, we also discuss ways for statistically guiding the elicitation procedure if data from elicitations of previous decision makers is available. Finally, we demonstrate how the proposed elicitation methods can be utilized in problems of decision under (severe) uncertainty. Precisely, we show that under certain conditions optimal decisions can be found without fully specifying the preference system.

Preference prediction analysis based on graph model for environmental governance conflict

<abstract> <p>A mathematical model for decision maker's preference prediction in environmental governance conflict is established based on the graph model for conflict resolution. The rapid economic development in many countries, over the past decades, has caused serious environmental pollution. Sewage companies are the main source of contamination since they are always wavering on the issue of environmental governance because of their profit-seeking nature. Environmental management departments cannot grasp the offending company preferences accurately. The problem of how to obtain decision maker's preference in environmental governance conflict is studied in this paper. The mathematical model established in this paper can obtain a preference set of one decision maker on the promise that the ideal conflict outcome and preference of the other decision makers are known. Then, preference value distribution information entropy is introduced to mine the preference information contained in the preference set, which means that it is possible to get the preference information of conflict opponent at their own ideal conflict outcome. All of these preference sets provide the possibility to choose the appropriate coping strategies and lead the conflict to the direction that some decision makers want. Finally, the effectiveness and superiority of the preference prediction analysis method is verified through a case study of "Chromium Pollution in Qujing County" which took place in China. The preference prediction analysis method in this paper can provide decision making support for the decision makers in environmental governance from strategic level.</p> </abstract>

A new hybrid method for selecting the best project manager: TODIM-FSE and Behavioral TOPSIS

This work aims to present the application of Multi-Criteria Decision-Making methods to the process of recruiting candidates for the position of project manager, considering aspects of the decision maker's preferences in uncertain and risk scenarios. Applied, descriptive and experimental, made up of the combined employment TODIM-FSE methods for multi-criteria classification of available candidates, and the method Behavioral TOPSIS, to choose the ideal project manager. The hybrid application of the Multi-Criteria Decision-Making methods TODIM-FSE, method based on Prospect Theory, and Behavioral TOPSIS, which considers the concept of loss aversion of Economic Behavior, is essentially innovative. When using TODIM-FSE and Behavioral TOPSIS, it was verified the explicit incorporation of the risk profile of the decision maker - aggressive, neutral, or conservative - in the context of aversion or propensity to the risks associated with the management of a project. Through the personal recruiting process from a large Brazilian organization, the possibility of adopting the hybrid model resulting from the combination of the two methods in a real situation was validated. Such validation allowed us to conclude that the candidates' classifications and choices, previously normally accepted, were at odds with the profile and risk propensity of the decision makers.

Learning target-based preferences through additive models: An application in radiotherapy treatment planning

This article presents a new Multi-Criteria Decision Aiding preference disaggregation method based on an asymmetric target-based model. The decision maker's preferences are elicited considering the choices made given a set of comparisons among pairs of solutions (the stimuli). It is assumed that the decision maker has a reference value (target) for the stimulus. Solutions that do not comply with this reference value for some of the criteria dimensions considered will be penalized, and an inferred weight is associated with each dimension to calculate a penalty score for each solution. One of the differentiating features of the proposed model when compared with other existing models is the fact that only solutions that do not meet the target are penalized. The target is not interpreted as an ideal solution, but as a set of threshold values that should be taken into account when choosing a solution. The proposed approach was applied to the problem of choosing radiotherapy treatment plans, using a set of retrospective cancer cases treated at the Portuguese Oncology Institute of Coimbra. Using paired comparison choices made by one radiation oncologist, the preference model was built and was tested with in-sample and out-of-sample data. It is possible to conclude that the preference model is capable of representing the radiation oncologist's preferences, presenting small mean errors and leading, most of the time, to the same treatment plan chosen by the radiation oncologist.

A Data Envelopment Analysis or Goal Programming Model with Fuzzy Preferences of Decision Makers

Due to the complexity and uncertainty involved in real world decision problems, the determination and interpretation of Decision Makers' preference relations remain a challenging task for them. This paper develops a novel procedure for incorporating preference information in the efficiency analysis of Decision Making Units. The efficiency of Decision Making Units is defined in the spirit of Data Envelopment Analysis, complemented with Decision Maker's preference information. Our procedure begins by aiding the different decision making group members to express their incomplete fuzzy preferences by using a multiple objective linear programming approach for generating a common set of weights in the DEA framework.

A multidimensional and multi-period analysis of safety on roads

This paper proposes a multidimensional and multi-period analysis of safety on roads. It aggregates different road safety performance indicators observed over different periods for which multicriteria and multi-period approaches are used. The criticality of a road depends on the interaction of various factors, such as human factors, causes of accidents and their severity levels, and the characteristics/states of the roads. Therefore, there is a need for a multidimensional view of risk and its consequences concerning traffic accidents. Furthermore, using the Multiple Criteria Decision Making/Aid methods (MCDM/A) allows the performance of roads in the multiple criteria to be considered according to the decision maker's preferences. On the other hand, the temporal approach reflects the performance of roads and their accidents in different periods, enabling the information on the temporal behavior of accidents to be aggregated to the result. Given that Brazil has a vast road network, there is thus the problem of prioritizing road segments to allocate resources for traffic accident prevention and mitigation actions, especially as these resources are usually limited and scarce. For that, a case study is developed in the state of Pernambuco in Brazil. Eleven road segments are analyzed. The strategic objective of the decision-maker is to have a broader view, initially, of the criticality of these road segments in terms of safety so that strategically he can allocate resources to prevent and mitigate the risks of traffic accidents. For this, the decision-maker considered eleven criteria. These represent the different dimensions that can influence traffic accidents, such as the damage (impacts) to human beings or other consequences resulting from traffic accidents and issues related to the characteristics/state of the road and its traffic. Five periods of time were considered to incorporate the temporal influence of these dimensions (2015 to 2019). As a result, it is seen that, for a more comprehensive assessment, it is essential to consider a multidimensional view of risk and a multi-period evaluation, thus incorporating more information into the decision model and thereby making its results more assertive.

A General Interactive Approach for Solving Multi-Objective Combinatorial Optimization Problems with Imprecise Preferences

In this paper, we develop a general interactive method to solve multi-objective combinatorial optimization problems with imprecise preferences. Assuming that preferences can be represented by a parameterized scalarizing function, we iteratively ask preferences queries to the decision maker in order to reduce the uncertainty over the preference parameters until being able to determine her preferred solution. To produce informative preference queries at each step, we generate promising solutions using the extreme points of the polyhedron representing the admissible preference parameters and then we ask the decision maker to compare two of these solutions (we propose different selection strategies). These extreme points are also used to provide a stopping criterion guaranteeing that the returned solution is optimal (or near-optimal) according to the decision maker's preferences. For the multi-objective spanning tree problem with a linear aggregation function, we provide numerical results to demonstrate the practical efficiency of our approach and we compare our results to a recent approach based on minimax regret, where preferences are asked during the construction of a solution. We show that better results are achieved by our method both in terms of running time and number of questions.

Study on the early warning and forecasting of flash floods in small watersheds based on the rainfall pattern of risk probability combination

Flash floods cause great harm to people’s lives and property safety. Rainfall is one of the main causes of flash floods in small watersheds. The uncertainty of rainfall events results in inconsistency between the traditional single rainfall pattern and the actual rainfall process, which poses a great challenge for the early warning and forecasting of flash floods. To carry out the effective flash flood early warning and forecasting, this paper proposes a novel rainfall pattern by coupling total rainfall and peak rainfall intensity based on copula functions, i.e., the rainfall pattern of risk probability combination (RPRPC). On this basis, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrological model is used to simulate the rainfall-runoff process, the trial algorithm is used to calculate the critical rainfall (CR), and the optimistic-general-pessimistic (O–G-P) early warning mode considering the decision maker's risk preference is proposed. The small watershed of Xinxian in Henan province, China, is taken as a case study for calculation. The results show that the RPRPC is feasible and closer to the actual rainfall process than the traditional rainfall pattern, Frank copula function is the best for determining the joint distribution function of total rainfall and peak rainfall intensity, and the HEC-HMS model can be applied to small watersheds in hilly areas. Additionally, both RPRPC and antecedent soil moisture condition (ASMC) have influence on CR, and the variation of RPRPC will change the influence of ASMC on CR. Finally, the effectiveness of O–G-P early warning mode is verified.

The Implementation of SAW and BORDA Method to Determine the Eligibility of Students’ Final Project Topic

The fourth-year students of Bachelor of Applied Studies (BAS) Software Engineering Technology Department of Information Technology (IT) Politeknik Negeri Padang (PNP) are required to work on the Final Project Proposal to the Coordinator, to deliver to the expertise group team to assess the eligibility of the topic. The expertise teams consist of the same skill family. The assessment criteria include originality, novelty, target and topic contribution, methodology, and similarity. Therefore, a system to support group decisions is highly needed to get eligibility for the topic. In a pandemic like today, indoor gatherings are severely restricted. The work from home policy also limits the movement of the team to gather together so that the expert team who would judge cannot conduct a meeting to determine the feasibility of the final project topic optimally. The existence of a subjective assessment of a particular topic requires discussion from the team. The simple Additive Weighting (SAW) method was used to rank the final project proposal, and BORDA method was used to Accumulate the assessment score of the expert team. The research revealed the recommendation on students’ final topics. Testing is done by testing the sensitivity of the criteria used in a decision maker's preference. The final result of this research is a recommendation of a final project that is feasible to be implemented by students and recommendation for sensitive assessment criteria. From the ten topics of the final project that were assessed, seven topics could be accepted. The sensitivity test results showed that the weight with criterion 1 and criterion 4 significantly affect the assessment results.

Ambiguity under growing awareness

In this paper, we study choice under growing awareness in the wake of new discoveries. The decision maker's behavior is described by two preference relations, one before and one after new discoveries are made. The original preference admits a subjective expected utility representation. As awareness grows, the original decision problem expands and so does the state space. Therefore, the decision maker's original preference has to be extended to a larger domain, and consequently the new preference might exhibit ambiguity aversion. We propose two consistency notions that connect the original and new preferences. Unambiguity Consistency requires that the original states remain unambiguous while new states might be ambiguous. This provides a novel interpretation of ambiguity aversion as a systematic preference to bet on old states than on newly discovered states. Likelihood Consistency requires that the relative likelihoods of the original states are preserved. Our main results axiomatically characterize a maxmin expected utility (MEU) representation of the new preference that satisfies the two consistency notions. We also extend our model by allowing the initial preference to be MEU, and characterize reverse full-Bayesianism, which is an extension of the reverse Bayesianism of Karni and Vierø (2013) to MEU preferences.

A novel approach for exploring the trade‐offs between several features of students’ well‐being

AbstractWe propose a novel approach to explore the trade‐offs between four features of students’ well‐being (anxiety, motivation, sense of belonging, and bullying). On the one hand, a multiobjective interval problem is formulated by considering these distinct components of well‐being as objective functions, being then instantiated with confidence intervals obtained from distinct econometric estimations. Then, the problem is solved through the use of a reference point approach that allows accounting for the decision maker's preferences by considering a set of weight vectors that can be used to express his/her preferences regarding the importance that should be given to each objective function. The results provide information on how the improvement of one objective might affect the remaining objectives. Furthermore, the student's profile corresponding to each scrutinized solution is also made available. Overall, the results claim that bullying is the most affected objective, highlighting the need to foster antibullying education policies in Spanish schools, according to PISA 2015 data. Finally, some educational polices are suggested in order to enhance students’ well‐being.

On Using Physical Programming for Multi-Domain SFC Placement With Limited Visibility

Service Function Chaining (SFC) is a networking concept by which traffic is steered through a set of ordered functions composing an end-to-end service. It represents one of the facilitating technologies for 5G, and is enabled by the Network Function Virtualization (NFV) and Software Defined Networks (SDN) paradigms. In the multi-domain context, SFC placement faces new challenges related to the lack of visibility on the local domain's networks. Indeed, the domain operators are often reluctant to unveil details on their topology to external parties. Furthermore, the new 5G use cases introduce new requirements for services such as end-to-end latency and a minimal guaranteed bandwidth that the placement process needs to optimize simultaneously. In this work, we propose a centralized framework that allows SFC partitioning and embedding over multiple domains with a limited visibility over the global infrastructure. We model the multi-objective SFC placement problem using the Physical Programming method, which allows the expression of the Decision Maker's preferences through meaningful parameters, and propose an exact algorithm as well as a scalable heuristic solution. We then perform an extensive evaluation of the framework as well as the proposed algorithms. The results demonstrate our solution's effectiveness with a limited visibility on the network.

Foundations of ambiguity models under symmetry: α-MEU and smooth ambiguity

The α-MEU model and the smooth ambiguity model are two popular models in decision making under ambiguity. However, the axiomatic foundations of these two models are not completely understood. We provide axiomatic foundations of these models in a symmetric setting with a product state space S∞. This setting allows marginals over S to be linked behaviorally with (limiting frequency) events. Bets on such events are shown to reveal the i.i.d. measures that are relevant for the decision maker's preferences and appear in the representations. By characterizing both models within a common framework, it becomes possible to better compare and relate them.

A NOVEL APPROACH FOR THE SOLUTION OF GENERALIZED FUZZY ASSIGNMENT PROBLEM

In engineering and other fields, generalised assignment problem plays a vital role. It has widespread applications in routing problems, knapsack problems and other complicated models. In real world problems, the available data may not be known with certainty. Hence to model and solve practical problems, we must deal with uncertainty and vagueness. Fuzzy sets play a vital role to tackle these uncertainty and vagueness. The generalised fuzzy assignment problem became popular and has gained its importance too. In this paper, we consider a generalised fuzzy assignment problem which has restrictions both on tasks and on persons with respect to his/her efficiency/qualification. We propose a unique approach for the solution of generalised fuzzy assignment problem with restrictions without converting the problem to a corresponding crisp form. Costs for handling jth task by the ith person are taken to be trapezoidal fuzzy numbers. The trapezoidal fuzzy numbers are first represented in its parametric form. In view of the decision maker's preference, a new ranking method and arithmetic operations are used to bring a desirable solution. A numerical example is given to illustrate the proposed method.

Model-Rich Approaches to Eliciting Weak Preferences: Evidence from a Multi-Valued Choice Experiment

This paper contributes to the elicitation of a decision maker's strict preferences and their possible indifference or incomparability/indecisiveness. Every subject in both treatments of an incentivized lab experiment could choose multiple alternatives from each of the 50 distinct menus of popular gift-card pairs that they saw. Subjects in the non-forced-choice treatment could, in addition, avoid/delay making an active choice at those menus. Applying a non-parametric optimization method on data collected from 273 subjects, we find that nearly 60% of them are well-approximated by an indifference-permitting model of complete- or incomplete-preference maximization. Most recovered preferences are unique, have a non-trivial indifference part and, where relevant, a distinct indecisiveness part. The two kinds of distinctions between indifference and indecisiveness uncovered by this method are theory-guided and documented empirically for the first time. These findings suggest that accounting for possible indifferences and/or incomparabilities in the data-collection process and analysis can be useful in eliciting transitive weak preferences. Two aspects of the experimental design, finally, allow for interpreting an additional 10% of subjects as revealing a systematic preference for randomization or satisficing.

Scenario planning combined with probabilities as a risk management tool - analysis of pros and cons

Scenario planning and probabilities are often very helpful in uncertain decision problems. In the paper, we examine the correctness of combining probabilities with scenario planning in risk management. We make a literature review and analyse diverse decision problems differing from each other with regard to their nature, the decision maker's objectives and preferences. We explore competition, quality, innovation, resource allocation, inventory and banking issues. The illustrative examples concern both one-criterion and multi-criteria decision problems. We get to the point that scenario planning is an unquestionable support for risk management. Nevertheless, the use of probabilities as an accompanying tool may be necessary and justified merely in some specific cases.