Omega-international Journal of Management Science最新文献_第5页

UNHRD’s humanitarian support in South Asia via Multistage Stochastic Programming 联合国开发计划署通过多阶段随机规划对南亚的人道主义支持

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-11-15 DOI: 10.1016/j.omega.2025.103464

Ruoyu Hu, Douglas Alem, Aakil Caunhye

One of the main tasks of the United Nations Humanitarian Response Depot (UNHRD) relies on allocating relief aid to save people who suffer from disasters. This task is particularly challenging in areas like South Asia, where relief aid efforts are confronted with complex transportation conditions, significant socioeconomic disparities, and the frequent occurrence of disasters, not to mention that financial resources are often scarce. In this paper, we develop a novel Multistage Stochastic Programming model to help UNHRD support critical decisions regarding site selection and relief aid allocation. Differently from the main literature, where these decisions are often made within a two-stage paradigm, our three-stage perspective takes into account in-kind donation campaigns that are triggered depending on the disaster impact and its effects, and is paramount to improving the effectiveness and fairness of the disaster relief operation. Our objective function maximizes the effectiveness of the disaster relief operation, defined as the extent to which it fulfills the needs of the population. Considering that different regions often exhibit distinct coping capacities, the effectiveness measure also factors in a vulnerability score to encourage relief aid allocation to the most in-need populations. The overall results show the importance of in-kind donation to achieve a more equitable relief aid allocation plan and the benefit of targeting more vulnerable regions under severely scarce resources.

联合国人道主义反应仓库的主要任务之一是分配救济援助，以拯救遭受灾害的人。这项任务在南亚等地区尤其具有挑战性，在这些地区，救援工作面临着复杂的运输条件、巨大的社会经济差距和频繁发生的灾害，更不用说财政资源往往稀缺。在本文中，我们开发了一个新的多阶段随机规划模型，以帮助联合国难民署支持有关选址和救济援助分配的关键决策。与主流文献的两阶段决策不同，我们的三阶段视角考虑了根据灾害影响和影响而引发的实物捐赠活动，这对提高救灾行动的有效性和公平性至关重要。我们的目标职能是最大限度地提高救灾行动的有效性，其定义是救灾行动在多大程度上满足了人民的需要。考虑到不同地区往往表现出不同的应对能力，有效性措施还考虑到脆弱性得分，以鼓励向最需要的人口分配救济援助。总体结果表明，实物捐赠对于实现更公平的救援援助分配计划的重要性，以及在资源严重稀缺的情况下，针对更脆弱的地区的益处。

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引用次数: 0

Multi-driver transportation scheduling for improving supply chain resilience 提高供应链弹性的多驾驶员运输调度

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-11-13 DOI: 10.1016/j.omega.2025.103461

Shaojun Lu , Yiyu Song , Min Kong , Chaoming Hu , Amir M. Fathollahi-Fard , Maxim A. Dulebenets

Optimizing transportation scheduling enhances the flexibility of resource allocation and transport operations, thereby reducing delays and costs while improving the resilience of supply chains. This study investigates a transportation scheduling problem aimed at minimizing the maximum completion time, incorporating key real-world considerations such as multiple drivers, loading and unloading times, round-trip transportation, distributed logistics centers, and the deterioration effect. Several structural properties of the problem are derived through a comprehensive preliminary analysis. Building on these properties, an exact algorithm for task sequencing is developed, and a Mixed-Integer Linear Programming model is formulated. A lower bound for the problem is also established. Given the NP-hard nature of the problem, we propose an enhanced Variable Neighborhood Search (VNS) algorithm, which integrates the exact algorithm with four neighborhood structures to accelerate convergence and improve solution quality. Experimental results indicate that the proposed intelligent algorithm significantly outperforms five state-of-the-art metaheuristics in both convergence speed and solution quality. This study further integrates deep learning to predict the best runtime of the proposed intelligent algorithm across problems of varying scales, which can reduce computational time in practical optimization scenarios. Sensitivity analysis highlights the critical influence of normal transportation times and the deterioration coefficient on scheduling performance, offering valuable theoretical insights for supply chain management. The findings of this research contribute to optimizing transportation task scheduling, enhancing supply chain resilience, and promoting sustainable development goals in supply chain management.

优化运输调度可以提高资源配置和运输作业的灵活性，从而减少延误和成本，同时提高供应链的弹性。本文研究了一个以最小化最大完工时间为目标的运输调度问题，并结合了多个驾驶员、装卸时间、往返运输、分布式物流中心和劣化效应等关键现实考虑因素。通过全面的初步分析，导出了问题的几个结构性质。在此基础上，提出了一种精确的任务排序算法，并建立了混合整数线性规划模型。并给出了问题的下界。针对该问题的NP-hard特性，本文提出了一种增强的可变邻域搜索（VNS）算法，该算法将精确算法与四种邻域结构相结合，以加速收敛并提高解的质量。实验结果表明，该算法在收敛速度和解质量上都明显优于五种最先进的元启发式算法。本研究进一步整合深度学习来预测所提出的智能算法在不同规模问题中的最佳运行时间，从而减少实际优化场景中的计算时间。敏感性分析强调了正常运输时间和恶化系数对调度绩效的重要影响，为供应链管理提供了有价值的理论见解。研究结果有助于优化运输任务调度，增强供应链弹性，促进供应链管理的可持续发展目标。

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引用次数: 0

A Deep & Cross Network-based framework for online food delivery time prediction with driver-specific information 基于深度和交叉网络的基于司机特定信息的在线食品配送时间预测框架

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-11-12 DOI: 10.1016/j.omega.2025.103457

Genshen Fu , Yujie Chi , Li Zheng , Zuo-Jun Max Shen

In online food delivery operations, accurate delivery time prediction is crucial, as it underpins effective resource allocation and ensures customer satisfaction. While prediction models can be trained using historical delivery data, there is significant room for improving accuracy, especially in incorporating driver-specific information. However, this task faces several challenges: (1) limited data availability and privacy concerns, (2) driver heterogeneity and data dispersion, and (3) tacit driver knowledge and feature engineering complexity. To tackle these issues, we introduce a Deep & Cross Network-based (DCN-based) framework. This framework utilizes limited driver-specific information and dispersed data to automate feature engineering and enhance prediction accuracy, enabling a more personalized and precise prediction process. It leverages both low-order feature interactions, captured by the Cross Network, and high-order interactions from the Deep Neural Network (DNN), effectively balancing interpretability and predictive power. Extensive experiments using real-world data from Zomato demonstrate that our approach with driver-specific information significantly outperforms both traditional and state-of-the-art models, achieving superior results across all regression accuracy metrics. The best performance yields a root mean square error (RMSE) of 3.6660, representing a 35.93% improvement over models without driver-specific information. Furthermore, the framework’s automatic feature engineering provides deeper insights into the interactions between driver information and external factors, offering a valuable tool for improving online food delivery operations.

在网上外卖业务中，准确的配送时间预测是至关重要的，因为它是有效分配资源和确保客户满意度的基础。虽然预测模型可以使用历史交付数据进行训练，但准确性仍有很大的提高空间，特别是在纳入驾驶员特定信息方面。然而，这项任务面临着几个挑战：(1)有限的数据可用性和隐私问题；(2)驱动的异质性和数据分散；(3)隐性驱动知识和特征工程的复杂性。为了解决这些问题，我们引入了一个基于深度跨网络（Deep & Cross Network-based, DCN-based）的框架。该框架利用有限的驾驶员特定信息和分散的数据来自动化特征工程并提高预测精度，从而实现更加个性化和精确的预测过程。它利用了交叉网络捕获的低阶特征交互和深度神经网络（DNN）的高阶交互，有效地平衡了可解释性和预测能力。使用来自Zomato的真实世界数据进行的大量实验表明，我们针对驾驶员特定信息的方法显著优于传统和最先进的模型，在所有回归精度指标上都取得了卓越的结果。最佳性能的均方根误差（RMSE）为3.6660，与没有特定驱动程序信息的模型相比，提高了35.93%。此外，该框架的自动特征工程可以更深入地了解驾驶员信息与外部因素之间的相互作用，为改进在线送餐业务提供了有价值的工具。

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引用次数: 0

Managing inventory and financing decisions under ambiguity 在不明确的情况下管理库存和融资决策

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-11-12 DOI: 10.1016/j.omega.2025.103460

Cheng Qian , Zhaolin Li , Qi Fu

This study proposes a robust optimization framework to address the persistent challenges faced by micro and small enterprises (MSEs) in raising capital due to high levels of demand ambiguity. We examine a robust newsvendor model in which the firm has insufficient initial capital and needs to raise capital from an external fund provider. Without knowing the precise demand distribution, both the firm and the fund provider adopt a max–min decision rule based on the mean and variance of the demand. The firm aims to maximize his expected worst-case profit by determining the production quantity, while the fund provider offers equity or loan financing, seeking a fair market-determined return on the contributed capital. We derive the robust production quantity and financing agreements under both equity and loan financing. We show that equity financing attains the system-optimal outcome under distributional ambiguity, and propose a simple formula for the robust interest rate under loan financing. We further generalize our analysis to consider collateral and initial capital, and extend the base model to a robust principal–agent setting where the firm can exert an unobservable effort to influence demand. In the latter case, we show that equity financing outperforms loan financing across a wide range of parameter values, contrary to the existing literature without demand ambiguity. Our analysis offers guidance for practitioners and policymakers seeking effective strategies to promote growth while safeguarding fund providers in the MSE sector.

本研究提出了一个稳健的优化框架，以解决微型和小型企业（mse）在筹集资金方面面临的持续挑战，这是由于高度的需求模糊性。我们研究了一个强大的报摊模型，其中公司没有足够的初始资本，需要从外部资金提供者筹集资金。在不知道确切需求分布的情况下，企业和资金提供者都采用基于需求均值和方差的最大最小决策规则。企业的目标是通过确定生产数量来最大化其预期最坏情况下的利润，而资金提供者提供股权或贷款融资，寻求市场决定的公平的出资回报。推导出了股权融资和贷款融资下的稳健生产数量和融资协议。我们证明了在分配不明确的情况下，股权融资达到了系统最优的结果，并提出了贷款融资下稳健利率的简单公式。我们进一步将我们的分析推广到考虑抵押品和初始资本，并将基本模型扩展到稳健的委托代理设置，在该设置中，企业可以施加不可观察的努力来影响需求。在后一种情况下，我们表明股权融资在广泛的参数值范围内优于贷款融资，与没有需求歧义的现有文献相反。我们的分析为从业者和政策制定者提供了指导，以寻求有效的策略来促进增长，同时保护MSE部门的资金提供者。

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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 : 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

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 : 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

Target Setting for the Digital Economy in China: A DEA Bargaining Approach with General Production Network Structure 中国数字经济目标设定：基于一般生产网络结构的DEA议价方法

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-11-08 DOI: 10.1016/j.omega.2025.103459

Ming-Miin Yu , Sebastián Lozano , Kok Fong See

Over the past two decades, China's digital economy (DE) has grown significantly, and this is mainly due to investments in digital infrastructure, technological advancements, and government support. Despite this growth, regional disparities and sustainability challenges persist. This study aims to develop a more realistic and equitable target-setting framework for improving DE performance across provinces in China. We propose a generalized Network Data Envelopment Analysis (NDEA) model with a Nash bargaining mechanism, which allows cooperative optimization of inputs, intermediate products, and outputs among stages and subprocesses within a network production structure. The methodological innovation lies in its ability to capture interdependencies among subprocesses and distinguish between desirable, undesirable, and neutral intermediate products, thereby integrating green development considerations. Our empirical results show substantial improvement potential across provinces, with differentiated targets in areas such as energy consumption, software income, and e-commerce sales. The proposed model not only advances methodological development in the NDEA but also provides policymakers with a practical tool for promoting balanced regional development and sustainable digital transformation in China.

在过去的二十年里，中国的数字经济（DE）显著增长，这主要是由于对数字基础设施的投资、技术进步和政府的支持。尽管有这种增长，但区域差异和可持续性挑战依然存在。本研究旨在建立一个更加现实和公平的目标设定框架，以提高中国各省的DE绩效。我们提出了一个具有纳什议价机制的广义网络数据包络分析（NDEA）模型，该模型允许在网络生产结构中的阶段和子过程之间协作优化投入、中间产品和产出。方法上的创新在于它能够捕捉子过程之间的相互依赖关系，并区分理想的、不理想的和中性的中间产品，从而整合绿色发展的考虑。我们的实证结果显示，各省之间存在巨大的改善潜力，在能源消耗、软件收入和电子商务销售等领域的目标存在差异。该模型不仅推动了NDEA方法的发展，而且为政策制定者提供了促进中国区域平衡发展和可持续数字化转型的实用工具。

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引用次数: 0

Discrete location models with customers’ choice and path improvements 具有客户选择和路径改进的离散位置模型

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-11-04 DOI: 10.1016/j.omega.2025.103458

Inmaculada Espejo , Alfredo Marín

We examine several facility location problems within a directed network involving two distinct cost types. The first, referred to as the “customer cost”, represents the expense each customer considers when selecting a facility to obtain service (e.g., delivery time or a measure of quality degradation). Consequently, once facilities are established, each customer chooses the one that minimizes their individual cost. The second type, termed the “company cost”, encompasses all expenses incurred by the company due to customer allocation to their chosen facilities. Additionally, the company possesses a budget that can be allocated to reduce company costs associated with the network’s arcs (the so-called arcs upgrading).

The company’s objective is to simultaneously determine facility locations and distribute the budget (or a portion of it) across the network arcs to minimize the total company cost. This total company cost comprises the post-upgrading company costs and the invested budget, while accounting for customer reactions after facility placement.

Different problem variants emerge based on the facility location criteria and customers’ choice strategy. In this paper, we address the following problems: the

p

-median problem, a two-stage facility location problem, a single-allocation hub location problem, and a tree-of-hubs location problem — all incorporating customers’ choice and arc upgrading.

我们研究了有向网络中涉及两种不同成本类型的几个设施选址问题。第一个被称为“客户成本”，表示每个客户在选择设施以获得服务时所考虑的费用（例如，交货时间或质量退化的度量）。因此，一旦建立了设施，每个客户都会选择将其个人成本降至最低的设施。第二种类型，称为“公司成本”，包括公司因客户分配其选择的设施而产生的所有费用。此外，公司拥有可分配的预算，以减少与网络弧线（所谓的弧线升级）相关的公司成本。公司的目标是同时确定设施位置，并在整个网络范围内分配预算（或部分预算），以最大限度地降低公司的总成本。该公司总成本包括升级后的公司成本和投入的预算，同时考虑到设备放置后的客户反应。根据设施选址标准和客户选择策略的不同，会出现不同的问题变体。在本文中，我们解决了以下问题：p中值问题，两阶段设施选址问题，单分配枢纽选址问题，以及枢纽树选址问题-所有这些问题都包含了客户的选择和弧升级。

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引用次数: 0

Distributionally robust joint inventory and pricing control across products 分布健壮的联合库存和跨产品的价格控制

IF 7.2 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-11-01 DOI: 10.1016/j.omega.2025.103443

Jianjun Xu , Xinyu Bi , Shaoxiang Chen , Feng Liu

We study a single-period, two-item joint inventory and pricing control problem under demand uncertainty, where only the means and variances of product demands are known. The model incorporates cross-price effects, meaning that the demand for each product is influenced by the prices of both products. To address this, we develop a distributionally robust optimization framework that determines ordering and pricing decisions to maximize the worst-case expected profit over all demand distributions consistent with the known moments. Under certain conditions, we derive closed-form optimal solutions. Theoretical analysis reveals that the economic relationships between products may differ from the perspectives of retailers and customers. We also extend the model to capture additional practical considerations such as customer behavior and supply unreliability. To evaluate the proposed approach, we conduct numerical experiments that demonstrate the closeness of our closed-form solutions to the optimal policies under the true distribution in cases with mild variances, as well as to the results derived from deterministic demand and the Sample Average Approximation method. Furthermore, we analyze the impact of distributional misspecification and find that our DRO-based solutions are more robust. Sensitivity analysis further shows that while variance has a moderate impact on expected profit, increasing variance leads to a greater discrepancy between the worst-case and true expected profit.

研究了需求不确定条件下的单周期、两项目联合库存和价格控制问题，其中只有产品需求的均值和方差是已知的。该模型结合了交叉价格效应，这意味着每种产品的需求都受到两种产品价格的影响。为了解决这个问题，我们开发了一个分布健壮的优化框架，该框架确定订购和定价决策，以最大化与已知时刻一致的所有需求分布的最坏情况预期利润。在一定条件下，我们得到了闭型最优解。理论分析表明，从零售商和消费者的角度来看，产品之间的经济关系可能是不同的。我们还扩展了该模型，以捕获额外的实际考虑因素，如客户行为和供应不可靠性。为了评估所提出的方法，我们进行了数值实验，证明了我们的封闭形式解与真实分布下的最优策略在轻度方差情况下的接近性，以及与确定性需求和样本平均近似方法得出的结果的接近性。此外，我们分析了分布错误规范的影响，发现我们基于ro的解决方案更健壮。灵敏度分析进一步表明，方差对期望利润的影响不大，但方差越大，最坏情况与真实期望利润的差异越大。

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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 : 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