TEA-IS: A hybrid DEA-TOPSIS approach for assessing performance and synergy in Chinese health care

2023 | Decision Support Systems | Citations: 0

Authors: Antunes, Jorge; Hadi-Vencheh, Abdollah; Jamshidi, Ali; Tan, Yong; Wanke, Peter

Abstract: This paper presents an assessment of the Chinese healthcare system in 31 provinc ... Expand

Abstract: This paper presents an assessment of the Chinese healthcare system in 31 provinces for a 10-year period in light of relevant physical and human resource variables. First, a novel TEA-IS (Trigonometric Envelopment Analysis for Ideal Solutions) model is developed to assess healthcare efficiency at the province level. Machine learning methods are also employed to predict high-low performance and the synergistic Chinese healthcare province in terms of contextual variables. The results indicate that synergy has played a pivotal role in the Chinese healthcare systems, not only by triggering higher performance levels due to the progressive adoption of best practices over the course of time, but also by being closely related to different socioeconomic and demographic variables, such as the illiteracy rate. It is possible to claim that healthcare performance has remained stable in China over the past two decades, performance and synergy at the province level are still heterogeneous. Collapse

Topics: health information system IT productivity human resource management service quality productivity

Methods: TOPSIS trade-off analysis method machine learning artificial neural network literature study

A collaborative decision support system for multi-criteria automatic clustering

2022 | Decision Support Systems | Citations: 0

Authors: Jabbari, Mona; Sheikh, Shaya; Rabiee, Meysam; Oztekin, Asil

Abstract: Automatic clustering is a challenging problem, especially when the decision-make ... Expand

Abstract: Automatic clustering is a challenging problem, especially when the decision-maker has little or no information about the nature of the dataset and the criteria of interest. There is a lack of generalizability in the current validity indexes (VI) for automatic clustering algorithms, as each considers a limited number of objectives and mostly ignores the other aspects of clustering validation. The proposed framework benefits from collaboration among selected evolutionary algorithms. A mixed-integer non-linear programming model is developed, and a framework is proposed for a six-step decision support system to solve it. The decision-maker (DM) selects the quantitative (primary) VIs and the evolutionary algorithms. Given DM’s knowledge on the dataset and VIs, DM can incorporate qualitative (secondary) VIs. DM determines the quality threshold for each VI and runs the evolutionary algorithms separately. The DSS then saves the best obtained value of VIs in order to prepare the input necessary to construct the aggregated function. Based on the selected primary VIs, a new normalized aggregated function is developed and solved repeatedly using the randomly selected or predefined weights of importance. Eventually, DM employs a proper DEA model to define the final clustering output among all possible solutions. Given multiple efficient solutions, the best-worst method and a multi-criteria decision-making approach are applied to find the final output. The applicability of the proposed approach is illustrated on a synthetic and two secondary datasets, and the result at each step is discussed in detail. Collapse

Topics: decision support system information exchange decision support

Methods: computational algorithm cluster analysis mathematical model DBSCAN descriptive statistic

Improved Shapley Values Based on Players’ Least Square Contributions and Their Applications in the Collaborative Profit Sharing of the Rural E-commerce

2022 | Group Decision and Negotiation | Citations: 0

Authors: Liu, Jia-Cai; Li, Deng-Feng

Abstract: In this paper, we introduce several improved Shapley values for cooperative tran ... Expand

Abstract: In this paper, we introduce several improved Shapley values for cooperative transferable utility (TU) games depending on the players’ least square contributions instead of their marginal contributions. This work is mainly enlightened by the Shapley value and the player’s productivity-based excess (usually called the excess of the player). Based on the player’s contribution-based excess, two quadratic programming models for obtaining the players’ (weighted) least square contributions are constructed. The efficient weighted Shapley-like value proposed in this paper as an extension of the Shapley value can be characterized by four independent axioms such as the symmetry, the efficiency, the additivity, and the quasi-null player, which are almost similar to the Shapley value’s four axioms except that the anonymity is replaced with the quasi-null player. The four axioms are proven to uniquely determine the efficient weighted Shapley-like value. Finally, the advantages of the proposed values are illustrated with a real case about the collaborative profit sharing of the rural e-commerce. Collapse

Topics: shapley additive explanation productivity electronic commerce anonymity

Methods: non linear optimization weighted least square data imputation

Managing Consistency and Consensus Issues in Group Decision-Making with Self-Confident Additive Preference Relations and Without Feedback: A Nonlinear Optimization Method

2022 | Group Decision and Negotiation | Citations: 0

Authors: Liu, Wenqi; Zhang, Hengjie; Liang, Haiming; Li, Cong-cong; Dong, Yucheng

Abstract: Preference relations are one of the most common tools used to represent decision ... Expand

Abstract: Preference relations are one of the most common tools used to represent decision makers’ preferences. Self-confident preference relations, whose preference elements include self-confidence levels and preference values, is a new and effective tool that can be used to express decision makers’ self-confidence levels in the preference relation. When using self-confident preference relations in group decision-making problems, the individual consistency and group consensus are two fundamental issues. To deal with these issues, a consistency index is proposed to measure the consistency level of a self-confident additive preference relation. Meanwhile, a consensus index is presented to measure the group consensus level of self-confident additive preference relations. Subsequently, two nonlinear optimization methods are developed to manage consistency and consensus issues for group decision-making within the context of consensus reaching without feedback. The first model is used to derive self-confident additive preference relations with acceptable consistency, and the second optimization model is employed to promote consensus reaching that simultaneously manage individual consistency. A hypothetical application with comparative analysis is conducted to show the usability and validity of the proposed methods. Collapse

Topics: decision making usability innovation management

Methods: optimization model cross sectional research non linear optimization

A Nonlinear Optimization Model of Advertising Budget Allocation across Multiple Digital Media Channels

2022 | HICSS | Citations: 0

Authors: Park, Sung-Hyuk; Lee, Minhyung; Kim, Kitae; Shin, Dongwook

Abstract: The goal of advertisers in the digital marketing industry is to optimize their a ... Expand

Abstract: The goal of advertisers in the digital marketing industry is to optimize their advertising budgets. Such a budget allocation problem plays a key role in maximizing advertising performance from different marketing channels under planned advertising investment. This study aimed to design a budgetperformance-based nonlinear programming model to find an optimized solution for the advertising budget allocation problem. The empirical analysis results of a leading e-business company’s advertising performance data show that the proposed non-LP model generates an optimized solution. The proposed model allows marketers to simulate expected advertising returns, such as conversions or revenues from different channels within their budget constraints. Collapse

Topics: advertising management marketing management online advertising Python electronic business

Methods: optimization model non linear optimization

An Integrated Group Decision-Making Method with Hesitant Qualitative Information Based on DEA Cross-Efficiency and Priority Aggregation for Evaluating Factors Affecting a Resilient City

2022 | Group Decision and Negotiation | Citations: 0

Authors: Wu, Peng; Liu, Jinpei; Zhou, Ligang; Chen, Huayou

Abstract: A city is a carrier of the development of human society; the higher the resilien ... Expand

Abstract: A city is a carrier of the development of human society; the higher the resilience of a city is, the better it can resist invasion from the outside. Therefore, the evaluation factors affecting resilient cities are of great practical significance in the study of resilient cities. The assessment method with hesitant fuzzy linguistic preference relation (HFLPR) has recently widely used in evaluation problems. However, some existing priority vector solving methods in the assessment method often ignore much decision-making information. Therefore, the aim of this paper is to present a new priority vector solving method for HFLPR by integrating data envelopment analysis (DEA) cross-efficiency and a distance-based priority aggregation (DPA) model. The innovation of this method contains: (1) DEA model is introduced to solve the priority vector of linguistic preference relation (LPR); (2) a DPA model is constructed to obtain the HFLPR’s priority vector, which can avoid the loss of decision-making information. For completely additive consistent LPR and incompletely additive consistent LPR, an output-oriented DEA model and DEA cross-efficiency model are introduced to derive its priority vector, respectively. An HFLPR is viewed as being composed of many LPRs, a DPA model is constructed based on the L2 norm to find the priority vector of HFLPR that minimizes the distance among the LPRs’ priority vectors. Based on the above, an integrated group decision-making method is proposed and then applied to an illustrative example of the evaluation factors affecting resilient cities to show its performance and advantages by comparing with the existing methods. Collapse

Topics: decision making Microsoft Windows innovation management

Methods: optimization model non linear optimization case study computational algorithm linear optimization

Consumer preference analysis based on text comments and ratings: A multi-attribute decision-making perspective

2022 | Information & Management | Citations: 0

Authors: Zhu, Bin; Guo, Dingfei; Ren, Long

Abstract: With the popularity of social media, extracting consumer preferences from online ... Expand

Abstract: With the popularity of social media, extracting consumer preferences from online consumer-generated content is of vital importance for product/service providers to develop tailored marketing strategies. However, existing approaches face difficulties analyzing consumer preferences over different attributes of alternatives (restaurants, hotels, etc.), which hinders product/service providers from comprehensively understanding consumer choice decisions. To address this issue, we solve for the consumer preferences over the attributes represented by attribute weights based on consumers’ historical data, including text comments and overall ratings. Specifically, for each comment and a corresponding rating, we first employ sentiment analysis to calculate values of the attributes, and then develop a quadratic programming model to solve for the weights. Based on a stream of a consumer’s text comments and overall ratings, we can correspondingly obtain a stream of weights indexed by the comment time. We then model this stream of weights as hesitant judgments and employ a hesitant multiplicative programming method to solve for the final weights that fit the consumer’s preferences over attributes at the highest satisficing level. In the application of recommendation, our approach not only provides insights into the consumer’s preferences but also has higher prediction power compared with some state-of-the-art methods. Collapse

Topics: recommender system artificial intelligence

Methods: sentiment analysis optimization model word embedding non linear optimization historical review

Theories: theory of bounded rationality

Incentive Compatible Cost Sharing of a Coalition Initiative with Probabilistic Inspection and Penalties for Misrepresentation

2020 | Group Decision and Negotiation | Citations: 0

Authors: Caballero, William N.; Lunday, Brian J.; Ahner, Darryl K.

Abstract: This research proposes cost sharing mechanisms such that payments for a coalitio ... Expand

Abstract: This research proposes cost sharing mechanisms such that payments for a coalition initiative are allocated among players based on their honest valuations of the initiative, probabilistic inspection efforts, and deception penalties. Specifically, we develop a set of multiobjective, nonlinear optimization problem formulations that alternatively impose Bayesian incentive compatible, strategyproof, or group strategyproof mechanisms with generalized cost sharing and penalty functions that can be tailored to specific applications. Any feasible solution to these problems corresponds to a Bayesian game with stochastic payoffs wherein a collectively honest declaration is a Bayes–Nash equilibrium, a Nash equilibrium in dominant strategies, or a collusion resistant Nash equilibrium, respectively, and wherein an optimal solution considers the central authority’s relative priorities between inspection and penalization. In addition to this general framework, we introduce special cases having specific cost sharing and penalty functions such that the set of mechanisms are budget-balanced-in-equilibrium and proportional by design. The convexity of the resulting mathematical programs are examined, and formulation size reductions due to constraint redundancy analyses are presented. The Pareto fronts associated with each multiobjective optimization problem are assessed, as are computer memory limitations. Finally, an experiment considers the clustering of available valuations and the player probability distributions over them to examine their effects. Collapse

Topics: public good privacy

Methods: optimization model cluster analysis experiment computational algorithm operations research

Minimizing the data quality problem of information systems: A process-based method

2020 | Decision Support Systems | Citations: 0

Authors: Liu, Qi; Feng, Gengzhong; Zhao, Xi; Wang, Wenlong

Abstract: The low quality of data in information systems poses enormous risks to business ... Expand

Abstract: The low quality of data in information systems poses enormous risks to business operations and decision making. In this paper, a single-period resource allocation problem for controlling the information system's data quality problem is considered. We develop a Data-Quality-Petri net to capture the process through which data quality problem generates, propagates, and accumulates in the information system. The net considers not only the factors leading to the production of the data quality problem by the data operation nodes and the data flow structure, but also the data transfer ratio of the nodes. Then, we propose a nonlinear programming optimization model with control resource constraints. The result of the model provides an optimal strategy to allocate re­ sources for minimizing the expected data quality problem of an information system. Further, we examine the impact of the data flow structure on optimal resource allocation. The result shows that the optimal resource input level for a data operation node is proportional to its potential for downstream propagation. A warehouse management system of an e-commerce company is utilized to illustrate the model. Our study provides a method for data managers to control the information system's data quality problem by employing a process perspective. Collapse

Topics: data quality resource allocation electronic commerce quality management database system

Methods: Petri net modeling language optimization model sensitivity analysis business process modeling

A Multivariate Minimum Cost Consensus Model for Negotiations of Holdout Demolition

2020 | Group Decision and Negotiation | Citations: 0

Authors: Liu, Yong; Zhou, Ting; Forrest, Jeffrey Yi-Lin

Abstract: Incidences of holdouts, as group events, have appeared most frequently in many c ... Expand

Abstract: Incidences of holdouts, as group events, have appeared most frequently in many countries. The underlying cause of these occurrences of high frequency has been closely related to multiple objectives or various conflicts of interest of stakeholders. Each negotiation of holdout demolition represents a typical group consensus problem with the outcome greatly influenced by multiple objectives or variables of the decision makers. In order to effectively deal with such difficult problems, we construct a multivariate, minimum cost consensus model based on interval number programming constrained with random chances by jointly employing various approaches, such as minimum cost consensus model, multivariate planning, stochastic opportunity constrained programming and interval numbers. After the theoretical development, this paper employs the established method to solve the problem of holdout demolition of particular town A. Collapse

Topics: decision making systems design logistics management

Methods: optimization model linear optimization survey non linear optimization factor analysis

Theories: theory of economic development decision theory