Pub Date : 2026-01-08DOI: 10.1016/j.ejor.2026.01.006
Mel T. Devine, Valentin Bertsch
Electricity consumers worldwide are investing in self-sufficiency technologies like solar photovoltaics and battery storage, often in markets dominated by oligopolistic generating firms that also consider generation investments. Previous models in the literature have not considered investment decisions on both the demand and the supply sides, nor the interactions between them. In this work, we study the interactions between investment decisions on both sides, and we investigate how price-making behaviour on the supply side affects these interactions. We introduce a novel stochastic equilibrium problem to model several players in an oligopolistic electricity market. On the supply side, we consider generating firms that make operational and investment decisions. On the demand side, we consider both industrial and residential consumers. This model enables us to examine how market power, feed-in premiums, and consumer prosumption influence self-sufficiency investments, consumer costs, and generation portfolios. It also allows us to explore how the interactions among these factors affect outcomes such as wholesale prices and carbon emissions. We apply the model to a case study of a stylised Irish electricity system in 2030. Our results demonstrate that price-making on the supply side increases investment in self-sufficiency on the demand side, which in turn reduces carbon emissions and lessens the increase in prices resulting from the presence of market power. We also find that both market power and self-sufficiency alter the investment decisions made by generation firms. Counter-intuitively, we also observe that the absence of a feed-in premium increases investment in solar generation on the demand side.
{"title":"Analysing the interactions between demand side and supply side investment decisions in an oligopolistic electricity market using a stochastic equilibrium model","authors":"Mel T. Devine, Valentin Bertsch","doi":"10.1016/j.ejor.2026.01.006","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.006","url":null,"abstract":"Electricity consumers worldwide are investing in self-sufficiency technologies like solar photovoltaics and battery storage, often in markets dominated by oligopolistic generating firms that also consider generation investments. Previous models in the literature have not considered investment decisions on both the demand and the supply sides, nor the interactions between them. In this work, we study the interactions between investment decisions on both sides, and we investigate how price-making behaviour on the supply side affects these interactions. We introduce a novel stochastic equilibrium problem to model several players in an oligopolistic electricity market. On the supply side, we consider generating firms that make operational and investment decisions. On the demand side, we consider both industrial and residential consumers. This model enables us to examine how market power, feed-in premiums, and consumer prosumption influence self-sufficiency investments, consumer costs, and generation portfolios. It also allows us to explore how the interactions among these factors affect outcomes such as wholesale prices and carbon emissions. We apply the model to a case study of a stylised Irish electricity system in 2030. Our results demonstrate that price-making on the supply side increases investment in self-sufficiency on the demand side, which in turn reduces carbon emissions and lessens the increase in prices resulting from the presence of market power. We also find that both market power and self-sufficiency alter the investment decisions made by generation firms. Counter-intuitively, we also observe that the absence of a feed-in premium increases investment in solar generation on the demand side.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"46 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.ejor.2026.01.008
Yashuang Wei, Guofang Nan, Hubert Pun
In response to rising privacy concerns from potential data misuse fueled by digital development, policymakers have implemented various privacy regulation policies. These regulations are progressively enhancing consumers’ control over their personal data, making it commonplace for them to make informed decisions about data sharing. Using an analytical framework, we examine how consumers’ data control rights shape consumer-firm interactions and decisions. Interestingly, we find that the data rights regulation consistently motivates firms to set higher product prices. Moreover, we show that this regulation for consumers can confer benefits onto firms in both monopoly and duopoly settings. In a duopoly market, data rights regulation may counter the Matthew effect by redistributing competitive advantages from superior to inferior firms, reducing monopolization risks. Unfortunately, our findings indicate that granting consumers data control rights can reduce their surplus, as they may have to pay higher prices for the privacy security these rights provide.
{"title":"Privacy Concerns and Data Rights Regulation in Digital Markets","authors":"Yashuang Wei, Guofang Nan, Hubert Pun","doi":"10.1016/j.ejor.2026.01.008","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.008","url":null,"abstract":"In response to rising privacy concerns from potential data misuse fueled by digital development, policymakers have implemented various privacy regulation policies. These regulations are progressively enhancing consumers’ control over their personal data, making it commonplace for them to make informed decisions about data sharing. Using an analytical framework, we examine how consumers’ data control rights shape consumer-firm interactions and decisions. Interestingly, we find that the data rights regulation consistently motivates firms to set higher product prices. Moreover, we show that this regulation for consumers can confer benefits onto firms in both monopoly and duopoly settings. In a duopoly market, data rights regulation may counter the Matthew effect by redistributing competitive advantages from superior to inferior firms, reducing monopolization risks. Unfortunately, our findings indicate that granting consumers data control rights can reduce their surplus, as they may have to pay higher prices for the privacy security these rights provide.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"7 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.ejor.2026.01.004
Antonio Consolo, Edoardo Amaldi, Emilio Carrizosa
Decision trees are popular in survival analysis for their interpretability and ability to model complex relationships. Survival trees, which predict the timing of singular events using censored historical data, are typically built through heuristic approaches. Recently, there has been growing interest in globally optimized trees, where the overall tree is trained by minimizing the error function over all its parameters. We propose a new soft survival tree model (SST), with a soft splitting rule at each branch node, trained via a nonlinear optimization formulation amenable to decomposition. Since SSTs provide for every input vector a specific survival function associated to a single leaf node, they satisfy the conditional computation property and inherit the related benefits. SST and the training formulation combine flexibility with interpretability: any smooth survival function (parametric, semiparametric, or nonparametric) estimated through maximum likelihood can be used, and each leaf node of an SST yields a cluster of distinct survival functions which are associated to the data points routed to it. Numerical experiments on 15 well-known datasets show that SSTs, with parametric and spline-based semiparametric survival functions, trained using an adaptation of the node-based decomposition algorithm proposed by Consolo et al. (2024) for soft regression trees, outperform three benchmark survival trees in terms of four widely-used discrimination and calibration measures. SSTs can also be extended to consider group fairness.
{"title":"Soft decision trees for survival analysis","authors":"Antonio Consolo, Edoardo Amaldi, Emilio Carrizosa","doi":"10.1016/j.ejor.2026.01.004","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.004","url":null,"abstract":"Decision trees are popular in survival analysis for their interpretability and ability to model complex relationships. Survival trees, which predict the timing of singular events using censored historical data, are typically built through heuristic approaches. Recently, there has been growing interest in globally optimized trees, where the overall tree is trained by minimizing the error function over all its parameters. We propose a new soft survival tree model (SST), with a soft splitting rule at each branch node, trained via a nonlinear optimization formulation amenable to decomposition. Since SSTs provide for every input vector a specific survival function associated to a single leaf node, they satisfy the conditional computation property and inherit the related benefits. SST and the training formulation combine flexibility with interpretability: any smooth survival function (parametric, semiparametric, or nonparametric) estimated through maximum likelihood can be used, and each leaf node of an SST yields a cluster of distinct survival functions which are associated to the data points routed to it. Numerical experiments on 15 well-known datasets show that SSTs, with parametric and spline-based semiparametric survival functions, trained using an adaptation of the node-based decomposition algorithm proposed by Consolo et al. (2024) for soft regression trees, outperform three benchmark survival trees in terms of four widely-used discrimination and calibration measures. SSTs can also be extended to consider group fairness.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"1 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-07DOI: 10.1016/j.ejor.2025.12.042
Zhaleh Rahimi, Douglas G. Down, Na Li, Donald M. Arnold
We investigate optimal ordering policies for a multi-item periodic-review inventory system, considering demand correlations and historical data for the products involved. We extend inventory models by transitioning from an autoregressive moving average (ARMA) demand process to a vector autoregressive moving average (VARMA) framework, explicitly characterizing optimal ordering policies when there is both autocorrelation and cross-correlation among multiple items. Through experimental studies, we evaluate inventory costs and cost improvements compared to multi-item ordering policies where demands are assumed to be independent under different degrees of correlation, noise levels, and training data window sizes. The results show that the framework effectively reduces inventory costs, particularly for products with moderate to high dependence. Cost reductions can reach up to 25% for moderate and up to 65% for strong dependence. We also apply our findings to real-world data to optimize inventory policies for immunoglobulin sub-products, intravenous (IVIg) and subcutaneous (SCIg), demonstrating cost improvements using the proposed policy. Furthermore, an empirical study analyzing a large sales dataset reinforces the applicability of our approach.
{"title":"Ordering policies for multi-item inventory systems with correlated demands","authors":"Zhaleh Rahimi, Douglas G. Down, Na Li, Donald M. Arnold","doi":"10.1016/j.ejor.2025.12.042","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.12.042","url":null,"abstract":"We investigate optimal ordering policies for a multi-item periodic-review inventory system, considering demand correlations and historical data for the products involved. We extend inventory models by transitioning from an autoregressive moving average (ARMA) demand process to a vector autoregressive moving average (VARMA) framework, explicitly characterizing optimal ordering policies when there is both autocorrelation and cross-correlation among multiple items. Through experimental studies, we evaluate inventory costs and cost improvements compared to multi-item ordering policies where demands are assumed to be independent under different degrees of correlation, noise levels, and training data window sizes. The results show that the framework effectively reduces inventory costs, particularly for products with moderate to high dependence. Cost reductions can reach up to 25% for moderate and up to 65% for strong dependence. We also apply our findings to real-world data to optimize inventory policies for immunoglobulin sub-products, intravenous (IVIg) and subcutaneous (SCIg), demonstrating cost improvements using the proposed policy. Furthermore, an empirical study analyzing a large sales dataset reinforces the applicability of our approach.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"95 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-06DOI: 10.1016/j.ejor.2025.12.040
Yuhan Xue, Chong Wu, Xinyu Wang, Zhuchun Li
This paper develops a graph-based dynamic opinion-formation framework for group decision-making that addresses a limitation of conventional consensus-oriented models: in many real decision environments, full agreement among experts is neither realistic nor required. Existing approaches are designed to force convergence to a single consensus and therefore cannot represent settings in which persistent disagreement naturally emerges while a collective decision still needs to be made. To fill this gap, we propose a dynamical system in which experts revise their opinions through heterogeneous interpersonal influences on a general graph and individual self-trust. The resulting dynamics admit two stable and internally coherent opinion groups, enabling collective decisions to be derived from the emergent polarized structure rather than from enforced consensus. We analytically characterize the long-term behavior of the model under general network structures and illustrate its decision-making implications through simulation studies. The results show how a heterogeneous graph network shapes the formation of opinion groups and the associated collective decision. The framework thus offers a methodological tool for graph-based group decision processes in which stable disagreement, rather than full consensus, is the expected outcome. In addition, its intrinsic bipolar structure makes the framework particularly effective for identifying a single best alternative by naturally amplifying the separation between the best option and others.
{"title":"Interaction and self-trust based decision-making via the voting Kuramoto model","authors":"Yuhan Xue, Chong Wu, Xinyu Wang, Zhuchun Li","doi":"10.1016/j.ejor.2025.12.040","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.12.040","url":null,"abstract":"This paper develops a graph-based dynamic opinion-formation framework for group decision-making that addresses a limitation of conventional consensus-oriented models: in many real decision environments, full agreement among experts is neither realistic nor required. Existing approaches are designed to force convergence to a single consensus and therefore cannot represent settings in which persistent disagreement naturally emerges while a collective decision still needs to be made. To fill this gap, we propose a dynamical system in which experts revise their opinions through heterogeneous interpersonal influences on a general graph and individual self-trust. The resulting dynamics admit two stable and internally coherent opinion groups, enabling collective decisions to be derived from the emergent polarized structure rather than from enforced consensus. We analytically characterize the long-term behavior of the model under general network structures and illustrate its decision-making implications through simulation studies. The results show how a heterogeneous graph network shapes the formation of opinion groups and the associated collective decision. The framework thus offers a methodological tool for graph-based group decision processes in which stable disagreement, rather than full consensus, is the expected outcome. In addition, its intrinsic bipolar structure makes the framework particularly effective for identifying a single best alternative by naturally amplifying the separation between the best option and others.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"35 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-06DOI: 10.1016/j.ejor.2026.01.002
Wei Wang, Tim J. Boonen, Wenjun Jiang, Yiying Zhang
{"title":"Optimal insurance design under distortion risk measures with variance constraint","authors":"Wei Wang, Tim J. Boonen, Wenjun Jiang, Yiying Zhang","doi":"10.1016/j.ejor.2026.01.002","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.002","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"18 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-06DOI: 10.1016/j.ejor.2026.01.003
Meng Wan, Songsong Liu, Richard Allmendinger, Rui Su
{"title":"Multi-objective Robust Optimization for Facility Location Problem of Personalized Medicine Supply Chain","authors":"Meng Wan, Songsong Liu, Richard Allmendinger, Rui Su","doi":"10.1016/j.ejor.2026.01.003","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.003","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"1 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.ejor.2025.12.044
Nils Boysen, Dirk Briskorn, Arne Schulz
{"title":"Stuck in the middle: Optimization of long-haul trucking with battery trailer support","authors":"Nils Boysen, Dirk Briskorn, Arne Schulz","doi":"10.1016/j.ejor.2025.12.044","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.12.044","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"49 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.ejor.2025.12.039
Rongzhu Ke, Xinyi Xu
{"title":"A general method for the existence of an optimal deterministic contract in moral hazard problems","authors":"Rongzhu Ke, Xinyi Xu","doi":"10.1016/j.ejor.2025.12.039","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.12.039","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"3 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.ejor.2025.12.048
Hyeon-Il Kim, Dong-Ho Lee
{"title":"Two-phase optimal and heuristic algorithms for flow shop scheduling with reworks under overlapped queue time limits","authors":"Hyeon-Il Kim, Dong-Ho Lee","doi":"10.1016/j.ejor.2025.12.048","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.12.048","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"58 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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