Operations Research Perspectives最新文献

Development of a robust design optimization algorithm for hierarchical time series pharmaceutical problems 层次时间序列药物问题鲁棒设计优化算法的发展

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-09-19 DOI: 10.1016/j.orp.2025.100355

Vo Thanh Nha , Kyungjin Park , Hyeonae Jang , Gyu M. Lee , Tuan-Ho Le , Seong Hoon Jeong , Sangmun Shin

{"title":"Development of a robust design optimization algorithm for hierarchical time series pharmaceutical problems","authors":"Vo Thanh Nha , Kyungjin Park , Hyeonae Jang , Gyu M. Lee , Tuan-Ho Le , Seong Hoon Jeong , Sangmun Shin","doi":"10.1016/j.orp.2025.100355","DOIUrl":"10.1016/j.orp.2025.100355","url":null,"abstract":"<div><div>Experimental design and robust design (RD) methodologies have received attention from researchers to improve the performance of many different quality characteristics and solve problems at low costs. However, there is room for improvement to simultaneously solve interdisciplinary optimization problems associated with time-oriented, multiple, and hierarchical responses. This paper proposes a new RD modeling and optimization algorithm for drug development based on three research motivations: Firstly, customized experiments and estimation frameworks for representing pharmaceutical quality characteristics (i.e., time-oriented, multiple, and hierarchical responses) and functional relationships between input factors and hierarchical time-oriented output responses are proposed. Secondly, new hierarchical time-oriented robust design (HTRD) optimization models (i.e., priority-based, weight-based, and integrated models) are developed for these interdisciplinary pharmaceutical formulation problems. Finally, the pharmaceutical case study for drug formulation development is conducted for demonstration purposes. Based on the case study results, the proposed approach can provide optimal solutions with significantly small biases and variances.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100355"},"PeriodicalIF":3.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

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

Sina Sayardoost Tabrizi , Saeed Yousefi , Keikhosro Yakideh

{"title":"Forecasting efficiency of two-stage Petrochemical sustainable supply chains using Deep Learning and DNDEA Model","authors":"Sina Sayardoost Tabrizi , Saeed Yousefi , Keikhosro Yakideh","doi":"10.1016/j.orp.2025.100354","DOIUrl":"10.1016/j.orp.2025.100354","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100354"},"PeriodicalIF":3.7,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145117677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"A hybrid optimization and data-driven approach to understand the role of the risk-aversion profile parameter in portfolio optimization problems with shorting constraints","authors":"Mariano Carbonero-Ruz , Francisco Fernández-Navarro , Antonio M. Durán-Rosal , Javier Pérez-Rodríguez","doi":"10.1016/j.orp.2025.100353","DOIUrl":"10.1016/j.orp.2025.100353","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100353"},"PeriodicalIF":3.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Jorge A. Huertas, Pascal Van Hentenryck

{"title":"Constraint programming models for serial batch scheduling with minimum batch size","authors":"Jorge A. Huertas, Pascal Van Hentenryck","doi":"10.1016/j.orp.2025.100352","DOIUrl":"10.1016/j.orp.2025.100352","url":null,"abstract":"<div><div>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 <em>Interval Assignment</em> model that computes and bounds the size of the batches using the presence literals of interval variables of the jobs. (ii) A <em>Global</em> model that exclusively uses global constraints that track the size of the batches over time. (iii) And a <em>Hybrid</em> 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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100352"},"PeriodicalIF":3.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Chunlai Yu, Xiaoming Wang, Qingxin Chen

{"title":"An enhanced tabu search algorithm for resource-constrained project scheduling with a flexible project structure","authors":"Chunlai Yu, Xiaoming Wang, Qingxin Chen","doi":"10.1016/j.orp.2025.100349","DOIUrl":"10.1016/j.orp.2025.100349","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100349"},"PeriodicalIF":3.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

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

{"title":"The interplay between learning effect and order acceptance in production planning","authors":"Kuo-Ching Ying , Pourya Pourhejazy , Wei-Jie Zhou","doi":"10.1016/j.orp.2025.100350","DOIUrl":"10.1016/j.orp.2025.100350","url":null,"abstract":"<div><div>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 <span><math><mrow><mi>O</mi><mo>(</mo><mrow><msup><mi>n</mi><mn>2</mn></msup><mi>E</mi></mrow><mo>)</mo></mrow></math></span> for makespan and total completion time minimization, and <span><math><mrow><mi>O</mi><mo>(</mo><mrow><msup><mi>n</mi><mn>2</mn></msup><mi>P</mi><mi>E</mi></mrow><mo>)</mo></mrow></math></span> for total tardiness minimization. The DP algorithms efficiently solve practical-sized instances, as validated by numerical experiments.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100350"},"PeriodicalIF":3.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Spyros Giannelos, Danny Pudjianto, Goran Strbac

{"title":"Smart home economic operation under uncertainty: comparing monte carlo and stochastic optimization using gaussian and KDE-based data","authors":"Spyros Giannelos, Danny Pudjianto, Goran Strbac","doi":"10.1016/j.orp.2025.100348","DOIUrl":"10.1016/j.orp.2025.100348","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100348"},"PeriodicalIF":3.7,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Soumen Atta , Vítor Basto-Fernandes , Michael Emmerich

{"title":"A Concise Review of Home Health Care Routing and Scheduling Problem","authors":"Soumen Atta , Vítor Basto-Fernandes , Michael Emmerich","doi":"10.1016/j.orp.2025.100347","DOIUrl":"10.1016/j.orp.2025.100347","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100347"},"PeriodicalIF":3.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Lucas Assunção, Andréa Cynthia Santos

{"title":"The Robust Steiner Team Orienteering Problem with Decreasing Priorities under budgeted uncertainty","authors":"Lucas Assunção, Andréa Cynthia Santos","doi":"10.1016/j.orp.2025.100344","DOIUrl":"10.1016/j.orp.2025.100344","url":null,"abstract":"<div><div>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 <em>uncertainty budget</em> 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 <em>kernel search</em> mat-heuristic and a <em>simulated annealing</em> 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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100344"},"PeriodicalIF":3.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Improving the robustness of retail workforce management with a labor flexibility strategy and consideration of demand uncertainty","authors":"Andrés Felipe Porto , Amaia Lusa , Sebastián A. Herazo , César Augusto Henao","doi":"10.1016/j.orp.2025.100345","DOIUrl":"10.1016/j.orp.2025.100345","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100345"},"PeriodicalIF":3.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0