Article
Computer Science, Interdisciplinary Applications
Jiapeng Liu, Milosz Kadzinski, Xiuwu Liao, Xiaoxin Mao
Summary: This paper introduces a novel preference learning method for multiple criteria sorting problems. It utilizes convex quadratic programming to construct a value-based preference model, extending the applicability of decision analysis methods. Experimental results demonstrate that the proposed method shows promising performance in dealing with complex decision problems.
INFORMS JOURNAL ON COMPUTING
(2021)
Article
Management
Zice Ru, Jiapeng Liu, Milosz Kadzinski, Xiuwu Liao
Summary: This paper proposes a novel Bayesian Ordinal Regression approach for multiple criteria choice and ranking problems. The approach utilizes an additive value function model to represent the Decision Maker's preferences in the form of pairwise comparisons. It applies the Bayesian rule to derive a posterior distribution over potential value functions and employs the Metropolis-Hastings method for summarizing the distribution and conducting robustness analysis.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Management
Evangelos Grigoroudis, Laurent Noel, Emilios Galariotis, Constantin Zopounidis
Summary: This paper explores the preferences of buyers in the art market and develops an ordinal regression analysis model to study these preferences. The research utilizes a large dataset from the Art Deco furniture market and considers various criteria that may influence buyer preferences. The results focus on analyzing buyer preferences and the ordinal regression model is applied over different time periods to study the evolution of preferences.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Shuxian Sun, Huchang Liao
Summary: This study proposes a value-driven multiple criteria sorting procedure that considers uncertain assignment examples with probabilistic linguistic information. By introducing probability linguistic term set and weighted additive value function, decision-makers' preferences can be more comprehensively reflected.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2022)
Article
Economics
Eduardo Fernandez, Jose Rui Figueira, Jorge Navarro, Efrain Solares
Summary: This paper proposes a generalized method for multi-criteria decision-making analysis in an interval framework, which can handle uncertainty and fuzziness. By modeling interval numbers, the method allows estimation of decision model parameters and assessment of criteria performance levels.
SOCIO-ECONOMIC PLANNING SCIENCES
(2022)
Article
Management
Eduardo Fernandez, Jorge Navarro, Efrain Solares
Summary: This paper introduces a method for decomposing complex decision-making problems into manageable sub-problems, while addressing interaction effects between criteria. The new method allows for easy setting of weights and model parameters, and generalizes multi-criteria sorting methods to handle interacting and hierarchical criteria. An application of the methods in evaluating research and development projects is also demonstrated.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Mathematics
Juan M. Munoz-Pichardo, Emilio D. Lozano-Aguilera, Antonio Pascual-Acosta, Ana M. Munoz-Reyes
Summary: In this article, a multiple ordinal correlation measure based on Kendall's tau is proposed, along with a sample version, confidence interval, and multiple ordinal independence test. The adequacy of the measure and proposed inferential techniques is illustrated through simulations and a real-world study.
Article
Computer Science, Artificial Intelligence
Zhiqiang Liao, Huchang Liao, Xinli Zhang
Summary: This study develops a preference learning model to predict decision-makers' preferences and provide robust decision recommendations. The model is applicable to multiple criteria decision analysis, taking into account bounded rationality of decision-makers and interactions among criteria.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Anas A. Makki, Ibrahim Mosly
Summary: By developing a prediction model based on factors that influence the safety climate observed by construction site personnel, this study identified significant predictors including supervision, management's commitment, and coworker influence. These predictors play a crucial role in personnel's perceived evaluations of the safety climate, and the prediction model can assist in improving safety performance levels.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Fa Zhu, Xingchi Chen, Shuo Chen, Wei Zheng, Weidu Ye
Summary: As a classical ordinal regression model, support vector ordinal regression (SVOR) finds parallel discriminant hyperplanes to maximize the minimal margins between different ranks. However, SVOR only considers minor patterns near the margin hyperplanes and ignores the contributions of other patterns. To address this issue, this paper proposes relative margin induced support vector ordinal regression (RMSVOR) models, which depict the margin between a pattern and a discriminant hyperplane based on relative margin information. Experimental results on various datasets show that RMSVOR outperforms previous ordinal regression models and canonical multi-class classification models.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Psychology, Mathematical
Sopiko Gvaladze, Marlies Vervloet, Katrijn Van Deun, Henk A. L. Kiers, Eva Ceulemans
Summary: Principal covariates regression (PCovR) is a method that reduces predictor variables to a limited number of components in order to deal with interpretation and technical issues in regression. PCovR2 extends PCovR by reducing criteria variables to a few components as well, allowing users to choose the emphasis on reconstruction and prediction. PCovR2 outperforms other approaches like PLS2 and PCR2 in recovering all relevant predictor and predictable criterion components, as shown in a simulated example.
BEHAVIOR RESEARCH METHODS
(2021)
Article
Computer Science, Software Engineering
Amin Mahmoudi, Mahsa Sadeghi, Xiaopeng Deng, Pengcheng Pan
Summary: Multiple criteria decision-making (MCDM) is an effective technique for evaluating and selecting alternative(s), but decision-makers often struggle to provide exact values for comparison. The Ordinal Priority Approach (OPA) provides a solution using ordinal data as input. In this study, a web-based software is developed to streamline the OPA model and check the quality of input and results.
Article
Computer Science, Information Systems
Grzegorz Miebs, Milosz Kadzinski
Summary: The researchers propose a heuristic method for constructing compromise incomplete rankings based on partial rankings allowing for incomparability. The algorithms utilize various optimization techniques and are demonstrated to be effective through a real-world case study and experimental comparisons on artificially generated problems.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Engineering, Marine
Moussa S. Elbisy, Ahmed M. S. Elbisy
Summary: In this study, ANN and MART were used to predict significant wave heights based on meteorological and wave data. The RBFNN showed the best overall performance among the ANN models, while the MART model outperformed all others in accuracy and efficiency.
Article
Computer Science, Theory & Methods
Marko Palangetic, Chris Cornelis, Salvatore Greco, Roman Slowinski
Summary: This paper discusses the importance of granular representations of crisp and fuzzy sets in rule induction algorithms based on rough set theory. It demonstrates that the OWA-based fuzzy rough set model, which has been successfully applied in various machine learning tasks, allows for a granular representation. The practical implications of this result for rule induction from fuzzy rough approximations are highlighted.
FUZZY SETS AND SYSTEMS
(2022)
Article
Management
Marco Cinelli, Milosz Kadzinski, Grzegorz Miebs, Michael Gonzalez, Roman Slowinski
Summary: A new methodology for selecting Multiple Criteria Decision Analysis (MCDA) methods is introduced, implemented in the Multiple Criteria Decision Analysis Methods Selection Software (MCDA-MSS). The software provides guidance for analysts in choosing the most suitable MCDA method for a given decision problem, offering a comprehensive evaluation of over 200 MCDA methods based on problem characteristics.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Operations Research & Management Science
Ozgur Ozpeynirci, Selin Ozpeynirci, Vincent Mousseau
Summary: This paper investigates the inverse multiple criteria sorting problem and solves it by decomposing the problem into two phases. In the first phase, a preprocessing step calculates the minimum cost required for each object-class pair. In the second phase, an interactive assignment model is generated to analyze the trade-offs between classification and budget. The paper also presents a modified version of a regret-based approach, reducing computation time by using a mixed integer program.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2023)
Article
Operations Research & Management Science
Ali Tlili, Oumaima Khaled, Vincent Mousseau, Wassila Ouerdane
Summary: This paper proposes a multi-objective interactive approach based on a constrained Non-Compensatory Sorting model for portfolio selection problem. The approach integrates preferences on both items and portfolios and uses two evaluation models for selection.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Operations Research & Management Science
Pegdwende Minoungou, Vincent Mousseau, Wassila Ouerdane, Paolo Scotton
Summary: This paper investigates an extended version of the inverse MR-Sort problem, exploring solutions when preferences on criteria are not necessarily monotone, and proposes a mixed-integer programming based learning algorithm.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Khaled Belahcene, Vincent Mousseau, Wassila Ouerdane, Marc Pirlot, Olivier Sobrie
Summary: This paper introduces the multicriteria ranking problem and proposes a ranking procedure called Ranking with Multiple reference Points (RMP) based on reference points. Implementing RMP requires eliciting the model preference parameters, which can be done indirectly by inferring the parameters from stated preferences. However, learning an RMP model from stated preferences is computationally costly and not feasible with current algorithms. Therefore, a Boolean satisfiability formulation is proposed in this paper to infer an RMP model from a set of pairwise comparisons, which is much faster than existing algorithms.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Operations Research & Management Science
Khaled Belahcene, Vincent Mousseau, Wassila Ouerdane, Marc Pirlot, Olivier Sobrie
Summary: Multiple criteria sorting methods categorize and rank objects based on their vector of attribute values. The categorization is ordered and the ranking of objects is monotonic with respect to the underlying order of attribute scales (criteria). This article provides a theoretical perspective of the field by describing a general framework for multiple criteria sorting models and positioning existing models within this framework. It also addresses issues related to imperfect or insufficient information and discusses questions that arise in the final phase of a decision aiding process, such as explaining recommendations or suggesting ways to improve object assignments.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2023)
Article
Operations Research & Management Science
Khaled Belahcene, Vincent Mousseau, Wassila Ouerdane, Marc Pirlot, Olivier Sobrie
Summary: Multiple criteria sorting methods categorize objects based on their attribute values and the ordering of categories is determined by the scale of attributes. This survey reviews the literature on multiple criteria sorting methods and focuses on the underlying models. The proposal is divided into two parts, with Part I discussing two main models (UTADIS and Electre Tri) and providing a structured overview of multiple criteria sorting models and methods for determining their parameters or learning based on assignment examples. Part II, to be published in a forthcoming issue, aims to provide a theoretical perspective of the field and discuss issues related to imperfect or insufficient information.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Marco Cinelli, Peter Burgherr, Milosz Kadzinski, Roman Slowinski
Summary: The current selection of Multiple Criteria Decision Analysis (MCDA) methods in energy systems analysis is problematic, primarily due to the misuse of weighting methods, inappropriate selection of MCDA techniques, and failure to address obvious interactions in preference models. A newly developed methodology can assist decision makers and analysts in choosing the most suitable MCDA method.
DECISION SUPPORT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Marko Palangetic, Chris Cornelis, Salvatore Greco, Roman Slowinski
Summary: Inconsistency refers to the situation where instances that share a certain relationship on condition attributes do not exhibit the same relationship on the decision attribute. Various methods, including rough sets and statistical/machine learning approaches, can be used to handle this inconsistency. The Kotlowski-Slowinski (KS) approach addresses the issue by relabeling objects to remove inconsistencies. In this paper, the KS approach is extended to handle inconsistency determined by a fuzzy preorder relation, leading to a consistent fuzzy relabeling that can be used in binary classification and regression algorithms. The method is supported by statistical foundations, includes optimization procedures, and is illustrated through examples.
INFORMATION SCIENCES
(2023)
Editorial Material
Management
Roman Slowinski
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Information Systems
Yizhao Zhao, Zaiwu Gong, Guo Wei, Roman Slowinski
Summary: In this paper, a new consensus model for group utility optimization is constructed. The utilities of individual decision-makers are aggregated using 2-additive Choquet integral, and fuzzy measures (weights) consistent with the decision-makers' preferences are learned through linear programming. Furthermore, the coordinator's fairness preference and tolerant behavior are described using Gini coefficient and orness operator, exploring the impact of the coordinator's psychological behaviors on consensus reaching. Finally, a comparative and parametric analysis is performed on a case study regarding the price negotiation of medical insurance drugs to validate the proposed models.
INFORMATION SCIENCES
(2023)
Article
Management
Sally Giuseppe Arcidiacono, Salvatore Corrente, Salvatore Greco
Summary: This paper introduces a method to handle multiple compatible value functions in multi-criteria decision making by building a probability distribution. Stochastic multicriteria acceptability analysis provides statistical information based on the decision maker's preferences. Extensive simulations and sensitivity analysis have been conducted to demonstrate the superiority of the proposed method.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2024)
Article
Management
Salvatore Corrente, Salvatore Greco, Benedetto Matarazzo, Roman Slowinski
Summary: In this paper, we propose an interactive evolutionary multiobjective optimization (IEMO) approach guided by a preference elicitation procedure inspired by artificial intelligence and decision psychology. The approach utilizes decision rules to influence the optimization process and has been proven to converge to the most interesting part of the Pareto front.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2024)
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)
