Operations Research Perspectives最新文献_第3页

Forecasting efficiency of two-stage Petrochemical sustainable supply chains using Deep Learning and DNDEA Model 基于深度学习和DNDEA模型的两阶段石化可持续供应链效率预测

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-09-16 DOI: 10.1016/j.orp.2025.100354

Sina Sayardoost Tabrizi , Saeed Yousefi , Keikhosro Yakideh

The efficiency of supply chains is essential for improving managerial decision-making and enhancing strategic planning capabilities. This research presents a novel integration of deep learning with a two-stage supply chain framework to assess the efficiency of 28 petrochemical units over a period of 90 months. Based on sustainability principles, a dynamic network data envelopment analysis (DEA) model is employed to measure and compare the relative efficiency of supply chains operating across different time horizons. To forecast future input–output relationships in the supply chain, an advanced two-layer Long Short-Term Memory (LSTM) model is proposed. This LSTM-based prediction system demonstrated exceptional accuracy, achieving a low Mean Squared Error (MSE) of 0.0004 and a Root Mean Square Error (RMSE) of 0.0208. Additionally, the trend of the loss function during training confirmed the reliability and stability of the proposed deep learning approach. The precise forecasting capability of the LSTM model enables managers to proactively identify and address inefficiencies in production facilities before they occur, rather than relying on reactive strategies. This proactive approach allows for better resource allocation and improved operational performance across petrochemical supply chains. By integrating deep learning with dynamic network DEA models, this study offers a robust framework for predictive efficiency analysis and performance evaluation in industrial applications. The suggested framework provides decision-makers with a pragmatic assessment instrument to identify efficient and underperforming supply chains and set realistic benchmarks for improvement. This methodology is designed to be scalable and adaptable, making it suitable for real-world evaluations of multi-stage supply chains and production systems. The research culminates in a two-phase case study, illustrating the practical applicability of the proposed framework.

供应链的效率对于改善管理决策和增强战略规划能力至关重要。本研究提出了一种新颖的深度学习与两阶段供应链框架的集成，以评估28个石化装置在90个月内的效率。基于可持续性原则，采用动态网络数据包络分析（DEA）模型，对不同时间跨度供应链的相对效率进行测度和比较。为了预测供应链中未来的投入产出关系，提出了一种先进的两层长短期记忆模型。该基于lstm的预测系统显示出优异的准确性，均方误差（MSE）为0.0004，均方根误差（RMSE）为0.0208。此外，训练过程中损失函数的变化趋势也证实了所提出的深度学习方法的可靠性和稳定性。LSTM模型的精确预测能力使管理人员能够主动识别和解决生产设施的低效率问题，而不是依赖于被动的策略。这种主动的方法可以更好地分配资源，提高整个石化供应链的运营绩效。通过将深度学习与动态网络DEA模型相结合，本研究为工业应用中的预测效率分析和绩效评估提供了一个强大的框架。建议的框架为决策者提供了一个实用的评估工具，以确定高效和表现不佳的供应链，并为改进设定现实的基准。该方法具有可扩展性和适应性，适用于多阶段供应链和生产系统的实际评估。研究在两个阶段的案例研究中达到高潮，说明了所提出框架的实际适用性。

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引用次数: 0

A hybrid optimization and data-driven approach to understand the role of the risk-aversion profile parameter in portfolio optimization problems with shorting constraints 一种混合优化和数据驱动的方法来理解风险规避剖面参数在有空头约束的投资组合优化问题中的作用

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-09-15 DOI: 10.1016/j.orp.2025.100353

Mariano Carbonero-Ruz , Francisco Fernández-Navarro , Antonio M. Durán-Rosal , Javier Pérez-Rodríguez

This study contributes to the optimization literature with an approach that would help investors understand how the risk-aversion profile hyperparameter affects excess returns, risk, and Sharpe ratio curves in portfolio optimization problems with short selling constraints. These curves were characterized by studying the original optimization problem and reducing it to a one-dimensional optimization problem. The problem variable was the excess return, and the minimum level of risk is expressed as a function of it. An approach to the functional form of the minimum risk level curve was also proposed, which allows us to determine an analytical expression for the aforementioned curves. The study provides significant results for the financial literature, such as (i) an upper and lower bound for the risk aversion profile hyperparameter; (ii) the optimal value for the risk aversion profile hyperparameter; (iii) a reduced version of the optimization problem that is easier to solve, and of course (iv) an analytical expression for the excess return, risk and Sharpe ratio curves as functions of the aforementioned hyperparameters. All of these results are reported using the Mean Squared Variance (MSV) portfolio optimization problem as the baseline model, representing the two objectives of the problem minimization function (excess return and risk) in the same unit.

本研究为优化文献提供了一种方法，帮助投资者理解在有卖空约束的投资组合优化问题中，风险规避剖面超参数如何影响超额收益、风险和夏普比率曲线。通过对原优化问题的研究，将其简化为一维优化问题，对这些曲线进行表征。问题变量是超额收益，最小风险水平表示为它的函数。提出了最小风险水平曲线的函数形式的一种方法，使我们能够确定上述曲线的解析表达式。该研究为金融文献提供了重要的结果，例如：(i)风险厌恶概况超参数的上下界；（ii）风险规避剖面超参数的最优值；（iii）更容易求解的简化版优化问题，当然（iv）作为上述超参数函数的超额收益、风险和夏普比率曲线的解析表达式。所有这些结果都是使用均方方差（MSV）投资组合优化问题作为基线模型报告的，代表了同一个单元中问题最小化函数的两个目标（超额收益和风险）。

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引用次数: 0

Constraint programming models for serial batch scheduling with minimum batch size 最小批量串行批调度的约束规划模型

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-09-02 DOI: 10.1016/j.orp.2025.100352

Jorge A. Huertas, Pascal Van Hentenryck

In serial batch (s-batch) scheduling, jobs are grouped in batches and processed sequentially within their batch. This paper considers multiple parallel machines, nonidentical job weights and release times, and sequence-dependent setup times between batches of different families. Although s-batch has been widely studied in the literature, very few papers have taken into account a minimum batch size, typical in practical settings such as semiconductor manufacturing and the metal industry. The problem with this minimum batch size requirement has been mostly tackled with dynamic programming and meta-heuristics, and no article has ever used constraint programming (CP) to do so. This paper fills this gap by proposing, three CP models for s-batching with minimum batch size: (i) an Interval Assignment model that computes and bounds the size of the batches using the presence literals of interval variables of the jobs. (ii) A Global model that exclusively uses global constraints that track the size of the batches over time. (iii) And a Hybrid model that combines the benefits of the extra global constraints with the efficiency of the sum-of-presences constraints to ensure the minimum batch sizes.The computational experiments on standard cases compare the three CP models with two existing mixed-integer programming (MIP) models from the literature. The results demonstrate the versatility of the proposed CP models to handle multiple variations of s-batching; and their ability to produce, in large instances, better solutions than the MIP models faster.

在串行批处理（s-batch）调度中，作业分批分组，并在其批处理中顺序处理。本文考虑了多台并行机器、不相同的作业权值和释放时间，以及不同家族批次之间的顺序相关的设置时间。虽然s-batch在文献中得到了广泛的研究，但很少有论文考虑到最小批量大小，这在半导体制造和金属工业等实际环境中是典型的。这个最小批大小要求的问题主要是通过动态规划和元启发式来解决的，没有一篇文章使用约束规划（CP）来解决这个问题。本文通过提出最小批大小的s批处理的三个CP模型来填补这一空白：(i)一个区间分配模型，该模型使用作业的区间变量的存在量来计算和限定批的大小。（ii）一个全局模型，专门使用跟踪批次大小的全局约束。（iii）混合模型，该模型结合了额外全局约束的好处和存在和约束的效率，以确保最小批量大小。在标准情况下的计算实验将这三种CP模型与文献中已有的两种混合整数规划（MIP）模型进行了比较。结果表明，所提出的CP模型在处理s批处理的多种变化时具有通用性；以及它们在大型情况下比MIP模型更快地产生更好解决方案的能力。

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引用次数: 0

An enhanced tabu search algorithm for resource-constrained project scheduling with a flexible project structure 基于柔性项目结构的资源约束项目调度改进禁忌搜索算法

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-07-23 DOI: 10.1016/j.orp.2025.100349

Chunlai Yu, Xiaoming Wang, Qingxin Chen

In this paper we consider the resource-constrained project scheduling problem with a flexible project structure and continuous activity durations. A mathematical model based on the resource-flow formulation is developed to tackle this problem. Due to the NP-hard nature of the problem, this mathematical model can only be used to find the optimal solution to small-scale problems. To address this issue, an enhanced tabu search algorithm is proposed, which utilizes an outer loop for activity selection and an inner loop for activity sequencing. The algorithm introduces several innovative features, including the integration of filtering, elite, and perturbation strategies, as well as new neighborhood operators. The parameters of the algorithm are calibrated using orthogonal experiments, and its efficacy is evaluated through extensive computational experiments conducted on multiple benchmark datasets. The results indicate that the proposed tabu search algorithm not only performs significantly better and more stable than existing metaheuristics, but also surpasses the performance of the traditional mathematical model based on rounded durations.

研究了具有柔性项目结构和连续工期的资源约束项目调度问题。为了解决这一问题，建立了一个基于资源流公式的数学模型。由于问题的NP-hard性质，该数学模型只能用于寻找小规模问题的最优解。为了解决这一问题，提出了一种增强的禁忌搜索算法，该算法利用外循环进行活动选择，内循环进行活动排序。该算法引入了几个创新的特征，包括过滤、精英和扰动策略的集成，以及新的邻域算子。通过正交实验对算法的参数进行校准，并通过在多个基准数据集上进行大量的计算实验来评估算法的有效性。结果表明，本文提出的禁忌搜索算法不仅比现有的元启发式算法性能更好、更稳定，而且优于基于舍入持续时间的传统数学模型的性能。

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引用次数: 0

The interplay between learning effect and order acceptance in production planning 生产计划中学习效应与订单接受的相互作用

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-07-15 DOI: 10.1016/j.orp.2025.100350

Kuo-Ching Ying , Pourya Pourhejazy , Wei-Jie Zhou

Learning takes time and hence its effects should be considered in short-term production planning (i.e., scheduling). This is especially true when human involvement is high and the shop floor experiences changes in workflow, workforce, or technology. The Single-Machine Scheduling Problem (SMSP) with the learning effect is considered to explore this interplay. The study first proves that the shortest processing time scheduling rule can solve the mathematical problems. Pseudo-polynomial solution algorithms based on Dynamic Programming (DP) are developed to solve the SMSPs with learning effects and job rejection to minimize the maximum completion time (makespan), total completion time, and total tardiness, separately. We found that the algorithms tend to reject a small number of orders with longer production times and retain more of those with shorter production times when the objective is to minimize the average response time for the new orders. This is contrary to situations when the system’s resource utilization or the delays in fulfilling demand are sought to be minimized. The study also found that orders requiring longer processing times should be scheduled later to improve all three performance metrics with higher learning rates. Finally, we establish that all three extended problems are solvable in pseudo-polynomial time, with complexities of

O (n^{2} E)

for makespan and total completion time minimization, and

O (n^{2} P E)

for total tardiness minimization. The DP algorithms efficiently solve practical-sized instances, as validated by numerical experiments.

学习需要时间，因此应在短期生产计划（即调度）中考虑其影响。当人工参与程度高，车间在工作流程、劳动力或技术方面经历变化时，尤其如此。考虑了具有学习效应的单机调度问题（SMSP）来探索这种相互作用。研究首先证明了最短加工时间调度规则可以解决数学问题。提出了基于动态规划（DP）的伪多项式求解算法，分别求解具有学习效应和作业拒绝的最大完工时间（makespan）最小化、总完工时间最小化和总延迟最小化的smsp问题。我们发现，当目标是最小化新订单的平均响应时间时，算法倾向于拒绝少量生产时间较长的订单，而保留更多生产时间较短的订单。这与试图将系统的资源利用或满足需求的延迟降至最低的情况相反。该研究还发现，需要更长的处理时间的订单应该安排得更晚，以提高这三个性能指标的学习率。最后，我们建立了这三个扩展问题都是在伪多项式时间内可解的，对于最大完工时间和总完工时间的最小化，其复杂度为0 (n2E)，对于总延误的最小化，其复杂度为0 （n2PE）。数值实验验证了算法的有效性。

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引用次数: 0

Smart home economic operation under uncertainty: comparing monte carlo and stochastic optimization using gaussian and KDE-based data 不确定性下的智能家居经济运行：使用高斯和基于kde的数据比较蒙特卡罗和随机优化

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-07-13 DOI: 10.1016/j.orp.2025.100348

Spyros Giannelos, Danny Pudjianto, Goran Strbac

This paper investigates optimal day-ahead operation of a building-scale energy hub equipped with photovoltaics and a battery. Electricity demand and PV availability are uncertain and are represented in two ways: (i) thin-tailed normal distributions and (ii) kernel density estimation (KDE) fitted to empirical CityLearn data. For each representation we evaluate (a) deterministic Monte Carlo analysis, where the hub is optimised separately for 1 000 daily scenarios, and (b) a two-stage stochastic optimisation that fixes one set of decisions for hours 0–11 and adapts for hours 12–23 after conditions are observed. Gaussian inputs yield clustered costs (mean= $51.6, σ= $0.2) and a 99 % CVaR below $52, suggesting negligible risk. KDE inputs raise the Monte Carlo mean to $80.6 and lift the 99 % CVaR to $114, exposing heavy-tailed risk. Within the stochastic model the identical first-stage policy costs $79.0 with Gaussian data but only $71.3 with KDE, as recourse exploits sunny scenarios and trims the 95 % CVaR from $106.4 to $93.5. Consequently, Gaussian assumptions obscure true operating costs and financial exposure, whereas incorporating empirically derived KDE uncertainty within stochastic optimisation both lowers the average cost and provides stronger protection against extreme cost outcomes.

本文研究了一个配备光伏和电池的建筑规模能源枢纽的最优日前运行。电力需求和光伏可用性是不确定的，并以两种方式表示：(i)细尾正态分布和（ii）核密度估计（KDE）拟合经验CityLearn数据。对于每个表示，我们评估(a)确定性蒙特卡罗分析，其中枢纽分别针对1,000个日常场景进行优化，以及(b)两阶段随机优化，该优化在0-11小时内固定一组决策，并在观察到条件后适应12-23小时。高斯输入产生聚类成本（平均值= 51.6美元，σ= 0.2美元），99%的CVaR低于52美元，这表明风险可以忽略不计。KDE输入将蒙特卡罗平均值提高到80.6美元，并将99% CVaR提高到114美元，暴露了重尾风险。在随机模型中，相同的第一阶段政策在高斯数据下的成本为79.0美元，而在KDE数据下仅为71.3美元，因为索赔权利用阳光明媚的情景，将95%的CVaR从106.4美元削减到93.5美元。因此，高斯假设模糊了真实的运营成本和财务风险，而在随机优化中结合经验推导的KDE不确定性既降低了平均成本，又为极端成本结果提供了更强的保护。

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引用次数: 0

A Concise Review of Home Health Care Routing and Scheduling Problem 家庭健康照护路线与排程问题简评

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-07-12 DOI: 10.1016/j.orp.2025.100347

Soumen Atta , Vítor Basto-Fernandes , Michael Emmerich

The Home Health Care Routing and Scheduling Problem (HHCRSP) plays a crucial role in optimizing the delivery of home-based healthcare services by efficiently allocating caregivers to patient locations while adhering to logistical, operational, and regulatory constraints. This concise review provides an analysis of HHCRSP, discussing its key objectives, constraints, and solution methodologies. The study examines various optimization approaches, including exact algorithms, heuristics, and metaheuristic techniques. Furthermore, the impact of HHCRSP on healthcare delivery efficiency is explored, highlighting its role in reducing operational costs, improving service quality, and ensuring continuity of care. The article also discusses the regulatory requirements affecting HHCRSP, addressing compliance with legal and organizational requirements, quality assurance frameworks, economic constraints, and patient prioritization mandates. The challenges associated with HHCRSP, including logistical complexities, workload balancing, and technological barriers, are also reviewed. To align HHCRSP with regulatory frameworks, this review discusses various strategies such as adaptive scheduling, advanced algorithmic solutions, and the integration of environmental and social sustainability considerations. Additionally, emerging technological advancements, including the use of Artificial Intelligence (AI), Internet of Things (IoT), and intelligent transport systems, are evaluated for their potential to enhance HHCRSP efficiency. The article concludes by summarizing key findings, discussing the practical implications of HHCRSP for healthcare providers, and outlining future research directions. Addressing existing gaps, such as AI explainability, blockchain integration for secure scheduling, and sustainable healthcare logistics, remains a critical avenue for further exploration. As the demand for home healthcare services grows, innovative HHCRSP solutions will be essential to ensuring high-quality, cost-effective, and patient-centered care.

家庭医疗保健路线和调度问题（HHCRSP）在优化家庭医疗保健服务的交付方面发挥着至关重要的作用，它通过有效地将护理人员分配到患者所在地，同时遵守后勤、操作和监管限制。这篇简明的综述提供了对HHCRSP的分析，讨论了其主要目标、制约因素和解决方法。该研究考察了各种优化方法，包括精确算法、启发式和元启发式技术。此外，本文还探讨了HHCRSP对医疗服务效率的影响，强调了其在降低运营成本、提高服务质量和确保护理连续性方面的作用。本文还讨论了影响HHCRSP的监管要求，解决了法律和组织要求的遵从性、质量保证框架、经济约束和患者优先级要求。与HHCRSP相关的挑战，包括后勤复杂性、工作负载平衡和技术障碍，也进行了审查。为了使HHCRSP与监管框架保持一致，本综述讨论了各种策略，如自适应调度、先进的算法解决方案以及环境和社会可持续性考虑的整合。此外，新兴的技术进步，包括人工智能（AI）、物联网（IoT）和智能交通系统的使用，对其提高HHCRSP效率的潜力进行了评估。文章最后总结了主要发现，讨论了HHCRSP对医疗服务提供者的实际意义，并概述了未来的研究方向。解决现有的差距，如人工智能的可解释性、用于安全调度的区块链集成以及可持续的医疗保健物流，仍然是进一步探索的关键途径。随着对家庭医疗保健服务需求的增长，创新的HHCRSP解决方案对于确保高质量、低成本和以患者为中心的护理至关重要。

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引用次数: 0

The Robust Steiner Team Orienteering Problem with Decreasing Priorities under budgeted uncertainty 预算不确定性下优先级递减的鲁棒斯坦纳团队定向问题

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-06-28 DOI: 10.1016/j.orp.2025.100344

Lucas Assunção, Andréa Cynthia Santos

Post-disaster relief operations have gained attention over the past decade, focusing on enhancing resilience in labor and social environments. This work introduces the Robust Steiner Team Orienteering Problem with Decreasing Priorities (R-STOP-DP) to model emergency rescue operations where several locations might need relief shuttles, but exact demands cannot be foreseen. R-STOP-DP is a variation of the vehicle routing problem with location priorities that applies robust optimization to model the variability on service times incurred by visiting locations. Locations are sub-divided into mandatory and optional, being the latter linked to priorities that linearly decrease over time. The goal is to find robust feasible routes maximizing the priorities collected, while considering the worst-case conditions of service times within an uncertainty budget and a routes’ duration limit. We propose two compact formulations – reinforced by valid inequalities adapted from the literature – and solve them in a cut-and-branch fashion. In addition, we propose a kernel search mat-heuristic and a simulated annealing heuristic. Computational experiments suggest the strict dominance of one formulation, improving dual bounds by 12.29% on average over the 360 instances tested. The cut-and-branch algorithm based on the stronger model also stands out, solving 20 more instances than the other. The simulated annealing heuristic obtains a remarkable performance by improving over and/or reaching the best-known bounds for the complete benchmark, within an average execution time of 2.52 s. In turn, the kernel search mat-heuristic reaches or improves the bounds for 81% of the instances within 4.5 min of average running time.

在过去的十年里，灾后救援工作受到了人们的关注，重点是提高劳动力和社会环境的复原力。这项工作引入了鲁棒斯坦纳团队定向问题与降低优先级（R-STOP-DP）来模拟紧急救援行动，其中几个地点可能需要救援航天飞机，但确切的需求无法预见。R-STOP-DP是具有位置优先级的车辆路线问题的一种变体，它应用鲁棒优化来模拟由于访问位置而引起的服务时间的变化。地点被细分为强制性和可选性，后者与随时间线性减少的优先级相关。目标是在考虑不确定预算和路线持续时间限制的最坏情况下，找到最大限度地提高所收集的优先级的鲁棒可行路线。我们提出了两个紧凑的公式-通过从文献中改编的有效不等式加强-并以切割和分支的方式解决它们。此外，我们还提出了一种核搜索启发式算法和模拟退火启发式算法。计算实验表明一个公式的严格优势，在360个测试实例中平均提高了12.29%的对偶边界。基于更强模型的切断和分支算法也很突出，比其他算法多解决了20个实例。模拟退火启发式算法在平均2.52秒的执行时间内，通过改进和/或达到完整基准的最知名界限，获得了显着的性能。反过来，内核搜索算法在平均运行时间的4.5分钟内达到或改进了81%的实例的边界。

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引用次数: 0

Improving the robustness of retail workforce management with a labor flexibility strategy and consideration of demand uncertainty 基于劳动力灵活性策略和需求不确定性的零售劳动力管理稳健性研究

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-06-28 DOI: 10.1016/j.orp.2025.100345

Andrés Felipe Porto , Amaia Lusa , Sebastián A. Herazo , César Augusto Henao

This article examines the challenge of personnel scheduling problem by incorporating a labor flexibility approach that integrates annualized hours, multiskilled employees, and overtime within an uncertain demand environment. To address this problem, a two-stage stochastic optimization model is developed to determine the optimal workforce size, structure a targeted training program using a 2-chaining approach, and allocate weekly working hours, both regular and overtime, while explicitly considering demand variability. The proposed approach is assessed through multiple experiments to analyze the impact of incorporating multiskilling and different levels of demand fluctuations. Furthermore, the workforce configuration—comprising staff size and training structure— resulting from the stochastic model is compared with that obtained using a deterministic framework. The findings indicate that the stochastic model yields more robust and cost-effective solutions under demand uncertainty, significantly reducing training costs and minimizing expected labor costs related to overstaffing, understaffing, and wages. Additionally, the results reinforce the synergistic relationship between multiskilling and overtime in mitigating workforce imbalances caused by demand uncertainty. Finally, this research offers strategic insights for managers in retail and service industries aiming to optimize workforce planning and adaptability while maintaining cost efficiency in the face of fluctuating and uncertain demand.

本文通过结合劳动力灵活性方法，在不确定的需求环境中集成年工时、多技能员工和加班时间，来研究人员调度问题的挑战。为了解决这个问题，开发了一个两阶段随机优化模型，以确定最佳劳动力规模，使用2链方法构建有针对性的培训计划，并分配每周工作时间（包括常规和加班），同时明确考虑需求变化。通过多次实验来评估所提议的方法，以分析将多技能和不同水平的需求波动结合起来的影响。此外，将随机模型产生的劳动力配置（包括员工规模和培训结构）与使用确定性框架获得的劳动力配置进行比较。研究结果表明，在需求不确定的情况下，随机模型产生了更稳健和更具成本效益的解决方案，显著降低了培训成本，并最大限度地降低了与人员过剩、人员不足和工资相关的预期劳动力成本。此外，研究结果强化了多技能和加班之间的协同关系，以缓解需求不确定性引起的劳动力失衡。最后，本研究为零售和服务行业的管理者提供了战略见解，旨在优化劳动力规划和适应性，同时在面对波动和不确定的需求时保持成本效率。

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引用次数: 0

Adaptive hybrid optimization for integrated project scheduling and staffing problem with time/resource trade-offs 具有时间/资源权衡的综合项目调度和人员配置问题的自适应混合优化

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-06-25 DOI: 10.1016/j.orp.2025.100346

Muhai Hu , Yao Wang , Wendi Tian

The integration of project scheduling and human resource allocation is crucial in modern project management, particularly in complex and resource-constrained environments. This study addresses the Integrated Project Scheduling and Personnel Staffing Problem (IPSPSP) with time/resource trade-offs by proposing a dual-objective optimization model that minimizes both project duration and personnel cost. To solve this problem, we introduce an adaptive hybrid algorithm combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The algorithm employs hybrid encoding for activity modes, activity priority lists and personnel allocation plans, coupled with a hypervolume-based adaptive search mechanism to improve solution quality. Experimental results demonstrate that the adaptive hybrid algorithm outperforms standalone NSGA-II and MOPSO in generating schedules and optimizing resource allocation. This study makes significant contributions by presenting a novel integrated model tailored for projects, an effective adaptive hybrid optimization algorithm and a comprehensive performance evaluation, thereby advancing the field of integrated scheduling and staffing in project management.

在现代项目管理中，特别是在复杂和资源受限的环境中，项目调度和人力资源配置的整合是至关重要的。本研究通过提出一个双目标优化模型来解决时间/资源权衡的综合项目调度和人员配备问题（IPSPSP），该模型可以最小化项目持续时间和人员成本。为了解决这一问题，我们引入了一种结合非支配排序遗传算法（NSGA-II）和多目标粒子群优化（MOPSO）的自适应混合算法。该算法采用混合编码对活动模式、活动优先级列表和人员分配计划进行编码，并结合基于hypervolume的自适应搜索机制提高求解质量。实验结果表明，自适应混合算法在调度生成和资源优化分配方面优于独立的NSGA-II和MOPSO算法。本研究提出了一种针对项目的新型集成模型、一种有效的自适应混合优化算法和一种全面的绩效评估方法，从而推动了项目管理中集成调度与人员配置领域的发展。

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引用次数: 0