INFORMS Journal on Computing - Volume 37, Issue 5 期刊速递

生成时间: 2025-10-16 13:46:05
期刊: INFORMS Journal on Computing
卷期: Volume 37, Issue 5

📊 AI分析统计

• • 有效文章数: 14 篇
• • 已完成分析: 14 篇
• • 有全文: 14 篇
• • 有摘要: 13 篇
• • 完成率: 93.3%

📚 目录

📂 Introduction to Quantum Computing Area

1. 1. 量子计算领域导论

📂 Research Articles

1. 2. 论风险规避多阶段随机规划在产能规划中的价值
2. 3. 大规模随机网络设计的随机Benders分解方案
3. 4. 二次强度模型的精确模拟
4. 5. 旅行商问题2-OPT邻域的平均情形次二次精确与启发式算法研究
5. 6. 多目标优化问题中凸近似集的高效构建方法
6. 7. 基于时间扩展网络的生物医学样本运输迭代精确算法
7. 8. 改进型组合Benders分解算法在人机协同装配线平衡问题中的应用
8. 9. 基于几何规划的零售业销售计划中促销效应整合研究
9. 10. 鲁棒多阶段决策的全多项式时间近似方案
10. 11. 分支定价算法求解带容量约束的自动驾驶车辆辅助配送问题
11. 12. 共享单车系统中静态再平衡运营的数据驱动优化框架
12. 13. 基于正则化对角距离度量学习的信用评估特征选择与群组效应分析
13. 14. 目标跟踪信息融合问题的计算框架

📋 已完成分析的文章

📂 Introduction to Quantum Computing Area

1. 量子计算领域导论

原标题: Introduction to Quantum Computing Area

作者: Giacomo Nannicini

DOI: https://doi.org/10.1287/ijoc.2025.qcintro.v37.n5

Abstract: 未获取到摘要

研究问题: 该论文探讨了如何在量子计算与运筹学交叉领域中，通过运筹学技术对量子计算的发展、芯片设计、操作优化等问题提供解决方案，即如何利用运筹学方法推动和支持量子计算的研究与应用。

研究方法: 文章采用文献综述和学术编辑领域的论述方式，借助特刊及期刊编辑实践的经验，分析了量子计算与运筹学交叉领域的发展现状和未来趋势。

主要发现: 主要发现指出：随着量子计算科学和技术的快速发展，运筹学在量子硬件设计、运营和算法开发等方面具有重要作用；当前学术界、政府和业界对该领域的兴趣日益增强，这为运筹学应用于量子计算提供了广阔的机遇。

管理启示: 管理者应关注并积极支持跨学科研究，利用运筹学方法去优化量子计算相关的研发、设计和操作流程，同时企业和科研机构应构建平台，促进量子计算与运筹学的深度融合，以抢占未来科技竞争的制高点。

📂 Research Articles

2. 论风险规避多阶段随机规划在产能规划中的价值

原标题: On the Value of Risk-Averse Multistage Stochastic Programming in Capacity Planning

作者: Xian Yu, Siqian Shen

DOI: https://doi.org/10.1287/ijoc.2023.0396

Abstract: Abstract We consider a risk-averse stochastic capacity planning problem under uncertain demand in each period. Using a scenario tree representation of the uncertainty, we formulate a multistage stochastic integer program to adjust the capacity expansion plan dynamically as more information on the uncertainty is revealed. Specifically, in each stage, a decision maker optimizes capacity acquisition and resource allocation to minimize certain risk measures of maintenance and operational cost. We compare it with a two-stage approach that determines the capacity acquisition for all the periods up front. Using expected conditional risk measures, we derive a tight lower bound and an upper bound for the gaps between the optimal objective values of risk-averse multistage models and their two-stage counterparts. Based on these derived bounds, we present general guidelines on when to solve risk-averse two-stage or multistage models. Furthermore, we propose approximation algorithms to solve the two models more efficiently, which are asymptotically optimal under an expanding market assumption. We conduct numerical studies using randomly generated and real-world instances with diverse sizes, to demonstrate the tightness of the analytical bounds and efficacy of the approximation algorithms. We find that the gaps between risk-averse multistage and two-stage models increase as the variability of the uncertain parameters increases and decrease as the decision maker becomes more risk averse. Moreover, a stagewise-dependent scenario tree attains much higher gaps than a stagewise-independent counterpart, whereas the latter produces tighter analytical bounds. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work of Dr. X. Yu was partially supported by the U.S. National Science Foundation Division of Information and Intelligent Systems [Grant 2331782]. 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.2023.0396 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0396 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 探讨在需求不确定且风险厌恶的情况下，如何通过多阶段与两阶段随机规划模型制定最优的产能规划决策，并量化这两种模型之间的差异和价值。

研究方法: 采用数学建模和随机规划方法，构建基于条件风险测度（ECRM）的风险厌恶两阶段模型与多阶段模型，推导模型间上下界和紧性分析，并设计近似算法来高效求解，最后通过数值实验和案例研究对模型性能进行验证。

主要发现: 研究发现，多阶段模型比两阶段模型具有更高的灵活性和较低的总成本，尤其在需求波动性较大时，两者间的目标值差距明显；此外，提出的近似算法能在较短时间内获得近似最优解，并显示当风险厌恶程度提高时，两种模型的结果趋于一致。

管理启示: 该研究为管理者提供了量化选择产能规划模型的指导：当面临较高的需求不确定性时，应采用多阶段模型以降低风险和成本；而在风险厌恶程度较高或不确定性较低时，两阶段模型也能取得较好效果，从而平衡计算复杂性与决策质量。

3. 大规模随机网络设计的随机Benders分解方案

原标题: A Stochastic Benders Decomposition Scheme for Large-Scale Stochastic Network Design

作者: Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet, Periklis Petridis

DOI: https://doi.org/10.1287/ijoc.2023.0074

Abstract: Abstract Network design problems involve constructing edges in a transportation or supply chain network to minimize construction and daily operational costs. We study a stochastic version where operational costs are uncertain because of fluctuating demand and estimated as a sample average from historical data. This problem is computationally challenging, and instances with as few as 100 nodes often cannot be solved to optimality using current decomposition techniques. We propose a stochastic variant of Benders decomposition that mitigates the high computational cost of generating each cut by sampling a subset of the data at each iteration and nonetheless, generates deterministically valid cuts, via a dual averaging technique, rather than the probabilistically valid cuts frequently proposed in the stochastic optimization literature. We implement both single-cut and multicut variants of this Benders decomposition as well as a variant that uses clustering of the historical scenarios. To our knowledge, this is the first single-tree implementation of Benders decomposition that facilitates sampling. On instances with 100–200 nodes and relatively complete recourse, our algorithm achieves 5%–7% optimality gaps compared with 16%–27% for deterministic Benders schemes, and it scales to instances with 700 nodes and 50 commodities within hours. Beyond network design, our strategy could be adapted to generic two-stage stochastic mixed-integer optimization problems where second-stage costs are estimated via a sample average. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: The work of R. Cory-Wright was supported in part by the MIT-IBM Research Lab for Goldstine postdoctoral fellowship. J. Pauphilet was funded by the Research and Materials Development Fund [RAMD_Pauphilet_J_22/23_8789] at London Business School. 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.2023.0074 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0074 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 本文探索的是如何利用随机采样与Benders分解技术相结合，构建一个能够处理大规模随机网络设计问题，并同时保证证实性（可证最优性）的高效求解方法。

研究方法: 作者构造了多阶段的混合整数优化模型，通过在Benders分解中嵌入随机采样和双重平均技术，分别提出了单切割、多切割及加速多切割等算法，并在单树分支切割框架下进行数值实验，验证算法在大规模问题上的性能。

主要发现: 实验结果显示，所提出的随机Benders分解算法能显著改善求解效率，在100-200节点的实例中取得约5%-7%的最优性间隙，而传统的确定性Benders方法间隙较大；在700节点和50种商品的问题上也能获得约30%的界限间隙，大幅提升了大规模随机网络设计问题的求解能力。

管理启示: 该研究为企业管理者提供了一种高效的网络设计决策工具，在物流、供应链、交通等领域中可用于处理不确定性因素下的网络规划问题，从而支持更加科学、及时和经济的战略决策。

4. 二次强度模型的精确模拟

原标题: Exact Simulation of Quadratic Intensity Models

作者: Yan Qu, Angelos Dassios, Anxin Liu, Hongbiao Zhao

DOI: https://doi.org/10.1287/ijoc.2023.0323

Abstract: Abstract We develop efficient algorithms of exact simulation for quadratic stochastic intensity models that have become increasingly popular for modeling events arrivals, especially in economics, finance, and insurance. They have huge potential to be applied to many other areas such as operations management, queueing science, biostatistics, and epidemiology. Our algorithms are developed by the principle of exact distributional decomposition, which lies in a fully analytical expression for the joint Laplace transform of quadratic process and its integral newly derived in this paper. They do not involve any numerical Laplace inversion, have been validated by extensive numerical experiments, and substantially outperform all existing alternatives in the literature. Moreover, our algorithms are extendable to multidimensional point processes and beyond Cox processes to additionally incorporate two-sided random jumps with arbitrarily distributed sizes in the intensity for capturing self-exciting and self-correcting effects in event arrivals. Applications to portfolio loss modeling are provided to demonstrate the applicability and flexibility of our algorithms. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the Beijing University of Posts and Telecommunications [Grant 2022RC58], the Shanghai University of Finance and Economics [Grant 2020110930], the National Natural Science Foundation of China [Grant 71401147], and Graduate Innovation Fund of Shanghai University of Finance and Economics [CXJJ-2023-387]. 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.2023.0323 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0323 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 本文核心在于探讨如何精确地模拟具有二次随机强度特性的点过程，特别是在考虑自激发、自校正以及多维协跳等扩展情况下，如何构造既准确又高效的仿真算法，从而解决传统方法中存在的近似性、计算量大和精度不足的问题。

研究方法: 作者采用了理论推导与模型构建相结合的方法。首先，通过解析推导获得了二次过程及其积分的联合Laplace变换，并利用严格的分布分解原理构造出精确的模拟算法；同时，设计了多种算法（例如基于接受拒绝法和数值优化的算法）并通过大量数值实验和案例比较（与离散化方法、投影法等对比）验证了算法的正确性和效率。

主要发现: 最重要的发现是提出了一套全新的基于精确分布分解原理的精确模拟算法，能够高效、准确地模拟具有二次强度特性及其扩展模型，包括自激发、自校正以及多维协跳的点过程。数值实验表明，该算法在精度和计算速度上显著优于传统的离散化方法和现有的投影算法。

管理启示: 该研究为管理者和金融风险管理实践提供了一个更可靠的工具，可以帮助他们在信用风险、投资组合损失以及其他涉及事件到达率的领域中进行精确评估和定价。利用这一精确模拟方法，企业可以更好地捕捉风险动态，制定更加科学的风险缓释与资产定价策略，同时也能应用于各种业务领域（如呼叫中心、在线流量监控等）的事件建模和决策支持。

5. 旅行商问题2-OPT邻域的平均情形次二次精确与启发式算法研究

原标题: Average Case Subquadratic Exact and Heuristic Procedures for the Traveling Salesman 2-OPT Neighborhood

作者: Giuseppe Lancia, Paolo Vidoni

DOI: https://doi.org/10.1287/ijoc.2023.0169

Abstract: Abstract We describe an exact algorithm for finding the best 2-OPT move that, experimentally, was observed to be much faster than the standard quadratic approach for a large part of a best-improvement local search convergence starting at a random tour. To analyze its average-case complexity, we introduce a family of heuristic procedures and discuss their complexity when applied to a random tour in graphs whose edge costs are either uniform random numbers in [0, 1] or Euclidean distances between random points in the plane. We prove that, for any probability p , there is a heuristic in the family that can find the best 2-OPT move with probability at least p in average-time 𝑂 ( 𝑛 ⁢ l o g ⁡ 𝑛 [数学公式: 方程00001] ) for uniform instances and 𝑂 ⁡ ( 𝑛 ) [数学公式: 方程00002] for Euclidean instances. The exact algorithm is then proved to be even faster in the sense that in those instances in which a heuristic finds the best move, the exact algorithm finds it in a smaller time. We give empirical evidence that a slight variant of our algorithm finds the best move in 𝑂 ⁡ ( 𝑛 ) [数学公式: 方程00003] time on both types of instances, achieving the best possible performance for this particular problem. Computational experiments are reported to show the effectiveness of our algorithms, both in best-improvement and in first-improvement 2-OPT local search. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. 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.2023.0169 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0169 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 如何设计并验证一种能在2-OPT局部搜索中更快地找到最佳换边移动（最佳2-OPT移动），从而提高旅行商问题（TSP）求解效率的算法？

研究方法: 作者通过构造两种确定性算法（A_g和A_L）以及一系列可调节的启发式算法H(δₙ)，并结合平均情况复杂度分析、概率理论推导和大量的计算机实验（包括仿真实验、对比综合表和图示分析）来验证这些算法在不同实例（均匀随机和欧几里得实例）下的性能。

主要发现: 研究证明在均匀随机实例下，算法A_g的平均时间复杂度为O(n log n)；而在欧几里得实例中，该算法能达到平均线性时间（O(n)），大幅减少了评估2-OPT移动的次数，且实验结果显示与传统的两重循环算法相比，可实现数量级的加速，混合策略还能在局部最优阶段进一步提高整体效率。

管理启示: 该研究为管理者提供了一种高效的局部搜索求解策略，能够在物流规划、运输调度及其他组合优化问题中快速获得高质量解，从而降低计算成本、缩短决策时间，并在实际应用中提升运营效率。

6. 多目标优化问题中凸近似集的高效构建方法

原标题: Efficiently Constructing Convex Approximation Sets in Multiobjective Optimization Problems

作者: Stephan Helfrich, Stefan Ruzika, Clemens Thielen

DOI: https://doi.org/10.1287/ijoc.2023.0220

Abstract: Abstract Convex approximation sets for multiobjective optimization problems are a well-studied relaxation of the common notion of approximation sets. Instead of approximating each image of a feasible solution by the image of some solution in the approximation set up to a multiplicative factor in each component, a convex approximation set only requires this multiplicative approximation to be achieved by some convex combination of finitely many images of solutions in the set. This makes convex approximation sets efficiently computable for a wide range of multiobjective problems: even for many problems for which (classic) approximations sets are hard to compute. In this article, we propose a polynomial-time algorithm to compute convex approximation sets that builds on an exact or approximate algorithm for the weighted sum scalarization and is therefore applicable to a large variety of multiobjective optimization problems. The provided convex approximation quality is arbitrarily close to the approximation quality of the underlying algorithm for the weighted sum scalarization. In essence, our algorithm can be interpreted as an approximate version of the dual variant of Benson’s outer approximation algorithm. Thus, in contrast to existing convex approximation algorithms from the literature, information on solutions obtained during the approximation process is utilized to significantly reduce both the practical running time and the cardinality of the returned solution sets while still guaranteeing the same worst-case approximation quality. We underpin these advantages by the first comparison of all existing convex approximation algorithms on several instances of the triobjective knapsack problem and the triobjective symmetric metric traveling salesman problem. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research was supported by the German Research Foundation [Project 398572517]. 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.2023.0220 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0220 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文主要探讨如何通过加权和标量化方法构造高效、近似质量良好的凸近似集，以解决多目标（尤其是最大化问题）优化中传统加权和方法在近似效果和计算可行性上的局限性。

研究方法: 作者提出了一种基于加权和标量化的自适应算法，该算法结合了Benson外近似算法的对偶变体、乘法网格离散化以及权重向量的四舍五入技术，通过理论证明和计算实验（在三目标背包问题和对称度量旅行商问题上的性能比较）验证了算法的正确性、近似质量和多项式运行时间。

主要发现: 研究表明，该自适应算法能够在多目标最小化和最大化问题中构造出具有逼近加权和标量化近似质量的凸近似集，其解集大小多项式有界，并且在实践中相比于现有方法（如传统网格方法和Diakonikolas等算法）运行时间更短、解集规模更小，同时达到相似甚至更好的近似指标。

管理启示: 该研究为管理者在多目标决策制定中提供了一种高效、可靠的计算工具，能够帮助企业在平衡成本、效益、安全等多个冲突目标时快速生成合理的备选方案，从而支持科学决策和资源分配优化。

7. 基于时间扩展网络的生物医学样本运输迭代精确算法

原标题: An Iterative Exact Algorithm over a Time-Expanded Network for the Transportation of Biomedical Samples

作者: Daniel M. Ocampo-Giraldo, Ana M. Anaya-Arenas, Claudio Contardo

DOI: https://doi.org/10.1287/ijoc.2023.0061

Abstract: Abstract In this article we propose an iterative algorithm to address the optimization problem of distributing a set of multiple highly perishable commodities in a healthcare network. In the biomedical sample transportation problem, numerous commodities with short lifespans presume multiple transportation requests at the same facility in a day and restrict the maximum time to reach their destination. These two characteristics create an interdependency between the routing and the pickup decisions in time that is highly complex. To address these timing issues, we model this problem as a service network design problem over a time-expanded network. Our solution method aggregates the network at two levels. First, the commodities are aggregated and artificially consolidated, reducing the symmetry arising when multiple transportation requests are solicited within a short period of time. Second, the space-time nodes in the network are constructed dynamically, thus reducing the size of the mathematical model to be solved at each iteration. Moreover, the method creates auxiliary networks to calculate good-quality primal bounds to the problem. Our algorithm proves to be efficient to solve a set of real-life instances from the Quebec laboratory network under the management of the Ministère de la Santé et des Services sociaux (Ministry of Health and Social Services) with a detailed network of up to 2,377 periods and 277 transportation requests. 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 2018-04609, 2020-06311]. 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.2023.0061 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0061 ). 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生物医学样本运输中，考虑样本的易腐性及其生成时刻等特性，通过优化取样调度和车辆路线，实现样本在规定寿命内安全送达实验室，同时降低运输成本的问题。

研究方法: 作者构建了基于时间扩展网络的服务网络设计模型，将每个样本视为独立商品，并结合动态离散化发现算法及商品分组技术，进行数学建模和精确求解。同时，通过对真实案例数据的计算实验进行验证。

主要发现: 研究发现，新模型能够自动决定最优取样次数和车辆调度方案，相较于传统的车辆路径问题模型，该方法在降低取样次数、缩短总行驶距离和运输成本方面具有明显优势，并能处理大规模、高细节度的问题实例。

管理启示: 研究为医疗物流管理提供了一种灵活、精细的调度工具，管理者可利用该方法自动确定最佳取样时机、最优路线以及所需车队规模，从而提升运输效率、确保样本质量并降低整体运营成本。

8. 改进型组合Benders分解算法在人机协同装配线平衡问题中的应用

原标题: An Improved Combinatorial Benders Decomposition Algorithm for the Human-Robot Collaborative Assembly Line Balancing Problem

作者: Dian Huang, Zhaofang Mao, Kan Fang, Enyuan Fu, Michael L. Pinedo

DOI: https://doi.org/10.1287/ijoc.2023.0279

Abstract: Abstract As an emerging technology, human-robot collaboration (HRC) has been implemented to enhance the performance of assembly lines and improve the safety of human workers. By integrating the advantages of human workers and collaborative robots (cobots), HRC enables production systems to process tasks consecutively, concurrently, or collaboratively. However, the introduction of cobots also makes the corresponding human-robot collaborative assembly line balancing problem more complex and difficult to solve. To solve this problem, we first propose an enhanced mixed integer program (EMIP) with various enhancement techniques and tighter bounds, and then, we develop an improved combinatorial Benders decomposition algorithm (Algorithm ICBD) with new local search strategies, Benders cuts, and acceleration procedures. To verify the effectiveness of our proposed model and algorithms, we conduct extensive computational experiments, and the results show that our proposed EMIP model is significantly better than the existing mixed integer program model; the percentages of instances that can obtain feasible and optimal solutions are increased from 82.42% to 100% and from 29.17% to 43.5%, respectively, whereas the average gap is decreased from 19.81% to 5.64%. In addition, our proposed Algorithm ICBD can get 100% of feasible solutions and 65.92% of optimal solutions for all of the test instances, and the average gap is only 1.49%. Moreover, compared with existing Benders decomposition methods for this problem, our approach yields comparatively better solutions in notably shorter average computational time when run in the same computational environment. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research was supported by the National Natural Science Foundation Council of China [Grants 72401214, 92167206, 7221101377, 72471169, and 72231005], the Ministry of Education of China [Grant 24YJC630078], and Computation and Analytics of Complex Management Systems (Tianjin University). This research was also supported by the Tianjin Natural Science Foundation Project [Grant 23JCQNJC01900] and the Tianjin Philosophy and Social Science Planning Project [Grant TJGL21-016]. 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.2023.0279 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0279 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 本文主要探讨如何在人机协作装配线上平衡任务分配和调度，即在给定工位数量的情况下，如何通过优化任务、协作机器人与人工的分配和工作方式（手动、自动、协作），以最小化生产周期时间，解决HRCALBP-II这一NP难问题。

研究方法: 作者构建了一个改进的混合整数规划模型（EMIP），引入了更紧的上下界、作业最早最晚站规划及其他有效不等式，并基于此提出了改进的组合Benders分解算法（Algorithm ICBD），结合局部搜索、两阶段求解策略和加速技巧，通过大量基准实例的计算实验验证其效果。

主要发现: 研究发现，所提出的EMIP模型和ICBD算法相比现有模型和算法在求解HRCALBP-II方面能够获得更优的周期时间、更低的最优性缺口和更显著的计算时间缩短，尤其在中大型实例中取得了更好的求解效果和效率。

管理启示: 对于制造业管理者而言，该研究提供了一种高效分配任务与资源的方法，能够帮助企业优化装配线平衡、缩短生产周期，提高生产灵活性与效率，尤其适合中小企业利用协作机器人实现自动化升级、降低成本和提升产品标准化水平。

9. 基于几何规划的零售业销售计划中促销效应整合研究

原标题: Incorporating Promotional Effects in Sales Planning of the Retail Industry Using Geometric Programming

作者: Melika Khandan, Pooya Hoseinpour

DOI: https://doi.org/10.1287/ijoc.2023.0275

Abstract: Abstract This paper addresses the challenge faced by managers in the fast-moving consumer goods industry: the joint optimization of promotion prices and the scheduling of promotion vehicles for multiple items to boost total profit. We first propose a general multiplicative demand function that encompasses all crossperiod effects, crossitem effects, promotion vehicle effects, and crossterm effects of promotion vehicles. Then, we formulate the problem of planning sales promotions, simultaneously using price reductions and promotion vehicles, considering several business rules as constraints. To efficiently solve this mixed-integer nonlinear program, we reformulate it as a convex optimization form by using the demand function’s multiplicative structure and the concept of geometric programming. Furthermore, to reduce the running time of the large-scale instances, we develop a Lagrangian decomposition algorithm, dividing the original model into a geometric program and an integer program. The algorithm significantly improves computational efficiency as evidenced by a reduction in running time from 8,125 to 78 seconds for large-scale instances. Finally, utilizing real sales data from a meal delivery company, we demonstrate that applying the convex promotion optimization model allows the company to increase its profits by roughly 21% compared with scenarios where neither price reductions nor promotion vehicles are utilized. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. 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.2023.0275 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0275 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 如何在考虑跨期、跨品以及促销载体相互作用的情况下，构建一个能够同时优化促销价格和促销载体排程的模型，以最大化快消品零售业的总利润？

研究方法: 作者构建了基于乘积型需求函数的多周期、多品类销售促销优化模型，通过混合整数非线性规划（MINLP）形式建模，并利用几何规划的凸化技术将其转换为混合整数凸规划，同时开发了拉格朗日分解算法。文中通过数值实验和实际案例研究（基于食品公司的真实销售数据）验证了模型和算法的有效性。

主要发现: 同时优化促销价格和促销载体的综合模型能够显著提高零售利润，数值实验显示在适当设置促销方案时，总利润可提升约21%。此外，所提出的凸模型和拉格朗日分解算法在保证所有业务规则的基础上有效缩短了运算时间，为大规模问题提供了可行的求解方案。

管理启示: 研究为零售管理者提供了一种基于数学建模的决策支持工具，建议管理者在促销规划中应同时考虑价格折扣和促销载体的合理搭配，遵循品牌与形象的约束条件，通过科学的优化方法提升促销效果和企业盈利能力。

10. 鲁棒多阶段决策的全多项式时间近似方案

原标题: Fully Polynomial Time Approximation Schemes for Robust Multistage Decision Making

作者: Nir Halman, Giacomo Nannicini

DOI: https://doi.org/10.1287/ijoc.2023.0126

Abstract: Abstract We design a framework to obtain Fully Polynomial Time Approximation Schemes (FPTASes) for adjustable robust multistage decision making under the budgeted uncertainty sets introduced by Bertsimas and Sim. We apply this framework to the robust counterpart of three problems coming from operations research: (i) ordered knapsack, (ii) single-item inventory control, and (iii) single-item batch dispatch. Our work gives the first FPTAS for these problems, and for adjustable robust multistage decision making in general. The proposed approximation schemes are constructed with the technique of K -approximation sets and functions, relying on careful robust dynamic programming formulations for a master problem (corresponding to the decision maker) and for an adversary problem (corresponding to nature, which chooses bad realizations of uncertainty for the decision maker). The resulting algorithms are short and simple, requiring just a few concise subroutines. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This research was supported in part by the United States-Israel Binational Science foundation [Grant 2018095). N. Halman was also supported in part by the Israel Science Foundation [Grants 399/17 and 1074/21].

研究问题: 论文主要探讨在面对不确定性（尤其是预算约束的不确定性）和对抗性环境下，如何构建适用于多阶段决策问题的可调鲁棒优化模型，并设计出具有多项式时间复杂度的完全多项式时间近似方案（FPTAS），以求得近似最优解的问题。

研究方法: 作者通过构建动态规划（DP）模型，将决策者与对手（不确定性）之间的交互过程形式化，再利用K-近似集和K-近似函数技术设计和证明FPTAS算法的正确性。并以有序背包问题、单一物料库存控制问题和单一物料批量调度问题为例进行了模型构建与算法应用。

主要发现: 论文的主要发现是成功提出了第一批针对非平凡可调鲁棒优化问题的FPTAS算法，通过交替求解决策者问题和对手问题，并利用K-近似技术对动态规划进行近似，实现了在预算不确定性下多阶段决策问题的高效求解，保证了常数因子的近似精度和多项式运行时间。

管理启示: 该研究为企业管理者提供了一种有效的决策支持工具，帮助管理者在面对市场需求、供应链和库存不确定性时作出更为稳健和动态的决策。通过应用该近似算法，可以平衡风险与收益，提升决策效率和系统鲁棒性，从而在实际运营中实现成本节约和资源最优化配置。

11. 分支定价算法求解带容量约束的自动驾驶车辆辅助配送问题

原标题: Branch-and-Price for the Capacitated Autonomous Vehicle Assisted Delivery Problem

作者: Rui Zhang

DOI: https://doi.org/10.1287/ijoc.2023.0177

Abstract: Abstract In recent years, the exponential growth of package volumes has posed significant challenges for logistics networks, particularly in the realm of last-mile delivery. To mitigate costs while upholding service and delivery commitments, companies are increasingly investigating autonomous assisted delivery as a viable solution. In this paper, we study the Capacitated Autonomous Vehicle Assisted Delivery Problem, where an autonomous vehicle works in conjunction with a delivery person. The autonomous vehicle drops off the delivery person at designated locations, and the delivery person completes the deliveries (with a capacity constraint) on foot to the final addresses. Once the deliveries are completed, the vehicle picks up the delivery person and travels to the next reloading point. The goal is to decide on a route to serve all customers while minimizing the route completion time. We introduce an integer programming formulation with exponentially many variables and develop a branch-and-price approach. For generating promising columns, we present a tailored pulse algorithm to solve the pricing problem. Furthermore, by leveraging the structural properties of optimal solutions, we carefully design algorithmic enhancements, valid inequalities, and preprocessing steps to improve computational tractability. By conducting computational experiments based on instances derived from real-world data, we demonstrate the positive impact of these components. More importantly, we provide optimal certificates for 426 out of the 450 instances documented in the literature. Among the 100 instances in which driving could be slower than walking, we report solutions for the 40 largest instances for the first time. History: Accepted by David Alderson, Area Editor for Network Optimization: Algorithms & Applications. 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.2023.0177 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0177 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文聚焦于最后一公里配送中如何利用自动驾驶车辆与人工配送相结合的模式，通过制定严格的数学模型来解决配送路线优化、同步与成本控制问题，探讨如何在不同实际场景下实现高效、经济的配送系统。

研究方法: 作者采用数学建模和整数规划的方法，构建了多种IP模型（RCT1、RCT2、IP1、IP2）并设计了基于列生成和分支定价的算法，同时利用真实数据实例进行大量计算实验，比较不同模型和算法在求解效率和最优性上的表现。

主要发现: 研究表明，通过提出更紧密的模型（如IP2）和分支定价方法，可以有效缩短配送路线完成时间、降低运营成本，并在较大规模和大容量的实例中取得最优或近优解，验证了无人驾驶辅助配送模式在实际应用中的显著优势。

管理启示: 该研究为物流和配送管理者提供了决策支持，建议在最后一公里配送中采用自动驾驶与人工配送混合的模式，通过优化路径规划和同步调度来提高效率和降低成本，同时强调在实际推广过程中应结合具体的客户地理和配送需求进行模型适应性调整。

12. 共享单车系统中静态再平衡运营的数据驱动优化框架

原标题: A Data-Driven Optimization Framework for Static Rebalancing Operations in Bike Sharing Systems

作者: Junming Liu, Weiwei Chen, Leilei Sun

DOI: https://doi.org/10.1287/ijoc.2022.0182

Abstract: Abstract Bike sharing systems have been widely deployed in urban cities for first- and last-mile transportation. However, because of the geographical and temporal imbalance of bike demand, bikes need to be reallocated system-wide among stations during the night to maintain a high service level while minimizing demand loss due to stockout or overcapacity. Two technical challenges remain in optimizing the static bike rebalancing operations. One challenge is to accurately predict bike pickup and dropoff demand at each station, considering demand substitution effects and subsequently determining the optimal rebalancing quantity for each station. The other is to efficiently optimize the routing of multiple rebalancing vehicles for large-scale bike sharing systems, considering outlier stations with rebalancing quantities exceeding vehicle capacity. To this end, we propose an end-to-end solution to tackle the aforesaid challenges. Specifically, we first develop deep learning-based predictors that capture the time dependencies of station-level demand, the impact of weather conditions, and the demand substitution effect by nearby stations. Based on the demand rate, a sequential simulation-based demand loss estimator is developed to find the optimal rebalancing quantities that lead to the minimum expected demand loss. Then, a mixed integer linear programming model is formulated to optimize the routing problem of rebalancing vehicles. To address the computational challenge, we propose a data-driven decomposition algorithm to support a multivehicle multivisit rebalancing strategy by decomposing the multivehicle routing problem into smaller and tractable single-vehicle routing problems, which can be solved in parallel. Finally, extensive numerical experiments using real-world data from New York City Citi Bike demonstrate the accuracy of the proposed bike demand predictors, the impact of demand substitution, and the efficiency of the data-driven optimization framework. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201222] and the Hong Kong Research Grants Council [Grants CityU 21500220 and CityU 11504322]. 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.2022.0182 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0182 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 如何基于多源数据和深度学习方法（尤其考虑需求替代效应）精准预测共享单车的取车与还车需求，并制定最优的静态再平衡调度策略，以降低系统需求损失和运营成本？

研究方法: 论文利用多源数据构建了基于NARX神经网络的需求预测模型，融合时间序列、气象信息及邻近站点的需求替代效应；进而构建了静态再平衡的模拟与优化模型，通过整数规划及聚类分解（CBD和SCA算法）设计求解大规模多车辆路径调度问题。

主要发现: 研究表明，考虑需求替代效应的NARX模型能更准确地预测各站点的需求；基于数据驱动的静态再平衡优化框架（尤其是采用聚类与分解算法）能显著降低预期需求损失和调度成本，有效提升共享单车系统的服务水平。

管理启示: 管理者应采用数据驱动的预测与调度工具，提前在夜间进行静态再平衡操作，使各站点库存调整至更优水平，从而延缓空闲或过载时刻；同时结合需求替代效应，配合白天动态再平衡，优化资源配置并降低运营成本。

13. 基于正则化对角距离度量学习的信用评估特征选择与群组效应分析

原标题: Feature Selection and Grouping Effect Analysis for Credit Evaluation via Regularized Diagonal Distance Metric Learning

作者: Tie Li, Gang Kou, Yi Peng, Philip S. Yu

DOI: https://doi.org/10.1287/ijoc.2023.0322

Abstract: Abstract In credit evaluation, feature selection and grouping effect analysis are used to identify the most relevant credit risk features. Most feature selection and grouping effect analysis are implemented via regularizing linear models. Nevertheless, substantial evidence shows that credit data are linearly inseparable due to heterogeneous credit customers and various risk sources. Although many nonlinear models have been proposed in the last two decades, the majority of them required recombination of the original features, which made it difficult to interpret the results of the models. To cope with this dilemma, we propose a diagonal distance metric learning model that improves distance metrics by rescaling the features. Meanwhile, feature selection and grouping effect analysis are realized by adding regularizations to the model. The main merit of the proposed model is that it avoids the limitation of the linear models by not pursuing linear separability, yet guaranteeing the interpretability. We also prove and explain why feature selection and grouping effect can be achieved and decompose the optimization problem into parallel linear programming problems, plus a small quadratic consensus-reaching problem, such that the optimization can be efficiently solved. Experiments using a real credit data set of 96,000 instances show that the proposed model improves the area under the receiver operating characteristic curve (AUC) of the distance-based classifier k -nearest neighbors by 14% in two-class credit evaluation and surpasses linear models in terms of accuracy, true positive rate, and AUC. The proposed regularized diagonal distance metric learning approach also has the potential to be applied to other fields where data are linearly inseparable. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: Financial support from the National Social Science Fund of China [Grant 23BTJ040] and the National Natural Science Foundation of China [Grants 72471047 and 71910107002] is gratefully acknowledged. 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.2023.0322 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0322 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 论文聚焦如何在信用评估中克服信用数据非线性可分性的问题，通过构建对角化的距离度量学习模型，利用ElasticNet正则化实现特征选择和分组效应分析，从而全面捕捉信用风险的各种信号。

研究方法: 作者构建了基于对角化DML模型的数学优化框架，在模型中引入ElasticNet正则化（结合L1和L2范数），并通过理论证明特征分组效应与特征选择的数学属性。同时，提出了一种基于ADMM的并行求解器来高效解决大规模线性规划问题，并利用真实信用数据进行实证实验和对比分析。

主要发现: 研究表明，所提出的对角DML模型能够显著改善距离基础分类器（如KNN）的预测性能，提升AUC和准确率；ElasticNet正则化有效地保留了高度相关的信用风险特征，避免单一特征选择可能遗漏的重要风险信息；同时，模型在计算效率和稳健性方面均表现出较大优势。

管理启示: 对于金融机构和风险管理者，采用该模型可以在保证模型解释性的前提下，更全面地捕捉和评估信用风险因素，从而提升信用评估的准确性和稳定性，为风险控制和信贷决策提供科学的数据支持和理论依据。

14. 目标跟踪信息融合问题的计算框架

原标题: Computational Framework for Target Tracking Information Fusion Problems

作者: Tianyu Yang, Jiongbai Liu, Tasnim Ibn Faiz, Chrysafis Vogiatzis, Md. Noor-E-Alam

DOI: https://doi.org/10.1287/ijoc.2023.0016

Abstract: Abstract In this work, we propose computationally tractable techniques for extracting valuable information from diverse data sources collected by multiple sensors in a variety of formats (visual, sonar, quantitative, qualitative, social information, etc.). More specifically, we develop an integrated approach consisting of two algorithms for extracting information and achieving a consensus-based, robust solution. The first algorithm extracts solutions from sensors within each data source, whereas the second algorithm reaches a compromise among the generated solutions from the previous algorithm across all data sources. To accomplish these goals, we initially transform the multisensor multitarget tracking problem (MSMTT) problem into a multidimensional assignment problem. Subsequently, we introduce a decomposition-based multisensor recursive approach referred to as a revised multisensor recursive algorithm, which can efficiently deliver a robust solution for each single data source MSMTT problem. In the second algorithm, we extend our methodology to the multisource MSMTT problem by introducing a connection-based symmetric nonnegative matrix factorization technique, which is shown to be computationally feasible and efficient in obtaining high-quality solutions. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the Army Research Laboratory [Grant G00006831]. 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.2023.0016 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0016 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

研究问题: 本文主要探讨如何构建一个稳健、高效且具备良好扩展性的计算框架来解决多传感器/多数据源环境下的目标跟踪问题，即如何实现不同数据来源下的信息融合和一致的目标跟踪。

研究方法: 作者基于数学建模和整数规划构建问题模型，提出了一系列启发式与优化算法（包括FHA、MSRA、RMSRA用于单源跟踪，以及CBSNMF和LMVC用于多源共识方案），并通过仿真实验和计算实验验证算法的效率、精度与鲁棒性。

主要发现: 研究表明，所提出的RMSRA算法在单源目标跟踪中能够大幅降低计算时间（效率提升达98%以上）且保持较高解的质量，同时CBSNMF算法在多源数据融合中有效整合不同数据源的跟踪结果，提供了高质量、鲁棒性强且适应大规模数据的共识解决方案。

管理启示: 该研究为管理者提供了一种在多传感器、多数据源环境中整合与利用分散信息的方法，启示在自动驾驶、安防监控、物流调度等领域，采用高效、稳健的信息融合算法可以降低运营成本、提高决策准确性和系统整体鲁棒性。

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