【期刊速递】INFORMS Journal on Computing Vol.37 No.2

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【期刊速递】INFORMS Journal on Computing Vol.37 No.2

跨境Emily

2025-10-30

INFORMS Journal on Computing - Volume 37, Issue 2 期刊速递

生成时间: 2025-10-22 21:25:50
期刊: INFORMS Journal on Computing
卷期: Volume 37, Issue 2

📊 AI分析统计

• 有效文章数: 15 篇
• 已完成分析: 15 篇
• 有全文: 15 篇
• 有摘要: 15 篇
• 完成率: 100.0%

📚 目录

📂 Research Articles

1. 求解最小和着色问题：替代模型、精确求解器及元启发式方法
2. 约束分布式鲁棒优化的随机一阶算法
3. 离散有序中值问题的排序分解方法
4. 数据库迁移中测试成本最小化模型研究
5. 变电站防洪两阶段模型比较研究
6. 自利导航服务平台间的充电请求分配协调机制
7. 混合可观测性马尔可夫决策过程中时变区间值参数的迁移强化学习及其在疫情防控中的应用
8. 基于增强交替方向乘子法的线性与锥优化内点法
9. 在线集成生产与配送调度研究：综述与拓展
10. 终结者：基于变量固定的瓦瑟斯坦分布鲁棒机会约束规划求解方法——内外逼近的融合
11. 基于满意化原则的即时出行匹配方法研究
12. 大规模关联网络学习的鲁棒并行追踪方法
13. 基于余弦模式的高效灵活长尾推荐方法
14. 基于融合预训练模型的讽刺言论成因识别方法
15. 面向电力预测模型开发的智能端到端神经架构搜索框架

📋 已完成分析的文章

📂 Research Articles

1. 求解最小和着色问题：替代模型、精确求解器及元启发式方法

原标题: Solving the Minimum Sum Coloring Problem: Alternative Models, Exact Solvers, and Metaheuristics

作者: Yu Du, Fred Glover, Gary Kochenberger, Rick Hennig, Haibo Wang, Amit Hulandageri

DOI: https://doi.org/10.1287/ijoc.2022.0334

Abstract: Abstract The minimum sum coloring problem (MSCP), a well-known NP-hard (nondeterministic polynomial time) problem with important practical applications, has been the subject of several papers in recent years. Because of the computational challenge posed by these problems, most solution methods employed are metaheuristics designed to find high-quality solutions with no guarantee of optimality. Exact methods (like Gurobi) and metaheuristic solvers have greatly improved in recent years, enabling high-quality and often optimal solutions to be found to a growing set of MSCPs. Alternative model forms can have a significant impact on the success of exact and heuristic methods in such settings, often providing enhanced performance compared with traditional model forms. In this paper, we introduce several alternative models for MSCP, including the quadratic unconstrained binary problem plus (QUBO-Plus) model for solving problems with constraints that are not folded into the objective function of the basic quadratic unconstrained binary problem (QUBO) model. We provide a computational study using a standard set of test problems from the literature that compares the general purpose exact solver from Gurobi with the leading QUBO metaheuristic solver NGQ and a special solver called Q-Card that belongs to the QUBO-Plus class. Our results highlight the effectiveness of the QUBO and QUBO-Plus models when solved with these metaheuristic solvers on this test bed, showing that the QUBO-Plus solver Q-Card provides the best performance for finding high-quality solutions to these important problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0334 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0334 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文聚焦于如何将NP困难的最小和着色问题（MSCP）通过构造QUBO及其扩展的QUBO-Plus模型进行数学建模，并探讨不同模型在求解大规模问题时的效果，从而回答在复杂组合优化问题中如何更高效地获得高质量解。

研究方法: 作者通过构建传统线性模型、纯QUBO模型及基于约束拆分的QUBO-Plus模型，对问题进行数学建模，并利用精确求解器（如Gurobi）和多种元启发式算法（如NGQ和自研的Q-Card求解器）进行数值实验和性能比较，采用标准问题实例和大规模基准问题进行验证。

主要发现: 实验结果表明，虽然传统的线性模型在小规模问题上表现良好，但在大规模MSCP实例中，基于QUBO-Plus模型并结合专门设计的Q-Card元启发式求解器能够在求解质量和求解速度上明显优于纯QUBO和传统模型，显著提升了大型问题的求解效率。

管理启示: 研究揭示了在实际管理和商业应用中，通过构建合适的数学模型并结合专门优化算法（尤其是应对大规模复杂优化问题的元启发式策略）可以有效改进调度、资源分配等决策问题的求解效果，为管理者提供了在处理复杂决策问题时采用混合数学建模与智能算法相结合的理论和实践指导。

2. 约束分布式鲁棒优化的随机一阶算法

原标题: Stochastic First-Order Algorithms for Constrained Distributionally Robust Optimization

作者: Hyungki Im, Paul Grigas

DOI: https://doi.org/10.1287/ijoc.2023.0167

Abstract: Abstract We consider distributionally robust optimization (DRO) problems, reformulated as distributionally robust feasibility (DRF) problems, with multiple expectation constraints. We propose a generic stochastic first-order meta-algorithm, where the decision variables and uncertain distribution parameters are each updated separately by applying stochastic first-order methods. We then specialize our results to the case of using two specific versions of stochastic mirror descent (SMD): (i) a novel approximate version of SMD to update the decision variables, and (ii) the bandit mirror descent method to update the distribution parameters in the case of 𝜒 2 [数学公式: 方程00001] -divergence sets. For this specialization, we demonstrate that the total number of iterations is independent of the dimensions of the decision variables and distribution parameters. Moreover, the cost per iteration to update both sets of variables is nearly independent of the dimension of the distribution parameters, allowing for high-dimensional ambiguity sets. Furthermore, we show that the total number of iterations of our algorithm has a logarithmic dependence on the number of constraints. Experiments on logistic regression with fairness constraints, personalized parameter selection in a social network, and the multi-item newsvendor problem verify the theoretical results and show the usefulness of the algorithm, in particular when the dimension of the distribution parameters is large. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: This work was supported by the National Science Foundation [Grant 2112533]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2023.0167 .

研究问题: 本文聚焦于如何高效求解带有多个期望约束的分布鲁棒可行性问题（DRF），即在存在分布不确定性的条件下，如何设计可扩展且计算代价较低的算法，以解决实际问题中鲁棒优化的计算瓶颈。

研究方法: 作者提出了一种随机一阶元算法，通过构建一个元算法框架，分别使用改进型ϵ-随机镜像下降（ϵ-SMD）更新决策变量x，以及带状镜像下降（BMD）更新分布参数p。在此基础上，理论上证明了该算法的高概率收敛性，并通过数值实验（如逻辑回归的公平性约束、个性化参数选择、多品种新闻采购问题）验证了其在大规模高维问题上的优越性能。

主要发现: 研究发现，所提出的随机元算法不仅能够利用ϵ-SMD减少每次迭代的计算成本，且其总体迭代次数及收敛速度对决策变量和分布参数的维度依赖较低，从而在大样本和高维情景下显著优于传统的确定性在线方法，实现了鲁棒优化问题的高效求解。

管理启示: 该研究为管理者提供了在面对不确定环境和多重约束时，如何利用分布鲁棒优化技术进行决策的理论基础和实用方法。特别是在大规模数据、个性化推荐、公平性控制以及库存管理等场景中，这种高效且可扩展的算法能帮助企业在降低风险的同时，更快速地获得可靠的优化决策。

3. 离散有序中值问题的排序分解方法

原标题: Ranking Decomposition for the Discrete Ordered Median Problem

作者: Marilène Cherkesly, Claudio Contardo, Matthieu Gruson

DOI: https://doi.org/10.1287/ijoc.2023.0059

Abstract: Abstract Given a set 𝒩 [数学公式: 方程00001] of size n , a nonnegative, integer-valued distance matrix D of dimensions [数学表达式: 𝑛×𝑛[数学公式: 方程00002]] , an integer [数学表达式: 𝑝∈ℕ[数学公式: 方程00003]] and an integer-valued weight vector [数学表达式: 𝝀∈ℤ𝑛[数学公式: 方程00004]] , the discrete ordered median problem ( DOMP ) consists of selecting a subset 𝒞 [数学公式: 方程00005] of exactly p points from 𝒩 [数学公式: 方程00006] (also referred to as the centers ) so as to: 1) assign each point in 𝒩 [数学公式: 方程00007] to its closest center in 𝒞 [数学公式: 方程00008] ; 2) rank the resulting distances (between every point and its center) from smallest to largest in a sorted vector that we denote 𝑑 * [数学公式: 方程00009] ; 3) minimize the scalar product 〈 𝝀 , 𝑑 * 〉 [数学公式: 方程00010] . The DOMP generalizes several classical location problems such as the p -center, the p -median and the obnoxious median problem. We introduce an exact branch-and-bound algorithm to solve the DOMP . This branch-and-bound decouples the ranking attribute of the problem to form a series of simpler subproblems which are solved using innovative binary search methods. We consider several acceleration techniques such as warm-starts, primal heuristics, variable fixing, and symmetry breaking. We perform a thorough computational analysis and show that the proposed method is competitive against several MIP models from the scientific literature. We also comment on the limitations of our method and propose avenues of future research. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants 2017-06106, 2020-06311, and 2021-03327]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0059 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0059 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 本文试图研究如何高效求解离散有序中值问题（DOMP），尤其是通过构建针对不同权重向量（λ）的单元子问题（DOMP(k)）的二分搜索方法及相应的分支界限算法，来提高该类问题在规模、参数幅度和对称性变化条件下的求解效率与鲁棒性。

研究方法: 作者构建了数学模型，通过分别针对λₖ为正和负的情况设计二分搜索方法求解DOMP(k)子问题，并将这些方法嵌入到一个分支界限算法中。同时，通过大量的计算实验，对比 benchmark 与新生成实例，并与现有几种MIP模型和分支-定价-切割方法的表现进行对比，验证算法的有效性和性能。

主要发现: 研究发现，针对单元权重向量设计的二分搜索方法在求解相关子问题时明显优于传统MIP模型，而整体的分支界限算法在处理参数大、距离矩阵非对称及稀疏权重向量问题时展现出较高的鲁棒性和内存效率，但对于权重向量密集的实例其性能有所下降。

管理启示: 该研究为管理者提供了一种高效、鲁棒的优化求解工具，能够在设施选址、供应链设计、图像聚类及传感器布局等实际场景中降低计算时间和资源消耗，从而为决策实践提供更为精确和高效的解决方案。

4. 数据库迁移中测试成本最小化模型研究

原标题: Models for Test Cost Minimization in Database Migration

作者: Bugra Caskurlu, K. Subramani, Utku Umur Acikalin, Alvaro Velasquez, Piotr Wojciechowski

DOI: https://doi.org/10.1287/ijoc.2023.0021

Abstract: Abstract Database migration is a ubiquitous need faced by enterprises that generate and use vast amounts of data. This is because of database software updates, or it is from changes to hardware, project standards, and other business factors. Migrating a large collection of databases is a way more challenging task than migrating a single database because of the presence of additional constraints. These constraints include capacities of shifts and sizes of databases. In this paper, we present a comprehensive framework that can be used to model database migration problems of different enterprises with customized constraints by appropriately instantiating the parameters of the framework. These parameters are the size of each database, the size of each shift, and the cost of testing each application. Each of these parameters can be either constant or arbitrary. Additionally, the cost of testing an application can be proportional to the number of databases that the application uses. We establish the computational complexities of a number of instantiations of this framework. We present fixed-parameter intractability results for various relevant parameters of the database migration problem. We also provide approximability and inapproximability results as well as lower bounds for the running time of any exact algorithm for the database migration problem. We show that the database migration problem is equivalent to a variation of the classical hypergraph partitioning problem. Our theoretical results also imply new theoretical results for the hypergraph partitioning problem that are interesting in their own right. Finally, we adapt heuristic algorithms devised for the hypergraph partitioning problem to the database migration problem, and we also give experimental results for the adapted heuristics. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: B. Caskurlu and U. U. Acikalin are supported by The Scientific and Technological Research Council of Türkiye [Grant 122E599]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0021 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0021 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文旨在探讨在满足数据库大小、迁移时隙容量、以及应用测试成本等多重实际约束条件下，如何对企业中的数据库进行迁移调度，以最小化应用测试费用，即构建并求解容量受限的数据库迁移（CCDM）问题。

研究方法: 作者首先建立了CCDM问题的数学模型，证明其在各种情况下都是NP难甚至参数化不可解的，并通过与加权最小二分问题及超图划分问题的关系推导出理论结果；同时设计了一种随机化近似算法，并在大量随机生成的实例上使用改进的超图划分启发式算法进行实验验证。

主要发现: 研究表明，无论数据库大小、应用测试成本及迁移时隙等参数如何设定，CCDM问题均是NP难的，并且存在固定参数不可解性；此外，作者提出的随机化近似算法（近似比为((2·k−1)/k+ε)）及改进后的启发式方法在实验中表现出较高的求解质量和效率，与加权最小二分和超图划分问题之间的紧密关联也为后续理论研究提供了新视角。

管理启示: 该研究对管理者的启示在于，企业在进行大规模数据库或云数据中心迁移时需高度关注系统中各数据库的大小、业务关键应用的测试成本和迁移时隙的安排，采用先进的算法工具进行调度和规划，以降低迁移期间的运营风险和成本，提高整体迁移效率与系统可靠性。

5. 变电站防洪两阶段模型比较研究

原标题: Comparisons of Two-Stage Models for Flood Mitigation of Electrical Substations

作者: Brent Austgen, Erhan Kutanoglu, John J. Hasenbein, Surya Santoso

DOI: https://doi.org/10.1287/ijoc.2023.0125

Abstract: Abstract We compare stochastic programming and robust optimization decision models for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. In our models, the first stage captures the deployment of a fixed quantity of flood mitigation resources, and the second stage captures the operation of a potentially degraded power grid with the primary goal of minimizing load shed. To model grid operation, we introduce adaptations of the direct current (DC) and linear programming alternating current (LPAC) power flow approximation models that feature relatively complete recourse by way of an indicator variable. We apply our models to a pair of geographically realistic flooding case studies, one based on Hurricane Harvey and the other on Tropical Storm Imelda. We investigate the effect of the mitigation budget, the choice of power flow model, and the uncertainty perspective on the optimal mitigation strategy. Our results indicate the mitigation budget and uncertainty perspective are impactful, whereas choosing between the DC and LPAC power flow models is of little to no consequence. To validate our models, we assess the performance of the mitigation solutions they prescribe in an alternating current (AC) power flow model. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the Energy Institute, The University of Texas at Austin. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0125 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0125 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文主要探讨如何在飓风来临前，通过制定和优化临时洪水防护措施（如Tiger Dam堤坝）部署策略，增强电网的韧性，减少因洪水引起的负荷中断和电网停运风险，同时平衡模型精度与计算效率之间的权衡。

研究方法: 作者构建了两阶段优化模型，包括基于随机规划（SP）和鲁棒优化（RO）的决策模型，并分别采用直流（DC）电流近似和线性规划交流（LPAC）近似作为电力流的代理模型，通过案例研究（以Tropical Storm Imelda和Hurricane Harvey的数据为例）、数值实验与求解器测试，对不同模型的最优解、计算时间和模型紧缩性进行对比分析。

主要发现: 研究发现，在时间紧迫的洪水防护应用中，虽然LPAC模型提供了更高的电力流精度，但其计算代价较高；相对而言，基于DC模型的方案能够以更快的速度获得近似相同的最佳防护部署方案。同时，不同的不确定性处理视角（“自然是公平”与“自然是对抗”）对最终的资源部署策略具有显著影响，且在防护投资预算逐步增加的情况下，其收益呈现出递减趋势。

管理启示: 该项研究为电网管理者提供了在极端天气风险下提前规划洪水防护和韧性增强的决策工具和方法，建议在实际应用中采用计算效率较高的DC模型进行防护措施优化，同时根据实际的风险偏好选择适当的决策模式，并警惕在资源投入上出现边际效益递减的现象。

6. 自利导航服务平台间的充电请求分配协调机制

原标题: Coordinating Charging Request Allocation Between Self-Interested Navigation Service Platforms

作者: Marianne Guillet, Maximilian Schiffer

DOI: https://doi.org/10.1287/ijoc.2022.0269

Abstract: Abstract Current electric vehicle market trends indicate an increasing adoption rate across several countries. To meet the expected growing charging demand, it is necessary to scale up the current charging infrastructure and to mitigate current reliability deficiencies, for example, due to broken connectors or misreported charging station availability status. However, even within a properly dimensioned charging infrastructure, a risk for local bottlenecks remains if several drivers cannot coordinate their charging station visit decisions. Here, navigation service platforms can optimally balance charging demand over available stations to reduce possible station visit conflicts and increase user satisfaction. Although such fleet-optimized charging station visit recommendations may alleviate local bottlenecks, they can also harm the system if self-interested navigation service platforms seek to maximize their own customers’ satisfaction. To study these dynamics, we model fleet-optimized charging station allocation as a resource allocation game in which navigation platforms constitute players and assign potentially free charging stations to drivers. We show that no pure Nash equilibrium guarantee exists for this game, which motivates us to study VCG mechanisms both in offline and online settings, to coordinate players’ strategies toward a better social outcome. Extensive numerical studies for the city of Berlin show that by coordinating players through VCG mechanisms, the social cost decreases on average by 42% in the online setting and by 52% in the offline setting. History: Accepted by David Alderson, Area Editor for Network Optimization: Algorithms & Applications. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0269 .

研究问题: 论文的核心问题在于如何协调多个自利的导航平台在电动车充电站分配中的资源分配和冲突问题，通过建立游戏理论模型（FCSA游戏）以及采用VCG机制，探讨如何使各平台的利己行为与系统整体效率相协调，从而降低整体社会成本并提高充电体验。

研究方法: 作者采用了理论模型构建和博弈论分析的方法，构建了FCSA游戏来刻画平台间资源分配的动态，并运用VCG机制设计（包括离线和在线、加权扩展）分析机制在稳定策略和社会最优解中的作用，同时辅以大量基于柏林实际充电网络的仿真实验与数据驱动在线策略研究。

主要发现: 研究发现，通过协调各平台采用VCG定价及数据驱动的在线分配策略，可以显著降低社会成本，离线设置下成本降低最多可达52%，在线设置下可降低42%。此外，虽然协调机制略微增加了司机行驶至充电站的平均时间，但大幅减少了充电站冲突，提高了成功分配率；同时，不同平台在司机分配比例上存在差异，小份额平台通常更能受益于VCG协调机制，而大份额平台可能在某些情形下收益较低。

管理启示: 对于管理者来说，研究表明采用中央化的协调机制（如VCG定价机制）以及利用数据驱动的在线分配策略，可以有效优化充电站分配，降低系统整体成本，提升用户充电服务质量。管理者应考虑跨平台合作与信息共享，通过机制设计来缓解平台间的分配冲突，从而打造更加高效稳定的电动车充电网络。

7. 混合可观测性马尔可夫决策过程中时变区间值参数的迁移强化学习及其在疫情防控中的应用

原标题: Transfer Reinforcement Learning for Mixed Observability Markov Decision Processes with Time-Varying Interval-Valued Parameters and Its Application in Pandemic Control

作者: Mu Du, Hongtao Yu, Nan Kong

DOI: https://doi.org/10.1287/ijoc.2022.0236

Abstract: Abstract We investigate a novel type of online sequential decision problem under uncertainty, namely mixed observability Markov decision process with time-varying interval-valued parameters (MOMDP-TVIVP). Such data-driven optimization problems with online learning widely have real-world applications (e.g., coordinating surveillance and intervention activities under limited resources for pandemic control). Solving MOMDP-TVIVP is a great challenge as online system identification and reoptimization based on newly observational data are required considering the unobserved states and time-varying parameters. Moreover, for many practical problems, the action and state spaces are intractably large for online optimization. To address this challenge, we propose a novel transfer reinforcement learning (TRL)-based algorithmic approach that ingrates transfer learning (TL) into deep reinforcement learning (DRL) in an offline-online scheme. To accelerate the online reoptimization, we pretrain a collection of promising networks and fine-tune them with newly acquired observational data of the system. The hallmark of our approach comes from combining the strong approximation ability of neural networks with the high flexibility of TL through efficiently adapting the previously learned policy to changes in system dynamics. Computational study under different uncertainty configurations and problem scales shows that our approach outperforms existing methods in solution optimality, robustness, efficiency, and scalability. We also demonstrate the value of fine-tuning by comparing TRL with DRL, in which at least 21% solution improvement can be yielded by TRL with fine-tuning for no more than 0.62% of time spent on pretraining in each period for problem instances with a continuous state-action space of modest dimensionality. A retrospective study on a pandemic control use case in Shanghai, China shows improved decision making via TRL in several public health metrics. Our approach is the first-ever endeavor of employing intensive neural network training in solving Markov decision processes requiring online system identification and reoptimization. History: Accepted by Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 72371051 and 72201047] to the first and second authors and in part by the National Science Foundation [Grant 1825725] to the third author. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0236 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0236 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 本文主要探讨在存在结构性和观测不确定性（部分状态可观测且参数为时间变化的区间数值）的环境下，如何构建并求解大规模在线序贯决策问题（MOMDP-TVIVP），以及如何利用迁移强化学习（TRL）来实现在非平稳、不完全信息条件下系统模型的在线更新和最优决策。

研究方法: 作者通过构建一个具有混合可观测状态与时间变化区间值参数的Markov决策过程模型，并提出了一种基于迁移强化学习的算法，该方法将深度强化学习（DRL）与迁移学习相结合，采用离线训练（基于actor-critic神经网络和基于种群的超参数调优）和在线微调相结合的方式，通过仿真实验以及疫情控制的案例研究，对模型性能和算法效率进行了验证。

主要发现: 研究发现，所提出的TRL方法在解决MOMDP-TVIVP问题时，相较于传统的DRL方法、滚动时域优化和在线展望方法，不仅能获得更优和更稳健的决策解，还在解决非平稳、不确定性较高的决策问题时展现出更高的算法效率和扩展性；同时，在线的迁移微调在中等不确定区间下能显著提高精准度，但在不确定性极大时可能出现负迁移效应。

管理启示: 该研究为管理者提供了一种新的决策支持工具，能够在数据有限、系统动态变化的实际运营环境中，通过在线学习和实时模型更新，实现对关键运营指标（如公共卫生中的疫情控制、资源分配等）的精准调控，从而在资源有限的条件下达到成本与效果的最佳平衡。

8. 基于增强交替方向乘子法的线性与锥优化内点法

原标题: An Enhanced Alternating Direction Method of Multipliers-Based Interior Point Method for Linear and Conic Optimization

作者: Qi Deng, Qing Feng, Wenzhi Gao, Dongdong Ge, Bo Jiang, Yuntian Jiang, Jingsong Liu, Tianhao Liu, Chenyu Xue, Yinyu Ye, Chuwen Zhang

DOI: https://doi.org/10.1287/ijoc.2023.0017

Abstract: Abstract The alternating-direction-method-of-multipliers-based (ADMM-based) interior point method, or ABIP method, is a hybrid algorithm that effectively combines interior point method (IPM) and first-order methods to achieve a performance boost in large-scale linear optimization. Different from traditional IPM that relies on computationally intensive Newton steps, the ABIP method applies ADMM to approximately solve the barrier penalized problem. However, similar to other first-order methods, this technique remains sensitive to condition number and inverse precision. In this paper, we provide an enhanced ABIP method with multiple improvements. First, we develop an ABIP method to solve the general linear conic optimization and establish the associated iteration complexity. Second, inspired by some existing methods, we develop different implementation strategies for the ABIP method, which substantially improve its performance in linear optimization. Finally, we conduct extensive numerical experiments in both synthetic and real-world data sets to demonstrate the empirical advantage of our developments. In particular, the enhanced ABIP method achieves a 5.8× reduction in the geometric mean of run time on 105 selected linear optimization instances from Netlib, and it exhibits advantages in certain structured problems, such as support vector machine and PageRank. However, the enhanced ABIP method still falls behind commercial solvers in many benchmarks, especially when high accuracy is desired. We posit that it can serve as a complementary tool alongside well-established solvers. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72394360, 72394364, 72394365, 72225009, 72171141, and 72150001] and by the Program for Innovative Research Team of Shanghai University of Finance and Economics. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0017 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0017 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文核心在于探讨如何利用基于ADMM的内点方法（ABIP及其增强版ABIP+）来高效求解大规模线性规划和广义锥规划问题，特别是针对具有特定结构（如PageRank、LASSO与SVM）的模型，以提升求解精度和计算效率。

研究方法: 作者在理论上扩展了ABIP算法，证明了其对广义锥规划问题收敛速度达到O(1/ε)的复杂度，并设计了多种实现策略（重启、半更新、预处理、预测校正、决策树参数选择等），最后通过大量数值实验（基于Netlib、MIPLIB、PageRank、LASSO、SVM和SOCP等标准数据集）对比验证了算法性能。

主要发现: 实验结果显示，经过上述多重策略增强后的ABIP+在迭代次数和运行时间上都有显著降低，并在特定问题（如具有阶梯型结构的PageRank和机器学习中的LASSO与SVM问题）上展现出与领先开源与商业求解器（如PDLP、GUROBI）相竞争甚至优于其性能的优势。

管理启示: 研究成果为管理者提供了一种高效、可扩展的优化求解工具，可应用于大数据背景下的资源配置、供应链优化、金融组合管理及搜索引擎排序等实际业务中，从而降低计算成本、提高决策效率，为商业实践中的大规模优化问题提供了新的技术选择。

9. 在线集成生产与配送调度研究：综述与拓展

原标题: Online Integrated Production and Distribution Scheduling: Review and Extensions

作者: Zhi-Long Chen

DOI: https://doi.org/10.1287/ijoc.2022.0305

Abstract: Abstract As a growing number of manufacturers adopt the make-to-order business mode and a growing number of retailers sell online, we are seeing numerous decision problems that can be modeled as what are known in the literature as integrated production and distribution scheduling (IPDS) problems. In such problems, order processing and delivery must be scheduled jointly in order to achieve an optimal balance between total operational costs and overall customer service. Offline IPDS problems, in which the information about every order is known in advance with certainty, are extensively studied. However, research on online IPDS problems, in which orders arrive randomly with their information unknown until they arrive, is relatively recent but is growing rapidly. In this paper, we first describe several real-world applications to illustrate the importance of studying online IPDS problems from a practical point of view. We then review the existing literature on online IPDS problems with a focus on existing online algorithms for these problems and their theoretical performance. We also derive some new results to fill several gaps left in the literature and discuss possible topics for future research. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0305 .

研究问题: 本文主要探讨如何在实时订单到达的情况下，协调生产调度与配送决策，即在线IPDS问题，以在保证客户服务的同时最小化交付延迟和运输成本。

研究方法: 本文通过文献综述和理论模型构建，对单机、并行机以及单一和多客户场景下的在线IPDS问题进行系统归纳，分析各类调度模型、算法和竞争比的理论结果，通过提出引理、定理与在线算法设计来进行理论分析和证明。

主要发现: 研究总结了各类在线IPDS问题在不同生产与配送模式（如个别立即派送与批量直送）和不同目标函数下的竞争比分析结果，指出在某些特定场景下已获得最优或近似最优算法，同时揭示了诸如有限配送车辆、固定出发时刻以及部分信息已知等实际应用情境下仍存在的研究空白与挑战。

管理启示: 研究对管理者的启示在于：企业在面对实时订单和紧迫交付要求时，应重视生产与配送的协同调度，通过使用基于竞争比的在线决策模型合理配置生产资源和配送车辆，并根据实际约束（如车队数量、固定出发时刻等）调整调度策略，从而有效提升订单履行效率和客户服务质量。

10. 终结者：基于变量固定的瓦瑟斯坦分布鲁棒机会约束规划求解方法——内外逼近的融合

原标题: The Terminator: An Integration of Inner and Outer Approximations for Solving Wasserstein Distributionally Robust Chance Constrained Programs via Variable Fixing

作者: Nan Jiang, Weijun Xie

DOI: https://doi.org/10.1287/ijoc.2023.0299

Abstract: Abstract We present a novel approach aimed at enhancing the efficacy of solving both regular and distributionally robust chance constrained programs using an empirical reference distribution. In general, these programs can be reformulated as mixed-integer programs (MIPs) by introducing binary variables for each scenario, indicating whether a scenario should be satisfied. Whereas existing methods have focused predominantly on either inner or outer approximations, this paper bridges this gap by studying a scheme that effectively combines these approximations via variable fixing. By checking the restricted outer approximations and comparing them with the inner approximations, we derive optimality cuts that can notably reduce the number of binary variables by effectively setting them to either one or zero. We conduct a theoretical analysis of variable fixing techniques, deriving an asymptotic closed-form expression. This expression quantifies the proportion of binary variables that should be optimally fixed to zero. Our empirical results showcase the advantages of our approach in terms of both computational efficiency and solution quality. Notably, we solve all the tested instances from literature to optimality, signifying the robustness and effectiveness of our proposed approach. History: Accepted by Andrea Lodi/Design & Analysis of Algorithms — Discrete. Funding: This work was supported by Office of Naval Research [N00014-24-1-2066]; Division of Civil, Mechanical and Manufacturing Innovation [2246414]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0299 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0299 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文的核心问题是：如何通过引入变量固定技术、big-M系数的强化以及内外近似方法，有效提升鲁棒概率约束程序（RCCP和DRCCP）求解的效率，特别是在Wasserstein模糊集下的情形。

研究方法: 作者采用数学建模和问题重构的方法，对RCCP和DRCCP进行理论推导，提出变量固定技术和big-M系数强化策略，并结合保守内外近似方法构建新的保守近似模型，最后通过大量数值实验对比验证其算法性能。

主要发现: 研究结果表明，利用变量固定技术和big-M系数强化方法可以显著减小模型规模，提高求解速度并缩小最优界隙，同时理论分析证明在一定条件下相当比例的情景变量可以预先固定，从而大幅度提升求解效率。

管理启示: 对于管理实践，该研究提供了一种高效解决在不确定环境下决策问题的优化工具，帮助管理者在供应链、投资组合、能源系统等领域快速获得稳健的决策方案，从而提升企业运作的稳定性和竞争力。

11. 基于满意化原则的即时出行匹配方法研究

原标题: Satisficing Approach to On-Demand Ride Matching

作者: Dongling Rong, Xinyu Sun, Meilin Zhang, Shuangchi He

DOI: https://doi.org/10.1287/ijoc.2021.0210

Abstract: Abstract Online ride-hailing platforms have developed into an integral part of the transportation infrastructure in many countries. The primary task of a ride-hailing platform is to match trip requests to drivers in real time. Although both passengers and drivers prefer a prompt pickup to initiate the trips, it is often difficult to find a nearby driver for every passenger. If the driver is far from the pickup point, the passenger may cancel the trip while the driver is heading toward the pickup point. For the platform to be profitable, the trip cancellation rate must be maintained at a low level. We propose a computationally efficient data-driven approach to ride matching, in which a pickup time target is imposed on each trip request and an optimization problem is formulated to maximize the joint probability of all the pickup times meeting the targets. By adjusting pickup time targets individually, this approach may assign more high-value trip requests to nearby drivers, thus boosting the platform’s revenue while maintaining a low cancellation rate. In numerical experiments, the proposed approach outperforms several ride-matching policies used in practice. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work of D. Rong and X. Sun was supported in part by the National Natural Science Foundation of China [Grant 71971165], the National Key Research and Development Program of China [Grant 2021YFB3301801], the MOE Project of Humanities and Social Science of China [Grant 19YJE630002], and the Soft Science Research Program of Shannxi [Grant 2018KRZ005]. The work of S. He was supported in part by the Singapore Ministry of Education Social Science Research Council [Grant MOE2022-SSRTG-029]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0210 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0210 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文主要探讨如何设计一种既能降低乘客因等待时间过长而取消订单风险，又能通过匹配高价值订单提升平台收益的高效实时叫车匹配算法。

研究方法: 作者构建了一个基于数据驱动的数学模型——P模型，该模型通过设定各订单的接车时间目标，最大化所有接车时间达到目标的联合概率，并将问题转化为最小成本流问题进行求解，同时结合数值实验与仿真（基于实际叫车数据）对模型进行验证与比较。

主要发现: 研究发现，通过调整每个订单的接车时间目标和引入偏好指数，可以在降低订单取消率的同时显著提升平台收入；相较于传统的最小接车距离或高价值优先匹配策略，所设计的满意化匹配方法能更好地平衡乘客体验和平台收益。

管理启示: 该研究建议管理者采用数据驱动的优化匹配策略，通过动态设定接车时间目标和合理区分订单价值，从而降低取消率、提高服务效率和平台盈利能力，同时也为平台如何整合定价与车辆调度等机制提供了参考。

12. 大规模关联网络学习的鲁棒并行追踪方法

原标题: Robust Parallel Pursuit for Large-Scale Association Network Learning

作者: Wenhui Li, Xin Zhou, Ruipeng Dong, Zemin Zheng

DOI: https://doi.org/10.1287/ijoc.2022.0181

Abstract: Abstract Sparse reduced-rank regression is an important tool to uncover the large-scale response-predictor association network, as exemplified by modern applications such as the diffusion networks, and recommendation systems. However, the association networks recovered by existing methods are either sensitive to outliers or not scalable under the big data setup. In this paper, we propose a new statistical learning method called robust parallel pursuit (ROP) for joint estimation and outlier detection in large-scale response-predictor association network analysis. The proposed method is scalable in that it transforms the original large-scale network learning problem into a set of sparse unit-rank estimations via factor analysis, thus facilitating an effective parallel pursuit algorithm. Furthermore, we provide comprehensive theoretical guarantees including consistency in parameter estimation, rank selection, and outlier detection, and we conduct an inference procedure to quantify the uncertainty of existence of outliers. Extensive simulation studies and two real-data analyses demonstrate the effectiveness and the scalability of the suggested approach. History: Accepted by Ram Ramesh, Area Editor/Data Science & Machine Learning. Funding: This work was supported by the National Key R&D Program of China [Grant 2022YFA1008000], Natural Science Foundation of China [Grants 72071187, 72091212, 71731010, and 71921001], China Postdoctoral Science Foundation [Grant 2023M733402], and Fundamental Research Funds for the Central Universities [Grants WK3470000017, WK2040000027, and WK2040000079]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0181 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0181 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 如何在大规模多变量关联网络中准确揭示节点间的潜在联系，尤其是在高维数据中存在异常观测值（离群值）的情况下，构建既具备稀疏性又具有低秩结构的鲁棒回归模型，从而实现对特征与异常值的联合估计和检测？

研究方法: 论文提出了一种新的鲁棒并行追踪方法（ROP），通过构建多变量均值漂移回归模型，将回归系数矩阵与异常值矩阵进行结合，并利用SVD分解和Lasso正则化方法将整体问题分解为若干平行的单秩子问题，继而进行理论证明、仿真实验及两个真实数据集（植物基因数据和用户行为数据）的实证分析来验证方法的有效性与可扩展性。

主要发现: 在满足异常值数量较少的前提下，所提出的方法能够准确恢复出具有稀疏和低秩结构的系数矩阵，其估计误差不受高维问题的影响，并且在模拟实验和实际案例中均展现出比现有方法更优的参数估计、预测性能以及异常值检测能力，证明了该方法在大规模、含异常数据环境下的鲁棒有效性。

管理启示: 该研究为管理者提供了一种高效且鲁棒的数据建模工具，能够在存在异常数据的复杂大规模关联网络中准确识别关键特征和异常观测，从而支持社交网络营销、用户行为分析、产品推荐等商业实践的精细化管理和决策制定。

13. 基于余弦模式的高效灵活长尾推荐方法

原标题: Efficient and Flexible Long-Tail Recommendation Using Cosine Patterns

作者: Yaqiong Wang, Junjie Wu, Zhiang Wu, Gediminas Adomavicius

DOI: https://doi.org/10.1287/ijoc.2022.0194

Abstract: Abstract With the increasing use of recommender systems in various application domains, many algorithms have been proposed for improving the accuracy of recommendations. Among various dimensions of recommender systems performance, long-tail (niche) recommendation performance remains an important challenge in large part because of the popularity bias of many existing recommendation techniques. In this study, we propose CORE, a cosine pattern–based technique, for effective long-tail recommendation. Comprehensive experimental results compare the proposed approach with a wide variety of classic, widely used recommendation algorithms and demonstrate its practical benefits in accuracy, flexibility, and scalability in addition to the superior long-tail recommendation performance. 1 History: Accepted by Ramaswamy Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72031001, 72072091, 72242101]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0194 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0194 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文探讨如何克服传统推荐系统中由于协同过滤等方法导致的流行度偏差问题，从而在保证推荐准确性的同时有效地推荐长尾（小众、利基）产品，即如何准确地提出长尾推荐。

研究方法: 作者构建了一种基于余弦模式（cosine patterns）的推荐算法CORE，利用模式挖掘技术识别消费历史中的项目共现关系，并设计了高效的CP树数据结构来加速候选模式的检索。通过在四个公开数据集（Book-Crossing、Last.fm、Yelp、MovieLens）上进行实验对比（包括与传统关联规则、协同过滤和深度学习方法的比较）来验证算法的准确性和长尾推荐性能。

主要发现: 实验表明，CORE方法能显著提高长尾推荐效果，能够更有效地推荐冷门或小众产品，同时在数据稀疏、分布偏斜的场景下保持较高的推荐准确率。此外，该方法具有高度的灵活性和可扩展性，可根据需要调整推荐中热门与小众项目的比例。

管理启示: 对于管理者而言，该研究提示企业可以通过采用基于余弦模式的长尾推荐算法，不仅满足消费者多样化和个性化需求，还能挖掘长尾产品的潜在市场价值，从而提升用户参与度、增加收益并改善库存管理。此外，推荐系统的灵活调优能力也为平台在不同市场环境下实现精准营销提供了决策支持。

14. 基于融合预训练模型的讽刺言论成因识别方法

原标题: A Fusion Pretrained Approach for Identifying the Cause of Sarcasm Remarks

作者: Qiudan Li, David Jingjun Xu, Haoda Qian, Linzi Wang, Minjie Yuan, Daniel Dajun Zeng

DOI: https://doi.org/10.1287/ijoc.2022.0285

Abstract: Abstract Sarcastic remarks often appear in social media and e-commerce platforms to express almost exclusively negative emotions and opinions on certain instances, such as dissatisfaction with a purchased product or service. Thus, the detection of sarcasm allows merchants to timely resolve users’ complaints. However, detecting sarcastic remarks is difficult because of its common form of using counterfactual statements. The few studies that are dedicated to detecting sarcasm largely ignore what sparks these sarcastic remarks, which could be because of an empty promise of a merchant’s product description. This study formulates a novel problem of sarcasm cause detection that leverages domain information, dialogue context information, and sarcasm sentences by proposing a pretrained language model-based approach equipped with a novel hybrid multihead fusion-attention mechanism that combines self-attention, target-attention, and a feed-forward neural network. The domain information and the dialogue context information are then interactively fused to obtain the domain-specific dialogue context representation, and bidirectionally enhanced sarcasm-cause pair representations are generated for detecting sarcasm spark. Experimental results on real-world data sets demonstrate the efficacy of the proposed model. The findings of this study contribute to the literature on sarcasm cause detection and provide business value to relevant stakeholders and consumers. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the National Natural Science Foundation of China [Grants 72293575, 62071467, and 62141608] and the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants 11500322 and 11500421]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0285 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0285 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 本论文旨在探讨如何通过深度语义交互建模识别和解析引发讽刺评论的原因，即在包含讽刺句、对话上下文和领域信息的复杂语境中，如何准确捕捉出触发讽刺表达的关键原因。

研究方法: 作者提出了一种基于预训练语言模型（如BERT）的讽刺原因检测方法——FPTA-ICSR，该方法融合了多头自注意力、目标注意力和前馈神经网络，通过构建融合领域信息、对话上下文和讽刺句的深度交互模型，并在Reddit和Twitter数据集上通过实验验证其有效性，与多种BERT基础模型进行了比较。

主要发现: 实验结果显示，通过动态融合领域、上下文和讽刺信息的双向交互机制，所提出的FPTA-ICSR方法在识别讽刺原因方面明显优于传统的BERT模型，证明了深度交互语义建模在捕捉讽刺语句与触发原因之间的内在逻辑关系上的关键作用，同时在跨子版块和跨平台的数据上也具有良好的泛化性能。

管理启示: 本研究为管理者提供了借助自动化文本分析识别用户真实不满原因的工具，建议企业及时关注并解析社交媒体上包含讽刺意味的负面反馈，从而能够针对性地改进产品设计、营销策略和服务质量，提升客户满意度和品牌形象，进而促进客户保留和业务增长。

15. 面向电力预测模型开发的智能端到端神经架构搜索框架

原标题: An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development

作者: Jin Yang, Guangxin Jiang, Yinan Wang, Ying Chen

DOI: https://doi.org/10.1287/ijoc.2023.0034

Abstract: Abstract Recent years have witnessed exponential growth in developing deep learning models for time series electricity forecasting in power systems. However, most of the proposed models are designed based on the designers’ inherent knowledge and experience without elaborating on the suitability of the proposed neural architectures. Moreover, these models cannot be self-adjusted to dynamically changed data patterns due to the inflexible design of their structures. Although several recent studies have considered the application of the neural architecture search (NAS) technique for obtaining a network with an optimized structure in the electricity forecasting sector, their training process is computationally expensive and their search strategies are not flexible, indicating that the NAS application in this area is still at an infancy stage. In this study, we propose an intelligent automated architecture search (IAAS) framework for the development of time series electricity forecasting models. The proposed framework contains three primary components, that is, network function–preserving transformation operation, reinforcement learning–based network transformation control, and heuristic network screening, which aim to improve the search quality of a network structure. After conducting comprehensive experiments on two publicly available electricity load data sets and two wind power data sets, we demonstrate that the proposed IAAS framework significantly outperforms the 10 existing models or methods in terms of forecasting accuracy and stability. Finally, we perform an ablation experiment to showcase the importance of critical components in the proposed IAAS framework in improving forecasting accuracy. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: J. Yang, G. Jiang, and Y. Chen were supported by the National Natural Science Foundation of China [Grants 72293562, 72121001, 72101066, 72131005, 71801148, and 72171060]. Y. Chen was supported by the Heilongjiang Natural Science Excellent Youth Fund [YQ2022G004]. Supplemental Material: The software ( Yang et al. 2023 ) that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0034 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0034 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 如何构建并自动优化适用于电力负荷和风能预测的深度神经网络架构，使模型能够自适应不同数据集并显著提高预测准确性？

研究方法: 作者提出了一种基于深度神经网络结构自动搜索的框架(IAAS)，该框架结合函数保持变换（针对RNN和LSTM）、扩展（加宽、加深）、裁剪操作以及强化学习（RL）的决策机制，利用多个演员网络（选择、加宽、加深）和网络池的启发式筛选算法，对电力负荷和风能数据进行实验验证，并与传统机器学习和深度学习模型进行了比较，同时还通过消融实验检验各组件的有效性。

主要发现: 实验结果表明，利用IAAS框架自动搜索得到的神经网络结构在电力负荷和风能预测任务中，可以显著降低RMSE、MAE等误差指标，整体性能优于传统DL方法和现有NAS技术，验证了该框架在自动优化模型结构和提高预测精度方面的有效性与稳健性。

管理启示: 该研究为电力系统管理者提供了一种可以自动调整和优化预测模型的工具，帮助提高电网调度、负荷管理和可再生能源发电的预测准确率，从而能更好地应对电网运行中的不确定性和中断风险，支持实时决策和资源配置。