COMPUTATIONAL MANAGEMENT SCIENCE (CMS) 是一本国际期刊，专注于管理科学的所有计算方面。这些包括计算模型的理论和经验分析；计算统计；约束、无约束、鲁棒、随机和组合优化算法的分析和应用；动态模型，例如动态规划和决策树；用于全局优化、建模、学习和预测的新搜索工具和算法；知识获取的模型和工具。强调计算范式是 CMS 的一个预期特征，将其与更经典的运筹学期刊区分开来。官方引用为：COMPUT MANAG SCI

Computational Management Science (CMS) is an international journal that focuses on all computational aspects of management science. The journal covers theoretical and empirical analyses of computational models, computational statistics, analysis and application of constrained and unconstrained optimization, robust and stochastic optimization, and combinatorial optimization algorithms. It also addresses dynamic models, such as dynamic programming and decision trees, as well as new search tools and algorithms for global optimization, modeling, learning, and prediction, along with models and tools for knowledge acquisition. A key feature of CMS is its emphasis on computational paradigms, distinguishing it from more classical operations research journals.

《计算管理科学》(Computational Management Science) 是一本国际期刊，专注于管理科学领域的所有计算方面。期刊内容涵盖计算模型的理论与实证分析、计算统计、约束与无约束优化、鲁棒性与随机性优化、组合优化算法的分析与应用，以及动态模型（如动态规划和决策树）。此外，期刊还关注用于全局优化、建模、学习和预测的新搜索工具和算法，以及知识获取的模型和工具。强调计算范式是《计算管理科学》的一个显著特征，使其与更传统的运筹学期刊有所区别。

Computational Management Science

Valeria D’Amato

Rita D’Ecclesia

Susanna Levantesi

ESG score prediction through random forest algorithm

通过随机森林算法进行ESG评分预测

Abstract Aneo is one of the first Nordic power companies to apply stochastic programming for day-ahead bidding of hydropower. This paper describes our experiences in implementing, testing, and operating a stochastic programming-based bidding method aimed at setting up an automated process for day-ahead bidding. The implementation process has faced challenges such as generating price scenarios for the optimization model, post-processing optimization results to create feasible and understandable bids, and technically integrating these into operational systems. Additionally, comparing the bids from the new stochastic-based method to the existing operator-determined bids has been challenging, which is crucial for building trust in new procedures. Our solution is a rolling horizon comparison, benchmarking the performance of the bidding methods over consecutive two-week periods. Our benchmarking results show that the stochastic method can replicate the current operator-determined bidding strategy. However, additional work is needed before we can fully automate the stochastic bidding setup, particularly in addressing inflow uncertainty and managing special constraints on our watercourses.

Kristine Klock Fleten

Ellen Krohn Aasgård

Liyuan Xing

Hanne Høie Grøttum

Stein-Erik Fleten

Odd Erik Gundersen

Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling

应用并评估基于随机规划的日前水电调度竞价策略

Anna Battauz

Francesco Rotondi

Optimal liquidation policies of redeemable shares

可赎回股票的最优清算策略

Omid Jadidi

Fatemeh Firouzi

John S. Loucks

Yong Shin Park

Multi-criteria supplier selection problem with fuzzy demand: a newsvendor model

模糊需求下的多准则供应商选择问题：报童模型

Abstract Lift-and-project (L &amp;P) cuts are well-known general 0–1 programming cuts which are typically deployed in branch-and-cut methods to solve MILP problems. In this article, we discuss ways to use these cuts within the framework of Benders’ decomposition algorithms for solving two-stage mixed-binary stochastic problems with binary first-stage variables and continuous recourse. In particular, we show how L &amp;P cuts derived for the master problem can be strengthened with the second-stage information. An adapted L-shaped algorithm and its computational efficiency analysis is presented. We show that the strengthened L &amp;P cuts can significantly reduce the number of iterations and the solution time.

Pavlo Glushko

Csaba I. Fábián

Achim Koberstein

An L-shaped method with strengthened lift-and-project cuts

一种带有加强提升和投影切割的L形方法

Lucas Merabet

Bernardo Freitas Paulo da Costa

Vincent Leclere

Policy with guaranteed risk-adjusted performance for multistage stochastic linear problems

多阶段随机线性问题的保证风险调整绩效政策

Le Xuan Dai

Kien Trung Nguyen

Le Phuong Thao

Pham Thi Vui

Some robust inverse median problems on trees with interval costs

一些具有区间成本的树上的鲁棒逆中位数问题

Abstract Higher order risk measures are stochastic optimization problems by design, and for this reason they enjoy valuable properties in optimization under uncertainties. They nicely integrate with stochastic optimization problems, as has been observed by the intriguing concept of the risk quadrangles, for example. Stochastic dominance is a binary relation for random variables to compare random outcomes. It is demonstrated that the concepts of higher order risk measures and stochastic dominance are equivalent, they can be employed to characterize the other. The paper explores these relations and connects stochastic orders, higher order risk measures and the risk quadrangle. Expectiles are employed to exemplify the relations obtained.

Alois Pichler

Connection between higher order measures of risk and stochastic dominance

高阶风险测度与随机占优之间的联系

Abbreviation	Comput. Manag. Sci.
Journal Impact	1.31
Quartiles(Global)	SOCIAL SCIENCES, MATHEMATICAL METHODS(Q3)

ISSN	1619-697X, 1619-6988
h-index	40

Publication Information	Publisher: Springer，Publishing cycle: 4 issues per year，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2005，Proportion of original research papers: 100.00%，Self Citation Rate:7.70%， Gold OA Rate: 58.51%

Computational Management Science

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ESG score prediction through random forest algorithm

Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling

Optimal liquidation policies of redeemable shares

Multi-criteria supplier selection problem with fuzzy demand: a newsvendor model

An L-shaped method with strengthened lift-and-project cuts

Optimal liquidation policies of redeemable shares

Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling

Policy with guaranteed risk-adjusted performance for multistage stochastic linear problems

Some robust inverse median problems on trees with interval costs

Connection between higher order measures of risk and stochastic dominance