Article
Computer Science, Artificial Intelligence
Ying-Ming Wang, Xiang Jia, Hui-Hui Song, Luis Martinez
Summary: This paper proposes a multi-attribute group decision making (MAGDM) method based on regret theory, which considers both the psychological behaviors of decision makers and the consistency of assessments. By defining the regret preference relation and devising four algorithms, the consistency of assessments is improved. Two weighting determination models are built to calculate the weights of attributes and decision makers, and alternatives are ranked based on their overall synthetic values. The application of the proposed method in an emergency assistance area selection problem verifies its feasibility and effectiveness.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Jie Long, Haiming Liang, Lei Gao, Zhaoxia Guo, Yucheng Dong
Summary: This study proposes a novel consensus reaching method for multi-attribute group decision making, aiming to improve the consensus level among individuals with minimum adjustments. By considering individuals' reference dependence and loss aversion behaviors, the method can decrease individual adjustments and promote the efficiency of consensus reaching. The proposed method can be applied to practical group decision-making scenarios without conflicts of interests.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Zhaohui Qi, Hui Li, Kai Zhang, Jianhua Dai
Summary: In recent years, an increasing number of researchers have focused on how to leverage limited information to make more reasonable decisions, thereby reducing the decision-making risk for decision-makers. Three-way decision, as a method for risky decision-making, offers a new approach to address this challenge and has gained significant attention. This paper proposes a novel three-way utility decision model oriented to attribute fuzzy concept in multi-attribute environments, based on the utility theory.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Feng Wang
Summary: The study proposes a preference degree-based algorithm to rank triangular fuzzy numbers, addressing the issues of ranking TFNs and selecting the best alternative in complex multi-attribute group decision making. By utilizing the utility ratings of alternatives and a control parameter, the attribute weights are determined, offering a new method to solve the triangular fuzzy MAGDM problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Mathematics, Applied
Tao Li, Liyuan Zhang
Summary: This paper addresses the multiple-attribute group decision-making problem using intuitionistic multiplicative linguistic variables (IMLVs). New operational laws and aggregation operators for IMLVs are introduced. Consistent IMLPR is defined, and a mathematical programming model is built to obtain the priority weight vector. An automatic convergent algorithm is designed to repair inconsistent IMLPR. Additionally, a model is established to estimate unknown values of an incomplete IMLPR. The proposed method is applied to practical problems and compared with existing approaches.
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Computer Science, Information Systems
Haibo Jiang, Bao Qing Hu
Summary: The paper proposes a novel three-way group investment decision (3WGID) model under intuitionistic fuzzy multi attribute group decision-making (MAGDM) environment. The model aims to deepen the understanding of three-way decision (3WD) and expand its applications in profit-based investment decision making problems by calculating relative cost and revenue functions, integrating aggregated functions, and determining overall profit functions of alternatives through collective opinions of multiple experts. The effectiveness and feasibility of the 3WGID model are demonstrated through a coalfield investment case study.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Yue He, Zeshui Xu
Summary: This paper provides an overview of the rapidly developing uncertain group decision-making (UGDM) methodology, which is closely related to the evolution of fuzzy set theory and linguistic term set theory. The methods are classified into different categories and connected to the development of related theories. It is found that the progress of UGDM methodology and information expressions mutually influence each other.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2021)
Article
Computer Science, Information Systems
Kifayat Ullah, Muhammad Naeem, Abrar Hussain, Muhammad Waqas, Izatmand Haleemzai
Summary: In this article, the concept of power operator is introduced to mitigate the influence of negative information on the decision-making process. The power aggregation tools are robust mathematical operators that facilitate mutual support among input arguments in decision-making. Frank aggregation expressions are reliable and updated versions of triangular norms used for handling complex information. Picture fuzzy set (PFS) is an extended version of fuzzy sets and intuitionistic fuzzy sets, with four terms representing an object's positive grade, abstained grade, negative grade, and refusal grade. New methodologies based on Frank aggregation tools for PF information, such as picture fuzzy frank power average (PFFPA) and picture fuzzy frank power geometric (PFFPG) operators, are proposed. The reliability and performance of these approaches are illustrated through a multi-attribute group decision-making (MAGDM) technique and a case study.
Article
Computer Science, Artificial Intelligence
Sha Fan, Haiming Liang, Yucheng Dong, Witold Pedrycz
Summary: This paper proposes a method based on personalized individual semantics for multi-attribute group decision making (MAGDM) model with flexible linguistic expression. The method takes into account the preferences of individuals and the impact of personalized semantics, and performs collective ranking for individual rankings.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics
Shahzad Faizi, Wojciech Salabun, Nisbha Shaheen, Atiq ur Rehman, Jaroslaw Watrobski
Summary: The paper introduces the hesitant 2-tuple linguistic set for handling uncertain data, along with operational laws and aggregation operators to merge information from decision makers. These operational laws and operators aid in dealing with complex choice situations and capturing diverse decision maker experiences.
Article
Computer Science, Artificial Intelligence
Salih Berkan Aydemir, Sevcan Yilmaz Gunduz
Summary: This paper introduces neutrality average and neutrality geometric aggregation operators based on power aggregation (PA), and proposes a general score function for q-rung orthopair fuzzy sets (q-ROFSs). The PA operators reduce the impact of excessively high or low arguments and emphasize interrelationships between attributes. The proposed neutrality aggregation operator provides reliable results by considering neutrality among decision-makers, and q-ROFSs offer a wider evaluation for decision-makers.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Automation & Control Systems
Muhammad Rizwan Khan, Kifayat Ullah, Hanen Karamti, Qaisar Khan, Tahir Mahmood
Summary: This article aims to develop the Aczel-Alsina operational laws based on power aggregation operators (PAOs) for the q-Rung orthopair fuzzy set (FS) (q-ROFS) framework. The q-ROFSs can adjust the information region by fluctuating the restriction ≥ 1. PAOs have the advantage of vanishing the influence of awkward data from the final results.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Mathematics
Mei Cai, Yuanyuan Hong
Summary: This paper proposes an asymmetric probabilistic linguistic cloud TOPSIS (ASPLC-TOPSIS) method for multi-attribute group decision making. By introducing cloud model and decision maker trust network, this method can effectively handle the fuzziness and randomness of decision maker preference, improving the accuracy and reliability of the decision results.
Article
Computer Science, Interdisciplinary Applications
Wei Liu, Yuhong Wang
Summary: This paper aims to provide a novel approach for spatially aggregating decision maker preference information. The optimal aggregation method, based on spatial Steiner-Weber point, effectively aggregates the preference information of group members and optimizes group preference. The method consists of three key elements: the spatial mapping of group preference, the spatial optimal aggregation model of group preference, and the use of the plant growth simulation algorithm to find optimal aggregation points. By comparing with classical group preference aggregation methods, the effectiveness and rationality of this approach are verified.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Shuli Yan, Yingying Zeng, Na Zhang
Summary: A multi-attribute quantum group decision-making method considering decision-maker's risk attitude is developed to address the entanglement issue among decision-makers' opinions. The method utilizes interval-valued intuitionistic fuzzy number to represent decision information in uncertain environment, derives the interference entanglement behavior using quantum probability, and reflects decision-makers' irrational risk attitude through comprehensive prospect value for alternative ranking.
Article
Engineering, Industrial
Yeu-Shiang Huang, Shiang-An Wang, Chih-Chiang Fang
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2017)
Article
Business
Yeu-Shiang Huang, Chia-Jen Lin, Chih-Chiang Fang
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2019)
Article
Green & Sustainable Science & Technology
Jyh-Wen Ho, Yeu-Shiang Huang, Chi-Lun Hsu
JOURNAL OF CLEANER PRODUCTION
(2018)
Article
Operations Research & Management Science
Yeu-Shiang Huang, Hau-Wen Lo, Jyh-Wen Ho
Summary: This study examines an inventory control problem in an assemble-to-order production system to determine optimal ordering strategies for perishable and non-perishable components, demonstrating increased profits by effectively managing inventory allocation.
OPERATIONAL RESEARCH
(2021)
Article
Engineering, Industrial
Yeu-Shiang Huang, Yung-Chen Hsu, Chih-Chiang Fang
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2019)
Article
Computer Science, Interdisciplinary Applications
Yeu-Shiang Huang, Chih-Chiang Fang, Ying-An Lin
COMPUTERS & INDUSTRIAL ENGINEERING
(2020)
Article
Management
Jyh-Wen Ho, Yeu-Shiang Huang, Chi-Ting Fu
Summary: This study investigates the dispatching of parallel machines for TFT-LCD manufacturing and develops a genetic algorithm to solve the scheduling problem. The research aims to reduce manufacturing time and satisfy consumer demand with the proposed algorithm.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Yeu-Shiang Huang, Jyh-Wen Ho, Hong-Jin Jian, Tzu-Liang (Bill) Tseng
Summary: This study focuses on developing an optimal long-term ordering policy in a two-echelon supply chain using a quantity discount-coordination mechanism. Coordinating quantity discounts can reduce demand uncertainty, decrease retailer orders, and effectively increase the supply chain's overall benefits.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Management
Yeu-Shiang Huang, Tzu-Yi Wu, Chih-Chiang Fang, Tzu-Liang (bill) Tseng
Summary: Consumer attitudes towards probabilistic goods are influenced by their risk preferences and word-of-mouth, impacting a retailer's profit in probabilistic selling strategies. Word-of-mouth affects consumers' perceived probability of obtaining preferred items and subsequently impacts the retailer's profits.
Article
Engineering, Industrial
Yeu-Shiang Huang, Jyh-Wen Ho, Jin-Wei Hung, Tzu-Liang (Bill) Tseng
Summary: This study examines customized leasing services and develops a pricing model for the leasing industry to meet the demands of heterogeneous consumers. Customers are categorized into high, moderate, and low usage types, with corresponding leasing services tailored to each type to optimize lease contracts for different lessees.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Multidisciplinary
Yeu-Shiang Huang, Chih-Chiang Fang, Pin-Chun Lin, Y. Chris Liao
Summary: The development of the Internet has significantly transformed the business models of firms, enabling the adoption of strategies such as bundling. This study introduces a two-stage game theoretic model to analyze the bundling and pricing strategies of products with different network externalities. The results suggest that the bricks-and-clicks approach benefits both manufacturers and dealers, with profits increasing as network externality grows.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2021)
Article
Engineering, Industrial
Kuei-Chen Chiu, Yeu-Shiang Huang, I-Chi Huang
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE
(2019)
Article
Engineering, Industrial
Yeu-Shiang Huang, Yeu-Hau Gu, Chih-Chiang Fang
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2019)
Article
Business
Yeu-Shiang Huang, Wei-Jeh Huang, Chih-Chiang Fang
JOURNAL OF BUSINESS RESEARCH
(2018)
Article
Computer Science, Interdisciplinary Applications
Chih-Chiang Fang, Min-Hsiu Lai, Yeu-Shiang Huang
COMPUTERS & INDUSTRIAL ENGINEERING
(2017)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
