Pub Date : 2026-01-15DOI: 10.1016/j.ejor.2026.01.017
Mathijs Van Zon, Remy Spliet, Wilco Van Den Heuvel
{"title":"The value of an algorithm in a cooperative setting","authors":"Mathijs Van Zon, Remy Spliet, Wilco Van Den Heuvel","doi":"10.1016/j.ejor.2026.01.017","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.017","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"4 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995438","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 : 2026-01-14DOI: 10.1016/j.ejor.2026.01.015
R. Gauchotte, A. Oulamara, M. Ghogho, M. Oudani
{"title":"Study of Electric Vehicle Charging Scheduling with Renewable Energy: Offline and Stochastic Online Optimization","authors":"R. Gauchotte, A. Oulamara, M. Ghogho, M. Oudani","doi":"10.1016/j.ejor.2026.01.015","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.015","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"63 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995112","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 : 2026-01-13DOI: 10.1016/S0377-2217(26)00009-3
{"title":"Prelim p. 2; First issue - Editorial Board","authors":"","doi":"10.1016/S0377-2217(26)00009-3","DOIUrl":"10.1016/S0377-2217(26)00009-3","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"330 1","pages":"Page ii"},"PeriodicalIF":6.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957760","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 : 2026-01-13DOI: 10.1016/j.ejor.2026.01.014
Xinyue Jia, Feng Li, Xindi Gao, Xianyan Yang
{"title":"Integrated Production and Transportation Rescheduling with Type-Dependent Setup Times and Multiple Shipping Modes","authors":"Xinyue Jia, Feng Li, Xindi Gao, Xianyan Yang","doi":"10.1016/j.ejor.2026.01.014","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.014","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"16 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962511","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 : 2026-01-12DOI: 10.1016/j.ejor.2026.01.016
Xiaolong Guo, Zaichen Luo, Shaofu Du
The traditional revenue-sharing (RS) policy allows platforms to extract the same proportion of revenue regardless of market conditions. Therefore, when the market is poor, creators’ retained revenue may not cover costs, leading them to reduce content quality. To incentivize creators to produce high-quality content, some platforms have decided to give up revenue below a given threshold through an incentive plan named two-tier revenue sharing (TRS). Under TRS, the platform sets a threshold value for charging commissions (the non-sharing threshold) such that the platform only takes a cut of the creators’ revenue above that threshold. We develop a game-theoretic model to study the platform’s policy choice—whether to adopt TRS and how to set the threshold—and the creator’s quality decision. Results show that when the platform cannot adjust the sharing ratio, he prefers TRS when the exogenous ratio (determined by the industry standard) is sufficiently small and the market uncertainty is high. The reason is that TRS can alleviate the creator’s concern about market uncertainty since it can protect the creator’s profit when the content is unpopular. When the platform can adjust the sharing ratio, TRS reduces the platform’s profit even though it sometimes improves the quality of content. Surprisingly, a higher probability of favorable market condition does not necessarily raise the platform’s expected profit under TRS. Moreover, creators may suffer in good markets if the platform switches from TRS to RS. These findings guide managers on adopting and designing TRS, emphasizing its benefits and trade-offs.
{"title":"Counteracting Quality Deterioration on Content Platforms with Two-Tier Revenue Sharing under Market Uncertainty","authors":"Xiaolong Guo, Zaichen Luo, Shaofu Du","doi":"10.1016/j.ejor.2026.01.016","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.016","url":null,"abstract":"The traditional revenue-sharing (RS) policy allows platforms to extract the same proportion of revenue regardless of market conditions. Therefore, when the market is poor, creators’ retained revenue may not cover costs, leading them to reduce content quality. To incentivize creators to produce high-quality content, some platforms have decided to give up revenue below a given threshold through an incentive plan named two-tier revenue sharing (TRS). Under TRS, the platform sets a threshold value for charging commissions (the non-sharing threshold) such that the platform only takes a cut of the creators’ revenue above that threshold. We develop a game-theoretic model to study the platform’s policy choice—whether to adopt TRS and how to set the threshold—and the creator’s quality decision. Results show that when the platform cannot adjust the sharing ratio, he prefers TRS when the exogenous ratio (determined by the industry standard) is sufficiently small and the market uncertainty is high. The reason is that TRS can alleviate the creator’s concern about market uncertainty since it can protect the creator’s profit when the content is unpopular. When the platform can adjust the sharing ratio, TRS reduces the platform’s profit even though it sometimes improves the quality of content. Surprisingly, a higher probability of favorable market condition does not necessarily raise the platform’s expected profit under TRS. Moreover, creators may suffer in good markets if the platform switches from TRS to RS. These findings guide managers on adopting and designing TRS, emphasizing its benefits and trade-offs.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"259 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956850","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 : 2026-01-12DOI: 10.1016/j.ejor.2026.01.013
Biao Yuan, Bingjie Yang, Na Geng, Roberto Baldacci
The advent of unmanned vehicles, such as drones and autonomous robots, presents a promising opportunity to enhance the efficiency and quality of last-mile delivery services. This paper studies a vehicle routing problem involving multiple cargo bikes and autonomous robots, with a focus on realistic loading constraints. Unlike most existing problems that assume single-valued vehicle capacities and customer demands, we account for robots equipped with containers of varying sizes and customers receiving multiple parcels, thereby introducing three-dimensional (3D) packing constraints. To address this problem exactly, we propose a two-stage solution approach. In the first stage, we convert the parcels of each customer into the number of containers of each required size by iteratively solving a 3D packing model with dynamically generated cuts, thereby significantly simplifying the overall problem. The resulting optimization problem is formulated as a set-partitioning model, whose relaxation is strengthened with subset-row inequalities and solved using a state-of-the-art branch-price-and-cut (BPC) algorithm. The BPC algorithm incorporates a bi-directional bounded labeling algorithm, ng-route relaxation, and heuristic labeling techniques to efficiently solve pricing problems with multi-dimensional capacity constraints. Extensive computational results validate the effectiveness of the proposed approach. We further analyze the impact of robot speed, travel cost per unit time, robot utilization, and customer accessibility constraints, providing practical insights for last-mile delivery operations.
{"title":"Improving Last-Mile Delivery Efficiency Using Cargo Bikes and Autonomous Robots","authors":"Biao Yuan, Bingjie Yang, Na Geng, Roberto Baldacci","doi":"10.1016/j.ejor.2026.01.013","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.013","url":null,"abstract":"The advent of unmanned vehicles, such as drones and autonomous robots, presents a promising opportunity to enhance the efficiency and quality of last-mile delivery services. This paper studies a vehicle routing problem involving multiple cargo bikes and autonomous robots, with a focus on realistic loading constraints. Unlike most existing problems that assume single-valued vehicle capacities and customer demands, we account for robots equipped with containers of varying sizes and customers receiving multiple parcels, thereby introducing three-dimensional (3D) packing constraints. To address this problem exactly, we propose a two-stage solution approach. In the first stage, we convert the parcels of each customer into the number of containers of each required size by iteratively solving a 3D packing model with dynamically generated cuts, thereby significantly simplifying the overall problem. The resulting optimization problem is formulated as a set-partitioning model, whose relaxation is strengthened with subset-row inequalities and solved using a state-of-the-art branch-price-and-cut (BPC) algorithm. The BPC algorithm incorporates a bi-directional bounded labeling algorithm, <ce:italic>ng</ce:italic>-route relaxation, and heuristic labeling techniques to efficiently solve pricing problems with multi-dimensional capacity constraints. Extensive computational results validate the effectiveness of the proposed approach. We further analyze the impact of robot speed, travel cost per unit time, robot utilization, and customer accessibility constraints, providing practical insights for last-mile delivery operations.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"187 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956853","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}
We design a decision-support tool that automates a key contract parameter to satisfy supply chain coordination conditions in buyback and revenue-sharing contracts to assist human decision-making. Specifically, after a supplier proposes a wholesale price, the decision-support tool determines a buyback price (revenue-sharing ratio) in the buyback case (revenue-sharing case) according to supply chain coordination requirements. Through a 2 × 2 human-to-human experiment design that varies the contract type (buyback vs. revenue-sharing) and decision-support condition (with vs. without), we compare how automating a contract parameter based on coordination requirements influences human decision biases and supply chain performance differently across two contracts. Leveraging behavioral model analysis, we find that automating buyback price mitigates the supplier’s bias of overweighting buyback costs under the buyback contract, thereby improving supply chain performance and promoting a fairer profit allocation between supply chain parties. However, automating revenue-sharing ratio does not improve supply chain performance under the revenue-sharing contract because it induces greater variability in retailer’s ordering decisions, leading to lower supply chain efficiency. Our findings suggest supply chain practitioners carefully implement such decision aids in practice.
{"title":"Debiasing Behaviors in Supply Chain Management: An Experimental Study","authors":"Shuaikun Hou, Shuyuan Zhu, Xiaobo Zhao, Wanshan Zhu, Jinxing Xie","doi":"10.1016/j.ejor.2026.01.011","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.011","url":null,"abstract":"We design a decision-support tool that automates a key contract parameter to satisfy supply chain coordination conditions in buyback and revenue-sharing contracts to assist human decision-making. Specifically, after a supplier proposes a wholesale price, the decision-support tool determines a buyback price (revenue-sharing ratio) in the buyback case (revenue-sharing case) according to supply chain coordination requirements. Through a 2 × 2 human-to-human experiment design that varies the contract type (buyback vs. revenue-sharing) and decision-support condition (with vs. without), we compare how automating a contract parameter based on coordination requirements influences human decision biases and supply chain performance differently across two contracts. Leveraging behavioral model analysis, we find that automating buyback price mitigates the supplier’s bias of overweighting buyback costs under the buyback contract, thereby improving supply chain performance and promoting a fairer profit allocation between supply chain parties. However, automating revenue-sharing ratio does not improve supply chain performance under the revenue-sharing contract because it induces greater variability in retailer’s ordering decisions, leading to lower supply chain efficiency. Our findings suggest supply chain practitioners carefully implement such decision aids in practice.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"96 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956852","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 : 2026-01-12DOI: 10.1016/j.ejor.2026.01.009
jinpeng wei, xuanhua xu, qiuhan wang, zongrun wang, weiwei guo, francisco Javier
Due to the uncertainty of information, decision-makers within a group often seek to compare themselves with others to determine whether they are being treated fairly, which introduces significant instability into consensus management. To provide a reliable solution, this study aims to achieve fair consensus in uncertain environments. First, fairness concerns are incorporated into the maximum experts consensus model, measuring decision-makers’ fairness utility levels and revealing the relationship between their opinion adjustment behavior and fair consensus. Additionally, to more accurately and objectively characterize the uncertainty of consensus parameters, we use a kernel estimation method based on historical decision data to capture the uncertain features of both costs and opinions separately, thereby analyzing their impact on fair consensus. Robust optimization methods are then employed to mitigate the decision risks associated with these uncertainties, and various robust data-driven consensus models are constructed. These models not only eliminates the decision risks arising from uncertainty, but also addresses the issue of conservative consensus often encountered in traditional experience-driven robust optimization to some extent. We also developed an improved particle swarm optimization algorithm to solve the robust models. Finally, extensive numerical analysis results demonstrate that our approach produces more stable and reliable decision outcomes.
{"title":"A robust data-driven maximum experts consensus modeling approach considering fairness concerns under uncertain contexts","authors":"jinpeng wei, xuanhua xu, qiuhan wang, zongrun wang, weiwei guo, francisco Javier","doi":"10.1016/j.ejor.2026.01.009","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.009","url":null,"abstract":"Due to the uncertainty of information, decision-makers within a group often seek to compare themselves with others to determine whether they are being treated fairly, which introduces significant instability into consensus management. To provide a reliable solution, this study aims to achieve fair consensus in uncertain environments. First, fairness concerns are incorporated into the maximum experts consensus model, measuring decision-makers’ fairness utility levels and revealing the relationship between their opinion adjustment behavior and fair consensus. Additionally, to more accurately and objectively characterize the uncertainty of consensus parameters, we use a kernel estimation method based on historical decision data to capture the uncertain features of both costs and opinions separately, thereby analyzing their impact on fair consensus. Robust optimization methods are then employed to mitigate the decision risks associated with these uncertainties, and various robust data-driven consensus models are constructed. These models not only eliminates the decision risks arising from uncertainty, but also addresses the issue of conservative consensus often encountered in traditional experience-driven robust optimization to some extent. We also developed an improved particle swarm optimization algorithm to solve the robust models. Finally, extensive numerical analysis results demonstrate that our approach produces more stable and reliable decision outcomes.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"57 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956854","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 : 2026-01-12DOI: 10.1016/j.ejor.2026.01.012
Ben Lowery, Anna-Lena Sachs, Idris A. Eckley, Louise Lloyd
We investigate the management of stock for a business with integrated online and offline store-fronts selling products facing uncertainty in demand. The integration of channels includes an opportunity for customers to have items sent directly to their home in case of a store stockout. We model a two-echelon divergent, periodic-review inventory model, with partial lost-sales at the store level and an online demand channel. The problem is developed as a Stochastic Dynamic Program minimising inventory costs. For the zero lead-time case, we prove desirable properties and develop ordering decisions based on optimality of a base-stock policy. For positive lead-time, we highlight the effectiveness of adding order caps to reduce system costs. In an extensive numerical study, we improve standard heuristic methods in the literature on costs by up to 19%. Further, we apply methods to real life data for a large mobile phone retailer, Tesco Mobile, with our methods outperforming the internal benchmark method. We show how the company’s target service level can be reached, with a reduction of inventory between 75% and 99% at the store level. By focusing on effective yet interpretable policies, we suggest methods that can be used to aid a decision maker in a practical context.
{"title":"Periodic review inventory control for an omnichannel retailer with partial lost-sales","authors":"Ben Lowery, Anna-Lena Sachs, Idris A. Eckley, Louise Lloyd","doi":"10.1016/j.ejor.2026.01.012","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.012","url":null,"abstract":"We investigate the management of stock for a business with integrated online and offline store-fronts selling products facing uncertainty in demand. The integration of channels includes an opportunity for customers to have items sent directly to their home in case of a store stockout. We model a two-echelon divergent, periodic-review inventory model, with partial lost-sales at the store level and an online demand channel. The problem is developed as a Stochastic Dynamic Program minimising inventory costs. For the zero lead-time case, we prove desirable properties and develop ordering decisions based on optimality of a base-stock policy. For positive lead-time, we highlight the effectiveness of adding order caps to reduce system costs. In an extensive numerical study, we improve standard heuristic methods in the literature on costs by up to 19%. Further, we apply methods to real life data for a large mobile phone retailer, Tesco Mobile, with our methods outperforming the internal benchmark method. We show how the company’s target service level can be reached, with a reduction of inventory between 75% and 99% at the store level. By focusing on effective yet interpretable policies, we suggest methods that can be used to aid a decision maker in a practical context.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"1 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956851","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 : 2026-01-10DOI: 10.1016/j.ejor.2026.01.005
Harun Avci, Barry L. Nelson, Eunhye Song, Andreas Wächter
Although much progress has been made in simulation optimization, problems involving computationally expensive simulations having high-dimensional, discrete decision-variable spaces have been stubbornly resistant to solution. For this class of problems we propose Dice and Slice Simulation Optimization (DASSO). DASSO is a form of Bayesian optimization that represents the prior on the objective function implied by the simulation as a sum of low-dimensional Gaussian Markov random fields. This prior is consistent with the full-dimensional objective function, rather than assuming that it is actually separable. By working iteratively between posteriors on these low-dimensional “dice” and a full-dimensional “slice” of the decision-variable space, DASSO makes rapid progress with little algorithm overhead even on problems with more than a trillion feasible solutions. We achieve further computational savings by showing that we can find the best solution to simulate on each iteration without having to assess the potential of all solutions—as is traditionally done in Bayesian optimization—by identifying a small set of Pareto-optimal solutions in subsets of the dimensions. We prove that DASSO is asymptotically convergent to the optimal solution, while emphasizing that its most important feature is the ability to find good solutions quickly in problems beyond the capability of other methods.
尽管在模拟优化方面取得了很大的进展，但涉及计算成本高、具有高维离散决策变量空间的模拟问题一直顽固地抵制解决。针对这类问题，我们提出了Dice and Slice Simulation Optimization （DASSO）。DASSO是贝叶斯优化的一种形式，它将模拟中隐含的目标函数的先验表示为低维高斯马尔可夫随机场的和。这个先验是与全维目标函数一致的，而不是假设它实际上是可分离的。通过在这些低维“骰子”和决策变量空间的全维“切片”的后置之间迭代工作，DASSO即使在具有超过一万亿可行解决方案的问题上也能以很少的算法开销取得快速进展。我们进一步节省了计算量，因为我们可以在每次迭代中找到模拟的最佳解决方案，而不必像传统的贝叶斯优化那样，通过识别维度子集中的一小组帕累托最优解决方案来评估所有解决方案的潜力。我们证明了DASSO是渐近收敛于最优解的，同时强调了它最重要的特征是在问题中快速找到好的解的能力，而不是其他方法的能力。
{"title":"Dice and Slice Simulation Optimization for High-Dimensional Discrete Problems","authors":"Harun Avci, Barry L. Nelson, Eunhye Song, Andreas Wächter","doi":"10.1016/j.ejor.2026.01.005","DOIUrl":"https://doi.org/10.1016/j.ejor.2026.01.005","url":null,"abstract":"Although much progress has been made in simulation optimization, problems involving computationally expensive simulations having high-dimensional, <ce:italic>discrete</ce:italic> decision-variable spaces have been stubbornly resistant to solution. For this class of problems we propose Dice and Slice Simulation Optimization (DASSO). DASSO is a form of Bayesian optimization that represents the prior on the objective function implied by the simulation as a sum of low-dimensional Gaussian Markov random fields. This prior is consistent with the full-dimensional objective function, rather than assuming that it is actually separable. By working iteratively between posteriors on these low-dimensional “dice” and a full-dimensional “slice” of the decision-variable space, DASSO makes rapid progress with little algorithm overhead even on problems with more than a trillion feasible solutions. We achieve further computational savings by showing that we can find the best solution to simulate on each iteration without having to assess the potential of all solutions—as is traditionally done in Bayesian optimization—by identifying a small set of Pareto-optimal solutions in subsets of the dimensions. We prove that DASSO is asymptotically convergent to the optimal solution, while emphasizing that its most important feature is the ability to find good solutions quickly in problems beyond the capability of other methods.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"7 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956855","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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