Omega-international Journal of Management Science最新文献_第6页

Supply chain digital twin design and implementation at scale: A case study at the Ford Motor Company and generalizations 供应链数字孪生模型的大规模设计和实施：福特汽车公司的案例研究及总结

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-02-01 Epub Date: 2025-10-20 DOI: 10.1016/j.omega.2025.103447

Dmitry Ivanov , Oleg Gusikhin

In this study, we describe Ford’s practices and propose three industry-based frameworks for supply chain digital twin (SCDT) design and implementation at scale. First, a generalized three-layer framework for the design of SCDTs based on Ford's approach is developed. The layers are intracompany, Tier-1 network, and deep-tier network, classified based on data visibility. We describe how digital twins can enhance operational performance and be utilized for resilience stress testing. Second, generalized frameworks of SCDT implementation are shown composed of two dimensions, i.e., implementation scale and implementation scope. The three-stage implementation scale framework proposes a roadmap for transition from data-driven organizations to digital twin-driven management systems. The four-level implementation scope framework encompasses product, process, organization, and extended network levels, with a focus on the key role of the data analytics department in deploying SCDTs. We then generalize four fundamental principles for SCDTs: (i): object-driven and data-driven design and adaptation, (ii) visibility as the central angle of digital twin design and technology, (iii) digital twins are integrators of data and knowledge, and (iv) SCDT continuous adaptation. To the best of our knowledge, our paper is the first in the literature to report on the design and deployment of an SCDT at scale, which can be useful for academics and practitioners alike. We conclude that a properly developed SCDT can enable strategic and operational performance improvements, end-to-end visibility, agentic AI integration in decision-making, and supply chain stress testing, as well as create a new approach to managing the supply chain.

在本研究中，我们描述了福特的实践，并提出了三个基于行业的供应链数字孪生（SCDT）设计和大规模实施框架。首先，基于Ford的方法，开发了scdt设计的通用三层框架。这些层分为公司内部网络、第一层网络和深层网络，根据数据可见性进行分类。我们描述了数字孪生如何提高操作性能并用于弹性压力测试。其次，给出了SCDT实施的广义框架，该框架由实施规模和实施范围两个维度组成。三阶段实施规模框架提出了从数据驱动型组织向数字双驱动型管理系统过渡的路线图。四个级别的实现范围框架包括产品、过程、组织和扩展的网络级别，重点关注数据分析部门在部署scdt中的关键角色。然后，我们概括了SCDT的四个基本原则：(i)：对象驱动和数据驱动的设计和适应，（ii）可见性作为数字孪生设计和技术的中心角度，（iii）数字孪生是数据和知识的集成商，以及（iv） SCDT的持续适应。据我们所知，我们的论文是文献中第一篇报告大规模设计和部署SCDT的论文，这对学术界和实践者都很有用。我们得出结论，适当开发的SCDT可以实现战略和运营绩效改进，端到端可见性，决策中的代理人工智能集成和供应链压力测试，以及创建管理供应链的新方法。

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引用次数: 0

Fleet size planning in crowdsourced delivery: Balancing service level and driver utilization 众包配送中的车队规模规划：平衡服务水平和司机利用率

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-02-01 Epub Date: 2025-10-18 DOI: 10.1016/j.omega.2025.103445

Aliaa Alnaggar, Sahil Bhatt

This paper addresses the fleet size planning problem for crowdsourced delivery platforms, focusing on optimizing the number of crowdsourced drivers to balance the platform’s service level and driver utilization. This is motivated by recent regulatory measures that limit the number of vehicle licenses a platform can hold, which caps fleet size as a means to enhance driver working conditions and mitigate congestion and emissions caused by an excessive number of idle vehicles on the road. We propose a two-stage optimization model where the first stage involves tactical decisions for determining fleet sizes, while the second stage captures the operational dynamics of the platform by a Markov decision process (MDP) in which uncertain crowd driver arrival in the MDP depends on fleet size decisions in the first stage, introducing decision-dependent uncertainty. To efficiently solve the model, we employ a value function approximation (VFA) algorithm that iteratively determines fleet sizes using Boltzmann exploration then approximates the second-stage MDP using a rolling-horizon parametric cost function approximation. Extensive computational experiments confirm the effectiveness of the VFA algorithm, demonstrating its ability to meet both service level and driver utilization targets under various conditions. The results show that a platform can achieve high driver utilization and meet its service level target while only marginally reducing its profit, especially when driver behavior is more predictable and fleet sizes are temporally adjusted.

本文研究众包配送平台的车队规模规划问题，重点优化众包司机数量，平衡平台的服务水平和司机利用率。最近的监管措施限制了一个平台可以持有的车辆牌照数量，这限制了车队规模，以此来改善司机的工作条件，缓解道路上闲置车辆过多造成的拥堵和排放。我们提出了一个两阶段优化模型，其中第一阶段涉及确定车队规模的战术决策，而第二阶段通过马尔可夫决策过程（MDP）捕获平台的运行动态，其中不确定的人群驾驶员到达MDP取决于第一阶段的车队规模决策，引入决策依赖的不确定性。为了有效地求解该模型，我们采用了一种值函数近似（VFA）算法，该算法使用玻尔兹曼探索迭代确定船队规模，然后使用滚动水平参数成本函数近似近似第二阶段MDP。大量的计算实验证实了VFA算法的有效性，证明了该算法能够满足各种条件下的服务水平和驾驶员利用率目标。结果表明，在司机行为可预测性较强、车队规模可临时调整的情况下，平台在实现高司机利用率和服务水平目标的同时，仅能略微降低利润。

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引用次数: 0

A truck–drone delivery problem with location and routing decisions under uncertainty 在不确定的情况下，具有位置和路线决策的卡车-无人机运输问题

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-02-01 Epub Date: 2025-10-17 DOI: 10.1016/j.omega.2025.103446

Okan Dukkanci

This study presents a new stochastic delivery problem with location and routing decisions, in which trucks and drones operate together. While trucks are routed between parking lots, drones are launched from parking lots to make deliveries to stores. The problem decides the location of parking lots, the route of trucks, and the assignment of stores to parking lots and drones. The objective function minimizes the total transportation cost of trucks and drones subject to the payload capacity of the trucks and drones, the flying range of the drones, and the time windows for parking lots. The uncertainty of demand and drone travel time is considered to adapt to the dynamics of the real world, such as the volatile customers’ demand and unpredictable weather conditions. We first develop a two-stage stochastic programming formulation and its deterministic equivalent problem reformulation. We propose the scenario decomposition algorithm as an exact solution approach to solve the proposed stochastic problem. We also develop improvement strategies to enhance the performance of the scenario decomposition algorithm. The computational experiments are conducted on two different data sets to evaluate the performance of the scenario decomposition algorithm, analyze the value of stochasticity and the expected value of perfect information, and examine the impact of some key parameters of the problem such as the number of drones per truck, unit transportation costs of drones and trucks, time windows, and payload capacity of trucks and drones.

本研究提出了一种新的随机配送问题，其中卡车和无人机一起操作，具有位置和路线决策。卡车在停车场之间穿梭，而无人机则从停车场发射，向商店送货。该问题决定了停车场的位置，卡车的路线，以及将商店分配给停车场和无人机。目标函数在卡车和无人机的有效载荷能力、无人机的飞行距离和停车场的时间窗口的约束下，使卡车和无人机的总运输成本最小化。考虑到需求的不确定性和无人机飞行时间的不确定性，以适应现实世界的动态，例如不稳定的客户需求和不可预测的天气条件。首先提出了一个两阶段随机规划公式及其确定性等价问题的重新表述。我们提出场景分解算法作为一种精确解方法来解决所提出的随机问题。我们还开发了改进策略来提高场景分解算法的性能。在两个不同的数据集上进行了计算实验，评估场景分解算法的性能，分析了随机值和完美信息期望值，并考察了问题的关键参数如每辆卡车的无人机数量、无人机和卡车的单位运输成本、时间窗、卡车和无人机的有效载荷能力等对问题的影响。

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引用次数: 0

Information disclosure and pricing decisions in competitive waste treatment systems: An agent-based approach 竞争性废物处理系统中的信息披露和定价决策：基于代理的方法

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-02-01 Epub Date: 2025-11-11 DOI: 10.1016/j.omega.2025.103463

Junfei Hu , Zhe Tian , Liang Cui , Peng Zhou

Private operators are increasingly involved in municipal solid waste management worldwide, resulting in competitive waste treatment systems. In such competitive systems, the gate fee as a crucial revenue stream for private operators needs to be set appropriately to capture a larger share of waste stream and maximize profit. Assessing the impact of information disclosure on gate fee pricing decisions provides valuable insights for policy analysis and decision-making. This study proposes an agent-based competitive waste treatment model to analyze gate fee pricing decisions under disclosed information. The proposed model outperforms traditional methods such as game theory by considering both cooperation and competition relationships among multiple agents. The experience-weighted attraction algorithm is utilized to solve the proposed model, enabling collaborative learning behavior to be considered in the decision-making process, thereby making it suitable for a disclosed environment. We apply the proposed approach to examine the Shenzhen waste treatment market in China. It has been found that without information disclosure, operators may misjudge allocation rules, causing landfills to withdraw from competition and significantly raise gate fees in retaliation. Besides, disclosing market information contributes to optimizing gate fee decisions, reducing government expenditure, and improving waste allocation. Disclosing allocation rules emerges as the most effective policy for Shenzhen waste treatment market. These findings are expected to provide government agencies with comprehensive insights into gate fee pricing decisions under conditions of information disclosure.

私营经营者越来越多地参与世界各地的城市固体废物管理，从而形成了具有竞争性的废物处理系统。在这种竞争体制中，门票费作为私营经营者的重要收入来源，需要适当设置，以获取更大的废物流份额，实现利润最大化。评估信息披露对门票定价决策的影响为政策分析和决策提供了有价值的见解。本文提出了一个基于主体的竞争性废物处理模型来分析信息披露条件下的闸费定价决策。该模型考虑了多个智能体之间的合作和竞争关系，优于博弈论等传统方法。利用经验加权吸引算法求解所提出的模型，使决策过程中能够考虑协同学习行为，从而使其适用于公开环境。我们运用该方法考察了中国深圳的垃圾处理市场。研究发现，在信息不公开的情况下，经营者可能会误判分配规则，导致垃圾填埋场退出竞争，并大幅提高门票费作为报复。此外，披露市场信息有助于优化门票收费决策，减少政府支出，改善垃圾分配。披露分配规则成为深圳垃圾处理市场最有效的政策。这些研究结果有望为政府机构在信息公开条件下的门票定价决策提供全面的见解。

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引用次数: 0

A novel information-driven approach for selecting a project portfolio: A benefit-to-cost based method combining decomposition and holistic elicitation paradigms 选择项目组合的一种新的信息驱动方法：一种结合分解和整体启发范例的基于收益-成本的方法

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-02-01 Epub Date: 2025-11-01 DOI: 10.1016/j.omega.2025.103456

Maria Elvira Borges Tunú Pessoa , Eduarda Asfora Frej , Adiel Teixeira de Almeida

Portfolio selection is a challenging task that involves multiple conflicting objectives that must be considered. In this context, this paper proposes a structured approach to addressing portfolio selection problems, based on the Benefit-to-Cost Ratio (BCR) heuristic for ranking projects, with a focus on the information provided by the decision maker (DM). First, we present an approach that combines two types of preference information derived from elicitation paradigms in decision-making: decomposition-based ones, which consider information provided about the consequences of projects, and holistic-based ones, which consider information about actual candidate projects and subsets of projects. Then, we present a mathematical programming model that combines both types of information and computes dominance relationships between projects, considering their estimated BCR. To operationalize the proposed approach, an interactive Decision Support System (DSS) was developed, offering a user-friendly interface with graphical visualization tools to help the DM when providing preference information. Finally, a portfolio selection problem for technology projects of a Brazilian company in the retail sector is presented to demonstrate the practical applicability of the proposed approach, thereby illustrating the methodology's potential to enhance the practice of portfolio selection in real-world scenarios.

投资组合选择是一项具有挑战性的任务，涉及必须考虑的多个相互冲突的目标。在此背景下，本文提出了一种结构化的方法来解决投资组合选择问题，该方法基于对项目进行排序的收益成本比启发式方法，重点关注决策者（DM）提供的信息。首先，我们提出了一种方法，该方法结合了两种类型的偏好信息，这些信息来自于决策中的启发范式：基于分解的偏好信息，它考虑了关于项目后果的信息，以及基于整体的偏好信息，它考虑了关于实际候选项目和项目子集的信息。然后，我们提出了一个数学规划模型，该模型结合了这两种类型的信息并计算了项目之间的优势关系，考虑了它们的估计BCR。为了实现所提出的方法，开发了一个交互式决策支持系统（DSS），提供一个用户友好的界面和图形可视化工具，以帮助决策制定者提供偏好信息。最后，提出了巴西零售行业公司技术项目的投资组合选择问题，以证明所提出方法的实际适用性，从而说明该方法在现实世界场景中增强投资组合选择实践的潜力。

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引用次数: 0

Two-stage distributionally robust optimization approach for drone-supported facility location and post-disaster relief distribution 无人机支持下设施选址与灾后救援分配的两阶段分布鲁棒优化方法

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-02-01 Epub Date: 2025-11-11 DOI: 10.1016/j.omega.2025.103462

Pan Gao , Min Li , Zhongming Wu , Zhenzhen Zhang

This paper explores the drone-supported application in a two-stage capacitated facility location problem, focusing on the strategic planning and operational phases of humanitarian relief. The first stage involves selecting facility locations and allocating drones, while the second stage involves transporting relief supplies post-disaster. We address the uncertainty inherent in post-disaster demand by employing a two-stage distributionally robust optimization (DRO) framework. To characterize various distributions of uncertainty, two types of ambiguity sets are introduced to characterize the unknown demand distribution: the Wasserstein and the event-wise mean absolute deviation ambiguity set. Furthermore, the DRO problem under the Wasserstein ambiguity set is decomposed and then solved using a column-and-constraint generation algorithm, due to the computational intractability of enumerating dual vertices in practical settings. In contrast, for the DRO problem tied to the mean absolute deviation ambiguity set, an event-wise affine decision rule is utilized to handle the recourse problem. This leads to reforming the problem into mixed-integer linear programming models, enabling its solution using standard optimization solvers. Numerical results demonstrate the effectiveness of both approaches, with the DRO models delivering more reliable solutions in out-of-sample tests compared to other state-of-the-art models. Specifically, our DRO models significantly reduce unmet post-disaster demand and ensure smaller total cost fluctuations on both in-sample and out-of-sample tests.

本文以人道主义救援的战略规划和操作阶段为重点，探讨了无人机支持在两阶段有能力设施选址问题中的应用。第一阶段包括选择设施地点和分配无人机，第二阶段包括灾后救援物资的运输。我们通过采用两阶段分布鲁棒优化（DRO）框架来解决灾后需求中固有的不确定性。为了描述各种不确定性分布，引入了两种类型的模糊集来描述未知需求分布：Wasserstein模糊集和事件平均绝对偏差模糊集。此外，由于在实际设置中枚举双顶点的计算困难，对Wasserstein模糊集下的DRO问题进行了分解，然后使用列约束生成算法求解。相反，对于与平均绝对偏差模糊集相关的DRO问题，使用事件仿射决策规则来处理追索权问题。这导致将问题转化为混合整数线性规划模型，使其能够使用标准优化求解器进行解决。数值结果证明了这两种方法的有效性，与其他最先进的模型相比，DRO模型在样本外测试中提供了更可靠的解决方案。具体来说，我们的DRO模型显著减少了灾后未满足的需求，并确保样本内和样本外测试的总成本波动较小。

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引用次数: 0

Enhanced indexing using cumulative prospect theory utility function with expectile risk 利用具有预期风险的累积前景理论效用函数增强指数

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-02-01 Epub Date: 2025-10-16 DOI: 10.1016/j.omega.2025.103444

Divyanee Garg , Ahmad Zaman Khan , Aparna Mehra

In this article, we explore portfolio selection strategies for enhanced indexation, focusing on integrating behavioral insights from cumulative prospect theory (CPT) to address investor preferences and risk appetite while aiming to achieve returns that outperform the benchmark index. We incorporate loss aversion and probability distortion biases through the CPT utility function and expectile risk measure to mitigate portfolio risk. We propose dynamically adjusting reference points within the CPT utility function as the sum of the index return and a specified excess return to exceed the index’s performance. The excess return is calculated as the average of the positive alphas of the stocks in the benchmark index, derived from the capital asset pricing model. The proposed model is nonlinear, nonconvex, and nondifferentiable.

From an algorithmic standpoint, we design and implement the Real-Coded Genetic Algorithm to solve the newly proposed nondifferentiable nonconvex model. The efficacy of the model is tested using global datasets from Dow Jones, DAX, FTSE 100, and S&P 100. Our empirical evidence shows that by taking a lower risk than the index (measured by the portfolio’s beta), our proposed CPT-based EI model outperforms the ordinary least square regression-based enhanced indexing model, the quantile regression-based enhanced indexing model, the naive strategy, and the benchmark index across nearly all performance metrics. Moreover, increasing the loss aversion parameter in the CPT value function improves the out-of-sample performance metrics up to a certain threshold. To further validate the consistency of our model, we tested it under different market phases of the FTSE 100 index and found favorable results.

在本文中，我们探讨了增强指数化的投资组合选择策略，重点是整合来自累积前景理论（CPT）的行为见解，以解决投资者偏好和风险偏好，同时旨在实现优于基准指数的回报。我们通过CPT效用函数和预期风险度量将损失厌恶和概率扭曲偏差结合起来，以降低投资组合风险。我们建议动态调整CPT效用函数内的参考点，作为指数收益和指定超额收益的总和，以超过指数的表现。超额收益是根据资本资产定价模型计算基准指数中股票的正阿尔法的平均值。所提出的模型是非线性的、非凸的、不可微的。从算法的角度出发，我们设计并实现了实数编码遗传算法来求解新提出的不可微非凸模型。使用道琼斯指数、DAX指数、富时100指数和标准普尔100指数的全球数据集对模型的有效性进行了测试。我们的经验证据表明，通过承担比指数更低的风险（由投资组合的beta衡量），我们提出的基于cpt的EI模型在几乎所有性能指标上都优于普通的基于最小二乘回归的增强索引模型、基于分位数回归的增强索引模型、朴素策略和基准指数。此外，增加CPT值函数中的损失厌恶参数可以将样本外性能指标提高到一定阈值。为了进一步验证我们的模型的一致性，我们在FTSE 100指数的不同市场阶段对其进行了测试，并发现了良好的结果。

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引用次数: 0

Robust de novo programming under different uncertainty sets and its application to the renewable energy sector 不同不确定集下的鲁棒从头规划及其在可再生能源领域的应用

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-01-01 Epub Date: 2025-07-16 DOI: 10.1016/j.omega.2025.103389

Noureddine Kouaissah

This paper proposes robust models of de novo programming (R-DNP) using cardinality-constrained robustness with interval-based, ellipsoidal, and norm-based uncertainty sets. R-DNP has not been researched or explored, and we aim to fill this gap in the literature. In particular, we develop the robust counterpart of the weighted DNP (W-DNP), Chebyshev DNP (C-DNP), and extended DNP (E-DNP) models to incorporate different uncertainty sets and decision-makers’ preferences. Methodologically, the proposed approach extends the conventional DNP model to solve uncertain coefficients for each decision variable on the left-hand side of each objective function and on the total budget, overcoming the limitations of the current multicriteria solution procedure of the DNP approach. The proposed methods provide decision-makers with more flexibility to express their level of conservatism and preferences by setting priority weights and aspiration levels. The proposed method’s usefulness over the standard DNP is demonstrated by providing an illustrative example. Moreover, we validate the proposed formulations for solving real-world problems through a hypothetical application: optimizing onshore wind farm locations in Morocco. The work’s results confirm the validity of the proposed methodologies, showing that they can assist decision-makers in determining the optimal system design for sustainable electricity generation under uncertain conditions.

本文提出了基于区间、椭球和范数的不确定性集的基于基数约束的鲁棒性的从头规划（R-DNP）鲁棒模型。R-DNP尚未被研究或探索，我们的目标是填补这一空白的文献。特别是，我们开发了加权DNP (W-DNP), Chebyshev DNP （C-DNP）和扩展DNP （E-DNP）模型的鲁棒对应模型，以纳入不同的不确定性集和决策者的偏好。在方法上，该方法扩展了传统的DNP模型，在每个目标函数的左侧和总预算上求解每个决策变量的不确定系数，克服了当前DNP方法多准则求解过程的局限性。所提出的方法通过设置优先级权重和期望水平，为决策者提供了更大的灵活性来表达他们的保守性和偏好水平。通过一个实例证明了该方法相对于标准DNP的有效性。此外，我们通过一个假设的应用验证了提出的解决现实问题的公式：优化摩洛哥的陆上风电场位置。工作结果证实了所提出方法的有效性，表明它们可以帮助决策者在不确定条件下确定可持续发电的最佳系统设计。

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引用次数: 0

Robust bi-objective mean-CVaR portfolio selection: Applications to energy sector 稳健的双目标均值- cvar投资组合选择：在能源行业的应用

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-01-01 Epub Date: 2025-08-23 DOI: 10.1016/j.omega.2025.103404

Asmerilda Hitaj , Elisa Mastrogiacomo , Elena Molho

A new approach to optimizing or hedging a portfolio of financial positions is presented and tested with applications to energy market. Motivated by uncertainty in the estimation of problem data we consider robust bi-objective optimization problems with mean and conditional value-at-risk objective functions where the underlying probability distribution of portfolio return is only known to belong to a certain set. To tackle the problem of uncertainty we consider two different approaches: in the first one, uncertainty is represented by an elliptic set centered at the sample estimators of mean and covariance matrix; in the second one, uncertainty takes into account experts beliefs. For both approaches, we derive analytical semi-closed-form solutions for the worst case mean-CVaR portfolio; in addition, we provide a characterization of the location of the robust Pareto frontier with respect to the corresponding original Pareto frontier.

提出了一种优化或对冲金融头寸组合的新方法，并通过能源市场的应用进行了测试。考虑到问题数据估计中的不确定性，我们考虑具有均值和条件风险值目标函数的鲁棒双目标优化问题，其中投资组合收益的潜在概率分布只属于某一集合。为了解决不确定性问题，我们考虑了两种不同的方法：第一种方法是用以均值和协方差矩阵的样本估计量为中心的椭圆集来表示不确定性；在第二种情况下，不确定性考虑了专家的信念。对于这两种方法，我们导出了最坏情况下均值- cvar投资组合的解析半封闭形式解；此外，我们提供了相对于相应的原始帕累托边界的鲁棒帕累托边界位置的表征。

引用次数: 0

Talent scheduling with daily working capacity and scene changeover times 根据每日工作容量和场景切换时间进行人才调度

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2026-01-01 Epub Date: 2025-10-08 DOI: 10.1016/j.omega.2025.103439

Zhaoqi Yang , Shunji Tanaka , Bertrand M.T. Lin

This paper investigates a talent scheduling problem that incorporates daily work capacity constraints and the changeover times between consecutive scenes filmed on the same day. The problem inherently integrates three key optimization challenges: talent scheduling, the traveling salesperson problem, and bin packing. To obtain an optimal solution, we develop two integer linear programming models and a dynamic programming algorithm. Additionally, we introduce a polynomial-time dynamic programming algorithm for the special case where the filming order of all scenes is predetermined. This polynomial-time algorithm is incorporated into a tabu search framework to generate high-quality approximate solutions. Finally, we conduct a computational study to assess the effectiveness and efficiency of all proposed solution approaches. The approach of simplifying the problem structures significantly outperforms other algorithms in solution quality and execution time for large-scale instances.

本文研究了一个包含日常工作能力约束和同一天拍摄的连续场景之间转换时间的人才调度问题。这个问题本质上集成了三个关键的优化挑战：人才调度、旅行销售人员问题和装箱问题。为了得到最优解，我们建立了两个整数线性规划模型和一个动态规划算法。此外，针对所有场景的拍摄顺序预先确定的特殊情况，我们引入了一种多项式时间动态规划算法。该多项式时间算法被整合到禁忌搜索框架中以生成高质量的近似解。最后，我们进行了计算研究，以评估所有提出的解决方法的有效性和效率。这种简化问题结构的方法在求解质量和执行时间上明显优于其他算法。

引用次数: 0