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A goal programming model for two-stage COVID19 test sampling centers location-allocation problem

CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2023年第1期31卷 1-20页

作者： Kuvvetli, Yusuf Cukurova Univ Dept Ind Engn Balcali Campus TR-01330 Adana Turkey

The COVID19 virus, which first appeared in Wuhan, China, and has become a pandemic in a short time, has threatened the health system in many countries and put it into a bottleneck. Simultaneously, the second wave's expectation spread it necessary to plan the health services correctly. In this study, a location-allocation problem in the two-echelon system, which considers different test sampling alternatives, is examined to obtain test sampling centers' location-allocation. The problem is modeled as a goal programming model to create a network that tests samples at a minimum total distance, establishes a minimum number of test sampling centers, and reaches the distance of PCR test laboratories at minimum total distances. The proposed model is applied as a case study for the two cities located in Turkey, and the obtained locations and inventory levels of each location are presented. Besides, different scenarios are examined to understand the structure of the model. As a result, only testing in hospitals will increase the risk of contamination. Since testing at all points will not be possible administratively, it will be ensured that the most appropriate location-allocation decisions are taken by considering all the proposed model's objectives.

关键词： Location-allocation problem Test sampling center location goal programming model COVID19 pandemic

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Analytical goal programming model for Optimal Restoration of Distribution Systems

Analytical Goal Programming Model for Optimal Restoration of...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Ferreira, Gustavo D. Gazzana, Daniel S. Silva, Rochele A. Bretas, Arturo S. Bettiol, Arlan L. Carniato, Antnio Passos, Luis F. do N. Homma, Rafael Z. Molina, Fernando H. Univ Fed Rio Grande do Sul Dept Elect Engn Porto Alegre RS Brazil Neo Domino Consulting & Res Florianopolis SC Brazil Cent Eletr Santa Catarina SA Florianopolis SC Brazil

ISBN: (纸本)9781467380409

This paper presents a optimization approach for solving the distribution systems' restoration problem. The problem is formulated as a goal programming model aiming to establish the combination of devices to be operated in order to maximize the number of customers restored in the post-fault period as well as to minimize the time required by the network reconfiguration process. This approach considers devices with arbitrary switching times, allowing the modeling of operation of automatic and manual switches. The viability of restoration is ensured through operational and topological constraints, including the system's voltage profile, spare capacity and the radial post-reconfiguration topology. A linear version of the power flow in terms of nodal injection of current is proposed in order to take into account such constraints. The equations are formulated as functions of the state of available sectionalizing switches in the feeder. A case study using a real distribution feeder is used to demonstrate the technique and the optimal solutions of the model obtained in reduced processing times.

关键词： goal programming model Restoration of distribution systems

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Analytical goal programming model for Optimal Restoration of Distribution Systems

Analytical Goal Programming Model for Optimal Restoration of...

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IEEE Power and Energy Society General Meeting

作者： Gustavo D. Ferreira Daniel S. Gazzana Rochele A. Silva Arturo S. Bretas Arlan L. Bettiol Antonio Carniato Luis F. do N. Passos Rafael Z. Homma Fernando H. Molina Department of Electrical Engineering Federal University of Rio Grande do Sul Neo Domino Consulting and Research Centrais Eletricas de Santa Catarina S.A

ISBN: (纸本)9781467380416

关键词： goal programming model Restoration of distribution systems dispensing system goal programming restoration Power flow distribution feeder optimal solution Sectionalizing switches

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goal programming models for incomplete interval additive reciprocal preference relations with permutations

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GRANULAR COMPUTING 2020年第3期5卷 373-386页

作者： Huang, Mao-Jie Liu, Fang Peng, Ya-Nan Yu, Qin Guangxi Univ Sch Math & Informat Sci Nanning 530004 Guangxi Peoples R China

Within the framework of the fuzzy analytic hierarchy process, the importance of alternatives could be expressed as interval-valued comparison matrices to model some uncertainty experienced by decision makers (DMs). Owing to the complexity of the considered problem and the lack of DMs' knowledge, the given interval-valued comparison matrix may be incomplete. It is of importance to develop mathematical models for incomplete interval-valued comparison matrices, so that the missing entries can be estimated. In this study, it is considered that a missing value may be one of the bounds of interval-valued entries. Acceptable incomplete interval additive reciprocal preference relations (IARPRs) are redefined and the existing drawback is overcome. By considering the random behavior of decision makers in comparing alternatives, the novel definitions of multiplicative and additive approximate consistency for incomplete IARPRs are introduced. Then, we propose two goal programming models to estimate the missing values of incomplete IARPRs. The permutations of alternatives are incorporated into the proposed mathematical models. It is found that when the permutations of alternatives are different, the estimated preference values could be different. Some numerical results are reported to illustrate the given algorithms for solving decision-making problems with incomplete IARPRs. The observations reveal that the proposed mathematical models can be used to capture the randomness experienced by decision makers in comparing alternatives.

关键词： Incomplete interval additive reciprocal preference relation (incomplete IARPR) goal programming model Approximate consistency Analytic hierarchy process (AHP) Permutation

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Two-way referral cooperative hospital selection with uncertain information: A two-sided matching decision-making approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期183卷

作者： Huang, Rui-lu Wang, Xiao-kang Hou, Wen-hui Chen, Zi-yu Wang, Yi-ting Wang, Jian-qiang Cent South Univ Sch Business Changsha 410083 Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Hunan Univ Finance & Econ Sch Finance Changsha 410205 Peoples R China

To better rationalize the allocation of medical resources and improve the efficiency of medical resource utilization, researchers have focused on the problem of two-way referral cooperation among hospitals. The selection of an appropriate referral hospital constitutes a crucial aspect of a two-way referral system, which affects the quality of patient treatment and the financial outcome of hospital operations. Within this context, this study regards the collaborative referral hospital selection problem as a two-sided matching decision-making process. Given the hesitance and bounded rationality of decision-makers, a novel two-sided matching framework is developed that integrates hesitant fuzzy linguistic term sets and regret theory to more accurately reflect realworld conditions. Specifically, evaluation information is converted to hesitant fuzzy linguistic term sets via context-free grammar, and new utility functions are introduced to calculate the utility value, regret utility, and rejoice utility through the hesitant degree and score function. Furthermore, an optimization model for calculating the criteria weight is established based on the Euclidean distance and maximization deviation method. Ultimately, the matching satisfaction degree is determined, and a biobjective programming model that maximizes the overall matching satisfaction degree is formulated and solved. The results from experiments and analysis indicate that the proposed framework produces optimal and stable matching solutions, thereby providing a useful reference for hospitals seeking satisfactory referral partners. Moreover, this model could be extended to address large-scale two-sided matching problems or to refine the referral hospital selection problem by considering individual patient needs.

关键词： Two-sided matching decision -making Hesitant fuzzy linguistic term sets Regret theory goal programming model

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Additive consistency of q-rung orthopair fuzzy preference relations with application to risk analysis

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期44卷 6939-6955页

作者： Zhang, Zhenyu Guo, Jian Zhang, Huirong Qin, Yong Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China Shandong Management Univ Sch Lab Relationship Jinan Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China

Preference relations have been extended to q-rung orthopair fuzzy environment, and the q-rung orthopair fuzzy preference relations (q-ROFPRs) with additive consistency are defined. Then, the concept of normalized q-rung orthopair fuzzy weight vector (q-ROFWV) is proposed, and the transformation method of constructing q-ROFPR with additive consistency is given. To obtain the weight vector of any q-ROFPRs, a goal programming model to minimize the deviation of the q-ROFPRs from the constructed additive consistent q-ROFPRs is established. The q-rung orthopair fuzzy weighted quadratic (q-ROFWQ) operator is selected to aggregate multiple q-ROFPRs, efficiently handling extreme values and satisfying monotonicity about the order relation. Further, a group decision-making (GDM) method is developed by combining the q-ROFWQ operator and the goal programming model. Finally, the practicality and feasibility of the developed GDM method are demonstrated by an example of rail bogie crucial component identification.

关键词： q-rung orthopair fuzzy preference relation (q-ROFPR) goal programming model q-rung orthopair fuzzy weighted quadratic (q-ROFWQ) operator group decision making

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Incomplete interval-valued probabilistic uncertain linguistic preference relation in group decision making

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 243卷

作者： Wu, Shouwen Zhang, Guoquan Jilin Univ Sch Business & Management Changchun 130012 Peoples R China

In group decision making (GDM), due to complexity of various factors, decision makers (DMs) often provide incomplete preference relations (PRs) in preference matrix. Interval-valued probabilistic uncertain linguistic term set (IVPULTS) is a flexible and accurate tool to depict evaluation information of experts. In this paper, we mainly propose a new concept pertaining to interval-valued probabilistic uncertain linguistic preference relation (IVPULPR) that applies the IVPULTS to preference relations. Firstly, some new basic theoretical concepts of IVPULTS are developed including ordered IVPULTS, normalization method and new expectation function. Secondly, we establish several goal programming models to estimate the unknown elements in incomplete IVPULPR and propose the expected additive consistency of IVPULPR. To improve the consistency level, two optimization models are constructed based on the idea of minimum adjustment. Thirdly, we derive the experts' weights in terms of information uncertainty, where a new method to measure information uncertainty of IVPULTS is proposed. For the sake of improving group consensus, we construct a group consensus index (GCI) and two optimization models depending on the adjustment mechanism of expert weight. Finally, a complete GDM framework with incomplete IVPULPR is devised based on the analysis of IVPULPR consistency and group consensus. Through an experiment analysis by using an UCI dataset, we find that the proposed GDM model can not only precisely express fuzzy preference information of DMs, but also ensure achievement of acceptable consistency and group consensus under the condition of not changing the initial preference as much as possible.

关键词： Group decision making Incomplete preference relation Consistency Group consensus goal programming model

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Dynamic Reference Point-Oriented Consensus Mechanism in Linguistic Distribution Group Decision Making Restricted by Quantum Integration of Information

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GROUP DECISION AND NEGOTIATION 2022年第2期31卷 491-528页

作者： Tan, Xiao Zhu, Jianjun Wu, Tong Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Jiangsu Peoples R China

We present a consensus improvement mechanism based on prospect theory and quantum probability theory (QPT) that enables the manifestation of irrational and uncertain behaviors of decision makers (DMs) in linguistic distribution group decision making. In this framework, the DMs pursue the possibility of working with different partial agreements on prospect values. Considering that the reference information should be comprehensive and accurate as it guides information modification and affects consensus efficiency, objective and subjective information is integrated to obtain the information. Several studies have verified that the interference effect will occur when the brain beliefs flow towards the different decision classification paths. To address this problem, QPT is introduced into the information integration and the optimized value of the interference term can be acquired by the designed multi-objective programming model based on the maximum individual utility. Finally, as the reference point changes during the preference adjustment process, a dynamic reference point-oriented consensus model is constructed to obtain the optimized modification. A case study is performed on the emergency plan for the selection of designated hospitals, and comparative analyses are performed to demonstrate the feasibility and advantages of the proposed model. Several important insights are offered to simulate the most likely possibility of consciousness flowing into different decision classifications for DMs and moderators.

关键词： Group decision and negotiation Prospect theory Quantum probability theory Linguistic distribution assessment goal programming model Reference point rules

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A multiobjective optimization approach for threshold determination in extreme value analysis for financial time series

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COMPUTATIONAL MANAGEMENT SCIENCE 2024年第1期21卷 7-7页

作者： Chu, Carlin C. F. Li, Simon S. W. Hang Seng Univ Hong Kong Dept Comp Hong Kong Peoples R China Hang Seng Univ Hong Kong Big Data Intelligence Ctr Hong Kong Peoples R China

The literature on the extreme value theory threshold optimization problem for multiple time series analysis does not consider determining a single optimal tail probability for all marginal distributions. With multiple tail probabilities, their discrepancy results in a differing number of exceedances, which may favour a particular marginal series. In this study, we propose a single optimal tail probability by integrating trade-offs among multiple time series within an MOO framework. Mathematically, our approach links the peaks-over-threshold technique and goal programming technique by developing a set of regression functions, which represent continuous paths of possible tail areas for multiple time series, and we formulate them at the desired levels within a multiobjective optimization framework. The optimal solution is found as the minimum Chebyshev variant weighted value. Our approach advances the development of the peaks-over-threshold method by considering the characteristics of a group of time series collectively instead of independently. The proposed optimal tail probability can be considered an optimal reference point for practical risk investment portfolio analysis that employs an identical tail size across multiple time series data. The daily log returns of four U.S. stock market indices, namely, S&P 500, NASDAQ Composite, NYSE Composite, and Russell 2000, from 1 July 1992 to 30 June 2022 are studied empirically.

关键词： EVT Generalized pareto distribution goal programming model Multiple criteria decision analysis Peaks over threshold

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Fuzzy best-worst method based on generalized interval-valued trapezoidal fuzzy numbers for multi-criteria decision-making

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INFORMATION SCIENCES 2021年 573卷 493-518页

作者： Wan, Shuping Dong, Jiuying Chen, Shyi-Ming Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330013 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Sch Stat Nanchang 330013 Jiangxi Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

This paper proposes a fuzzy best-worst method (BWM), called the GITrF BWM, based on generalized interval-valued trapezoidal fuzzy (GITrF) numbers (GITrFNs) for multi criteria decision-making (MCDM). The reference comparisons between criteria are represented by GITrFNs and the weights of criteria are also taken the form of GITrFNs. The concept of normalized GITrF weight vector is proposed and a new graded mean integration representation (GMIR) of GITrFN is given. A goal programming model is built to obtain the optimal normalized GITrF weights of criteria. Furthermore, the GITrF consistency index and the GITrF consistency ratio are proposed. The GMIR of the GITrF consistency ratio is calculated to measure the acceptable consistency of all the reference comparisons between criteria. For the unacceptable consistent reference comparisons, we propose an approach to improve the consistency of reference comparisons between criteria. Finally, a GITrF BWM is proposed for MCDM. Three real examples are analyzed to illustrate the proposed GITrF BWM. The comparison analyses show that the proposed GITrF BWM outperforms the existing methods for MCDM in GITrF environments. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Best-worst method Consistency index Generalized interval-valued trapezoidal fuzzy number goal programming model Multi-criteria decision-making

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