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Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models* J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-07-17 Jungjun Choi, Ming Yuan
This paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if t...
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Latent Interaction Effect in the CLPM Model: A Two-Step Multiple Imputation Analysis Struct. Equ. Model. (IF 2.5) Pub Date : 2024-07-16 Ming-Chi Tseng
This study aims to estimate the latent interaction effect in the CLPM model through a two-step multiple imputation analysis. The estimation of within × within and between × within latent interactio...
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Evaluation of Maximal Reliability for Multidimensional Measuring Instruments Using Structural Equation Modeling Struct. Equ. Model. (IF 2.5) Pub Date : 2024-07-16 Tenko Raykov, Bingsheng Zhang
Multidimensional measuring instruments are often used in behavioral, social, educational, marketing, and biomedical research. For these scales, the paper discusses how to find the optimal score bas...
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Controlling the False Split Rate in Tree-Based Aggregation J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-07-09 Simeng Shao, Jacob Bien, Adel Javanmard
In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to industries, which i...
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-07-08 Reza Mohammadi
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Distance Covariance, Independence, and Pairwise Differences Am. Stat. (IF 1.8) Pub Date : 2024-07-03 Jakob Raymaekers, Peter J. Rousseeuw
Distance covariance (Székely et al. 2007) is a fascinating recent notion, which is popular as a test for dependence of any type between random variables X and Y. This approach deserves to be touche...
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Robust Matrix Completion with Heavy-tailed Noise J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-07-03 Bingyan Wang, Jianqing Fan
This paper studies noisy low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incom...
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A matheuristic for integrated medium-term home healthcare planning Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-07-02 Arne Delaet, Katrien Ramaekers, Patrick Hirsch, Yves Molenbruch, Kris Braekers
Due to staff shortages and budget restrictions, home healthcare service providers struggle to construct efficient schedules to service the growing number of people that require medical services at home. Previous research on home healthcare planning concentrates on planning periods of one to seven days, often does not focus on including real-life characteristics, and generally develops a heuristic procedure
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A novel dynamic programming heuristic for the quadratic knapsack problem Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-07-02 M. Eliass Fennich, Franklin Djeumou Fomeni, Leandro C. Coelho
The Quadratic Knapsack Problem (QKP) is a well-studied combinatorial optimization problem with practical applications in various fields such as finance, logistics, and telecommunications. Despite its longstanding interest, the QKP remains challenging due to its strong -hardness. Moreover, recent studies have introduced new instances where all existing algorithms have failed to produce good-quality
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A Multi-Method Data Science Pipeline for Analyzing Police Service Am. Stat. (IF 1.8) Pub Date : 2024-07-01 Anna Haensch, Daanika Gordon, Karin Knudson, Justina Cheng
Despite the fact that most police departments in the U.S. serve jurisdictions with fewer than 10,000 residents, policing practices in small towns are understudied. This is due in part to data limit...
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Degree centrality, von Neumann–Morgenstern expected utility and externalities in networks Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-29 René van den Brink, Agnieszka Rusinowska
This paper aims to connect the social network literature on centrality measures with the economic literature on von Neumann–Morgenstern expected utility functions using cooperative game theory. The social network literature studies various concepts of network centrality, such as degree, betweenness, connectedness, and so on. This resulted in a great number of network centrality measures, each measuring
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The Deck of Cards Method to Build Interpretable Fuzzy Sets in Decision-making Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-29 Diego García-Zamora, Bapi Dutta, José Rui Figueira, Luis Martínez
This paper deals with the construction of interpretable fuzzy sets by following a socio-technical-based approach, where two key actors, the (decision) analyst, and the decision-maker, interact in a co-constructive way for building a membership function that accurately represents the decision-maker’s individualized semantics, even when considering heterogeneous scales. In the proposed approach, the
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Remaining useful life prediction for two-phase degradation model based on reparameterized inverse Gaussian process Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-28 Liangliang Zhuang, Ancha Xu, Yijun Wang, Yincai Tang
Two-phase degradation is a prevalent degradation mechanism observed in modern systems, typically characterized by a change in the degradation rate or trend of a system’s performance at a specific time point. Ignoring this change in degradation models can lead to considerable biases in predicting the remaining useful life (RUL) of the system, and potentially leading to inappropriate condition-based
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Optimal day-ahead offering strategy for large producers based on market price response learning Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-27 Antonio Alcántara, Carlos Ruiz
In day-ahead electricity markets based on uniform marginal pricing, small variations in the offering and bidding curves may substantially modify the resulting market outcomes. In this work, we deal with the problem of finding the optimal offering curve for a risk-averse profit-maximizing generating company (GENCO) in a data-driven context. In particular, a large GENCO’s market share may imply that
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Mixed-integer linear programming for project scheduling under various resource constraints Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-27 Nicklas Klein, Mario Gnägi, Norbert Trautmann
Project scheduling is an important management task in many companies across different industries. Generally, projects require resources, such as personnel or funds, whose availabilities are limited, giving rise to the challenging problem of resource-constrained project scheduling. In this paper, we consider the scheduling of a project consisting of precedence-related activities that require time and
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Strategic flood impact mitigation in developing countries’ urban road networks: Application to Hanoi Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-26 Siao-Leu Phouratsamay, Maria Paola Scaparra, Trung Hieu Tran, Gilbert Laporte
Due to climate change, the frequency and scale of flood events worldwide are increasing dramatically. Flood impacts are especially acute in developing countries, where they often revert years of progress in sustainable development and poverty reduction. This paper introduces an optimization-based decision support tool for selecting cost-efficient flood mitigation investments in developing countries’
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Stable set reformulations for the degree preserving spanning tree problem Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-26 Abílio Lucena, Alexandre Salles da Cunha
Let be a connected undirected graph and assume that a spanning tree is available for it. Any vertex in this tree is called if it has the same degree in the graph and in the tree. Building upon this concept, the Degree Preserving Spanning Tree Problem (DPSTP) asks for a spanning tree of with as many degree preserving vertices as possible. DPSTP is very much intertwined with the most important application
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Sparse Independent Component Analysis with an Application to Cortical Surface fMRI Data in Autism J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-26 Zihang Wang, Irina Gaynanova, Aleksandr Aravkin, Benjamin B. Risk
Independent component analysis (ICA) is widely used to estimate spatial resting-state networks and their time courses in neuroimaging studies. It is thought that independent components correspond t...
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Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-26 Manuel Hentschel, Sebastian Engelke, Johan Segers
The severity of multivariate extreme events is driven by the dependence between the largest marginal observations. The Hüsler–Reiss distribution is a versatile model for this extremal dependence, a...
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Contextual Dynamic Pricing with Strategic Buyers J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-26 Pangpang Liu, Zhuoran Yang, Zhaoran Wang, Will Wei Sun
Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can als...
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Interactive preference analysis: A reinforcement learning framework Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-25 Xiao Hu, Siqin Kang, Long Ren, Shaokeng Zhu
Automated investment managers are increasingly popular in personal wealth management due to their cost effectiveness, objectivity, and accessibility. However, it still suffers from several dilemmas, e.g., cold start, over-specialization, and black boxes. To solve these issues, we develop an online reinforcement learning framework based on the multi-armed bandit algorithm to offer personalized investment
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Investigating Latent Interaction Effects in Multiple-Group Analysis in the Structural Equation Modeling Framework Struct. Equ. Model. (IF 2.5) Pub Date : 2024-06-25 Suyoung Kim, Sooyong Lee, Jiwon Kim, Tiffany A. Whittaker
This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for e...
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An Alternative Prior for Estimation in High-Dimensional Settings Struct. Equ. Model. (IF 2.5) Pub Date : 2024-06-25 Michael Nagel, Lukas Fischer, Tim Pawlowski, Augustin Kelava
Bayesian estimations of complex regression models with high-dimensional parameter spaces require advanced priors, capable of addressing both sparsity and multicollinearity in the data. The Dirichle...
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On bi-objective combinatorial optimization with heterogeneous objectives Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-24 Raphaël Cosson, Roberto Santana, Bilel Derbel, Arnaud Liefooghe
The heterogeneity among objectives in multi-objective optimization can be viewed from several perspectives. In this paper, we are interested in the heterogeneity arising in the underlying landscape of the objective functions, in terms of multi-modality and search difficulty. Building on recent efforts leveraging the so-called single-objective NK-landscapes to model such a setting, we conduct a three-fold
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A bilateral deliberation mechanism for conflict resolving with multi-actor and multi-criteria Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-24 Shucheng Luo, Zeshui Xu, Bin Zhu
Multi-actor multi-criteria analysis (MAMCA) is widely used to support group decision-making processes that involve various stakeholders. These stakeholders usually have divergent attributes and heterogeneous preferences, which leads to conflicting views on certain pre-set criteria. To deal with this issue, we propose a four-step conflict resolution approach to diagnose and mitigate such conflicts.
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Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-24 Zhaoxing Gao, Ruey S. Tsay
This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an ...
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Synthetic likelihood in misspecified models J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-24 David T. Frazier, David J. Nott, Christopher Drovandi
Bayesian synthetic likelihood is a widely used approach for conducting Bayesian analysis in complex models where evaluation of the likelihood is infeasible but simulation from the assumed model is ...
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The many Shapley values for explainable artificial intelligence: A sensitivity analysis perspective Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-22 Emanuele Borgonovo, Elmar Plischke, Giovanni Rabitti
Predictive models are increasingly used for managerial and operational decision-making. The use of complex machine learning algorithms, the growth in computing power, and the increase in data acquisitions have amplified the black-box effects in data science. Consequently, a growing body of literature is investigating methods for interpretability and explainability. We focus on methods based on Shapley
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Scheduling maintenance activities subject to stochastic job-dependent machine deterioration Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-22 Dirk Briskorn, Jochen Gönsch, Antonia Thiemeyer
This paper considers a maintenance scheduling problem on a single machine with a fixed job sequence involving job-dependent, stochastic deterioration. If the machine breaks down, then a time-consuming emergency maintenance activity needs to be conducted. Instead, planned maintenance activities can be conducted between jobs in order to improve the machine’s state and, thus, prevent the machine from
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Risk-aversion versus risk-loving preferences in nonparametric frontier-based fund ratings: A buy-and-hold backtesting strategy Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-22 Tiantian Ren, Kristiaan Kerstens, Saurav Kumar
The eventual risk-loving nature of preferences of investors has largely been ignored in the existing frontier-based fund rating literature. This contribution develops a series of nonparametric frontier-based methods to rate mutual funds accounting for both mixed risk-loving and mixed risk-aversion preferences. These new methods are proposed by defining the corresponding shortage functions that can
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Collusion by mistake: Does algorithmic sophistication drive supra-competitive profits? Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-22 Ibrahim Abada, Xavier Lambin, Nikolay Tchakarov
A burgeoning literature shows that self-learning algorithms may, under some conditions, reach seemingly-collusive outcomes: after repeated interaction, competing algorithms earn supra-competitive profits, at the expense of efficiency and consumer welfare. This paper offers evidence that such behavior can stem from insufficient exploration during the learning process and that algorithmic sophistication
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Mathematical optimisation in the honeycomb cardboard industry: A model for the two-dimensional variable-sized cutting stock problem Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-22 Paula Terán-Viadero, Antonio Alonso-Ayuso, F. Javier Martín-Campo
This paper presents a mixed-integer linear programming model for a two-dimensional variable-sized cutting stock problem with guillotine cuts that arises in the honeycomb cardboard sector. This research is developed in collaboration with a company based in Spain. The aim is not only to define the cutting patterns but also to establish the dimensions (width and length) of the panels to be produced, in
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A bilevel programming approach to price decoupling in Pay-as-Clear markets, with application to day-ahead electricity markets Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-22 Antonio Frangioni, Fabrizio Lacalandra
Motivated by the recent crisis of the European electricity markets, we propose the concept of (SPaC) market, introducing a new family of market clearing problems that achieve a degree of decoupling between groups of participants. This requires a relatively straightforward modification of the standard PaC model and retains its crucial features by providing both long- and short-term sound price signals
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Corrigendum to Maximum Likelihood Estimation of the Multivariate Normal Mixture Model J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-21
Published in Journal of the American Statistical Association (Just accepted, 2024)
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High-dimensional propensity score and its machine learning extensions in residual confounding control Am. Stat. (IF 1.8) Pub Date : 2024-06-17 Mohammad Ehsanul Karim
“The use of health care claims datasets often encounters criticism due to the pervasive issues of omitted variables and inaccuracies or mis-measurements in available confounders. Ultimately, the tr...
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SEM Approach to the Mediation Analysis of the Two-Condition Within-Subject Design Struct. Equ. Model. (IF 2.5) Pub Date : 2024-06-18 Eujin Park, Changsoon Park
The effects of the two-condition within-subject (TCWS) conditional mediation model are developed using the SEM approach. The structural equation model for the within-subject mediator and the within...
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Nonparametric multi-product dynamic pricing with demand learning via simultaneous price perturbation Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-21 Xiangyu Yang, Jianghua Zhang, Jian-Qiang Hu, Jiaqiao Hu
We consider the problem of multi-product dynamic pricing with demand learning and propose a nonparametric online learning algorithm based on the simultaneous perturbation stochastic approximation (SPSA) method. The algorithm uses only two price experimentations at each iteration, regardless of problem dimension, and could be especially efficient for solving high-dimensional problems. Under moderate
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Optimal trading with regime switching: Numerical and analytic techniques applied to valuing storage in an electricity balancing market Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-20 Paul Johnson, Dávid Zoltán Szabó, Peter Duck
Accurately valuing storage in the electricity market recognizes its role in enhancing grid flexibility, integrating renewable energy, managing peak loads, providing ancillary services and improving market efficiency. In this paper we outline an optimal trading problem for an Energy Storage Device trading on the electricity balancing (or regulating) market. To capture the features of the balancing (or
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Solving constrained consumption–investment problems by decomposition algorithms Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-20 Bernardo K. Pagnoncelli, Tito Homem-de-Mello, Guido Lagos, Pablo Castañeda, Javier García
Consumption–investment problems with maximizing utility agents are usually considered from a theoretical viewpoint, aiming at closed-form solutions for the optimal policy. However, such an approach requires that the model be relatively simple: even the inclusion of nonnegativity constraints can prevent the derivation of explicit solutions. In such cases, it is necessary to solve the problem numerically
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An exact algorithm for the multi-trip vehicle routing problem with time windows and multi-skilled manpower Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-20 Nan Huang, Hu Qin, Yuquan Du, Li Wang
Motivated by the challenges of non-emergency patient transportation services in the healthcare industry, this study investigated a multi-trip vehicle routing problem incorporating multi-skilled manpower with downgrading. We aimed to find an optimal plan for vehicle routing and multi-skilled manpower scheduling in tandem with the objective of minimizing the total cost, including travel and staff costs
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Enhanced Response Envelope via Envelope Regularization J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-18 Oh-Ran Kwon, Hui Zou
The response envelope model provides substantial efficiency gains over the standard multivariate linear regression by identifying the material part of the response to the model and by excluding the...
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Integrative data analysis where partial covariates have complex non-linear effects by using summary information from an external data Am. Stat. (IF 1.8) Pub Date : 2024-06-17 Jia Liang, Shuo Chen, Peter Kochunov, L. Elliot Hong, Chixiang Chen
A full parametric and linear specification may be insufficient to capture complicated patterns in studies exploring complex features, such as those investigating age-related changes in brain functi...
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Joint Effects in Cross-Lagged Panel Research Using Structural Nested Mean Models Struct. Equ. Model. (IF 2.5) Pub Date : 2024-06-17 Jeroen D. Mulder, Satoshi Usami, Ellen L. Hamaker
A popular approach among psychological researchers for investigating causal relationships from panel data is cross-lagged panel modeling within the structural equation modeling (SEM) framework. How...
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Investment–consumption optimization with transaction cost and learning about return predictability Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-17 Ning Wang, Tak Kuen Siu
In this paper, we investigate an investment–consumption optimization problem in continuous-time settings, where the expected rate of return from a risky asset is predictable with an observable factor and an unobservable factor. Based on observable information, a decision-maker learns about the unobservable factor while making investment–consumption decisions. Both factors are supposed to follow a mean-reverting
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Generalized Data Thinning Using Sufficient Statistics J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-13 Ameer Dharamshi, Anna Neufeld, Keshav Motwani, Lucy L. Gao, Daniela Witten, Jacob Bien
Our goal is to develop a general strategy to decompose a random variable X into multiple independent random variables, without sacrificing any information about unknown parameters. A recent paper s...
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Augmented patterns for decomposition of scheduling and assignment problems Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-15 Paola Cappanera, Andrea Matta, Maria Grazia Scutellà, Martino Singuaroli
Scheduling and assignment are relevant decisions widespread in complex organizations that produce goods or deliver services. Industrial companies and service providers periodically make these decisions that take into account their specific context in terms of objectives and constraints. As a consequence, a multitude of mathematical models for solving specific scheduling and assignment problems have
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Real-time train timetabling with virtual coupling operations on a Y-type metro line Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-15 Hongyang Wang, Lixing Yang, Jinlei Zhang, Qin Luo, Zhongsheng Fan
The spatiotemporal imbalance of passenger flows is a prominent characteristic of urban rail transit systems. To match the provided transportation capacity with passenger distribution, this study considers an integer linear programming model to optimize train operation on a Y-type line, including the train timetable, rolling stock circulation plan and virtual coupling/uncoupling strategy that enables
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Sequentially extending space-filling experimental designs by optimally permuting and stacking columns of the design matrix Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-15 J.D. Parker Jr., T.W. Lucas, W.M. Carlyle
Researchers make available computationally expensive designs for computer experiments for widespread use by cataloging them and providing online links. This paper presents an algorithm that augments space-filling designs (SFDs) by optimally permuting and stacking columns of the design matrix to minimize the maximum absolute pairwise correlation among columns in the new extended design. The algorithm
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Adaptive stochastic lookahead policies for dynamic multi-period purchasing and inventory routing Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-15 Daniel Cuellar-Usaquén, Marlin W. Ulmer, Camilo Gomez, David Álvarez-Martínez
We explore a problem faced by agri-food e-commerce platforms in purchasing different, perishable products and collecting them from multiple producers and delivering them to a single warehouse, aiming to maintain adequate inventory levels to meet current and future customer demand, while avoiding waste. Customer demand and suppliers’ purchase prices and supply volumes are uncertain and revealed on a
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Mixed-model sequencing with stochastic failures: A case study for automobile industry Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-14 I. Ozan Yilmazlar, Mary E. Kurz, Hamed Rahimian
In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day. However, there are some vehicles, failed vehicles, that cannot be produced due to some reasons such as material shortage or paint failure. These vehicles are pulled out of the sequence, and the vehicles in the succeeding positions are moved forward, potentially resulting in challenges for
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Tyranny-of-the-minority Regression Adjustment in Randomized Experiments J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-12 Xin Lu, Hanzhong Liu
Abstract–Regression adjustment is widely used in the analysis of randomized experiments to improve the estimation efficiency of the treatment effect. This paper reexamines a weighted regression adj...
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Russell and slack-based measures of efficiency: A unifying framework Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-12 Valentin Zelenyuk, Shirong Zhao
Some of the popular technical efficiency measures are not able to account for potential slacks in inputs or outputs and may misrepresent the degree of inefficiency pertinent to firms, industries, and countries when compared to their peers. A wide range of methods have been proposed in the literature over the last four decades to address this important issue. The precise relationship among many of these
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Scale-Invariance, Equivariance and Dependency of Structural Equation Models Struct. Equ. Model. (IF 2.5) Pub Date : 2024-06-12 Ke-Hai Yuan, Ling Ling, Zhiyong Zhang
Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, wher...
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Test and Measure for Partial Mean Dependence Based on Machine Learning Methods J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-11 Leheng Cai, Xu Guo, Wei Zhong
It is of importance to investigate the significance of a subset of covariates W for the response Y given covariates Z in regression modeling. To this end, we propose a significance test for the par...
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Nonparametric Multiple-Output Center-Outward Quantile Regression J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-11 Eustasio del Barrio, Alberto González Sanz, Marc Hallin
Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested ...
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A review of recent advances in time-dependent vehicle routing Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-11 Tommaso Adamo, Michel Gendreau, Gianpaolo Ghiani, Emanuela Guerriero
In late 2015 three of the co-authors of this paper published the first review on time-dependent routing problems. Since then, there have been several important algorithmic developments in the field. These include travel time prediction methods, real-time re-optimization by operating directly on the road graph, efficient exploration of solution neighborhoods, dynamic discretization discovery and -inspired
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An effective multi-level memetic search with neighborhood reduction for the clustered team orienteering problem Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-11 Mu He, Qinghua Wu, Una Benlic, Yongliang Lu, Yuning Chen
The Clustered Team Orienteering Problem (CluTOP) extends the classic Clustered Orienteering Problem by considering the use of multiple vehicles. The problem is known to be NP-hard and can be used to formulate many real-life applications. This work presents a highly effective multi-level memetic search for CluTOP that combines a backbone-based edge assembly crossover to generate promising offspring
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A learning-based granular variable neighborhood search for a multi-period election logistics problem with time-dependent profits Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-11 Masoud Shahmanzari, Renata Mansini
Planning the election campaign for leaders of a political party is a complex problem. The party representatives, running mates, and campaign managers have to design an efficient routing and scheduling plan to visit multiple locations while respecting time and budget constraints. Given the limited time of election campaigns in most countries, every minute should be used effectively, and there is very
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Performance evaluation using multi-stage production frameworks: Assessing the tradeoffs among the economic, environmental, and social well-being Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-11 Yiran Niu, Jean-Philippe Boussemart, Zhiyang Shen, Michael Vardanyan
Aiming to achieve sustainable development, a constantly growing number of countries have strived to promote economic growth while simultaneously mitigating environmental degradation and maximizing social welfare. However, despite the importance attributed to social well-being in contemporary discourse, its role has not received much attention in the performance evaluation literature. We propose a novel
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A machine learning approach to two-stage adaptive robust optimization Eur. J. Oper. Res. (IF 6.0) Pub Date : 2024-06-10 Dimitris Bertsimas, Cheol Woo Kim
We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets. We encode the optimal here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the optimal wait-and-see decisions into what we denote as the strategy. We solve multiple