Mani Venkatesh, Samuel Fosso Wamba, Angappa Gunasekaran, V. G. Venkatesh
{"title":"Emerging trends in the interplay between analytics and operations in MSMEs","authors":"Mani Venkatesh, Samuel Fosso Wamba, Angappa Gunasekaran, V. G. Venkatesh","doi":"10.1007/s10479-025-06704-7","DOIUrl":"10.1007/s10479-025-06704-7","url":null,"abstract":"<div><p>This editorial synthesizes the principal research trends and prospective directions emphasized in the special issue titled Emerging Trends in the Interplay between Analytics and Operations in MSMEs, published in <i>Annals of Operations Research</i>. The contributions underscore the transformative impact of analytics on Micro, Small, and Medium Enterprises (MSMEs), accentuating the integration of Industry 4.0 technologies, artificial intelligence (AI), machine learning, deep learning, blockchain, and big data analytics into operations management. Furthermore, the discussions illuminate emerging trends concerning the application of these technologies in MSMEs, presenting a future roadmap and directions regarding the interplay between analytics and operations. This also affirms a renewed focus on data analytics capabilities in enhancing operational efficiency, resilience, and sustainability within MSMEs. Prospective research directions encompass the development of transparent and responsible AI models, the addressing of implementation challenges related to business intelligence tools, and the fostering of dynamic capabilities to navigate the evolving digital landscape.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"350 2","pages":"355 - 364"},"PeriodicalIF":4.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Combining multiple data resampling methods and classifier ensembles for better financial distress prediction: homogeneous and heterogeneous approaches","authors":"Ya-Han Hu, Chih-Fong Tsai, Pei-Ting Wang","doi":"10.1007/s10479-025-06706-5","DOIUrl":"10.1007/s10479-025-06706-5","url":null,"abstract":"<div><p>Financial distress prediction (FDP) is a critical task for financial institutions and is typically framed as a class imbalance learning problem. To address this challenge, this paper proposes two ensemble-based strategies: the homogeneous and heterogeneous approaches, which combine multiple data re-sampling algorithms to generate diverse re-balanced training sets for classifier construction. Experimental results on seven FDP datasets demonstrate that the heterogeneous approach, which integrates under-, over-, and hybrid sampling methods with their optimal imbalance ratio settings, achieves superior performance in terms of AUC, particularly when applied with the LightGBM and XGBoost classifiers. Regarding Type I error, the heterogeneous combinations consistently outperform the homogeneous and other baseline approaches across various classifiers. The generalizability of the proposed methods is further validated using 37 additional class-imbalanced datasets from different domains, where the heterogeneous approach again shows the most robust performance. These findings suggest that the proposed models can serve as effective decision support tools for financial institutions to enhance credit risk evaluation and lending strategies. From a policy perspective, adopting such predictive frameworks can improve financial stability by reducing exposure to high-risk loans and enabling more accurate early warning systems for economic distress.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"353 2","pages":"793 - 814"},"PeriodicalIF":4.5,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145296542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Are monopolies efficient setters of ethical standards?","authors":"Yahel Giat, Eran Manes","doi":"10.1007/s10479-025-06600-0","DOIUrl":"10.1007/s10479-025-06600-0","url":null,"abstract":"<div><p>We propose a novel analytical framework to study the equilibrium determination of ethical standards when a boycott movement (BM) that represents ethically concerned consumers pressures producers to scale back the production of objectionable products, at the expense of other, ethically indifferent, consumer groups. Focusing on monopolies, we find that under a fixed price regime monopolies—depending on the price—are either over or under appeasing the BM. If monopolies are free to set the price and boycotters substitute ethical violations with price reductions, then monopolies’ distortionary effect is twofold: (i) they are less likely to appease the BM compared to the social planner, and (ii) whenever they choose to appease, they over appease relative to the social optimum. Our results provide theoretical foundations for why producers in industries with abnormal customer willingness to pay such as luxury brands, are less likely to appease. The results also suggest that managers can use pricing mechanisms to exploit ethical demands of their customers. Conversely, governments should consider the welfare loss to the remaining consumer base caused by this exploitation.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"351 3","pages":"1803 - 1829"},"PeriodicalIF":4.5,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Saeed Heidary, Devika Kannan, Saeid Dehghani, Hassan Mina
{"title":"A decision support system for physician scheduling during a public health crisis: a mathematical programming model","authors":"Mohammad Saeed Heidary, Devika Kannan, Saeid Dehghani, Hassan Mina","doi":"10.1007/s10479-025-06654-0","DOIUrl":"10.1007/s10479-025-06654-0","url":null,"abstract":"<div><p>With the occurrence of a public health crisis, the demand for healthcare services increases, which leads to an increase in the workload of hospitals. To overcome this predicament, hospitals should increase the number of their medical staff. Adding new medical staff, especially physicians, is a time-consuming process, and in such a situation, when the society is facing a shortage of physicians, it is almost impossible. Physician scheduling can be a practical solution to overcome this problem. Scheduling physicians without adding new physicians increases the workload of physicians, and this may affect their productivity and the service quality. To solve this problem, in addition to financial incentives, non-financial incentives such as increasing physicians' satisfaction should also be considered. Hence, by applying a novel mixed-integer linear programming (MILP) model, this study configures a decision support system for scheduling physicians by considering physicians' satisfaction during a public health crisis. The purpose of the proposed model is to maximize the fairness in the distribution of workload among physicians by considering their preferences. It should be noted that the satisfaction of physicians is considered using two indicators including equitable shifts distribution and physicians' preferences. The effectiveness of the proposed MILP model is examined using data from a hospital in Iran during the outbreak of the coronavirus disease (COVID-19). The investigated hospital consists of 15 regular departments that are served by 79 physicians. With the spread of COVID-19 pandemic, three departments are added to the existing departments to serve the COVID-19 patients. Finally, the proposed MILP model is implemented with and without considering physicians' preferences, and the effect of considering preferences on physician scheduling is shown.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"351 3","pages":"1831 - 1881"},"PeriodicalIF":4.5,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-025-06654-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"EOQ model with defective products, batch shipment and partial backorders","authors":"Harun Öztürk, Ioannis Konstantaras","doi":"10.1007/s10479-025-06669-7","DOIUrl":"10.1007/s10479-025-06669-7","url":null,"abstract":"<div><p>The existing literature on the economic order quantity (EOQ) problem with backordering does not address the impact of batch shipments on backordering behavior in a business to customer (B2C) environment. This study develops inventory models for a retailer receiving batch shipments and managing inventory through backorders. In this scenario, a large quantity of items is received, some of which are found to be defective. To identify defective items, the retailer conducts a 100% inspection of the goods received. Once inspected, the saleable products are added to the warehouse inventory in batches, rather than individually. The retailer follows a policy of receiving equal-sized batches at regular time intervals, deciding on the number of batches, as well as the ordering and backordering quantities. The analysis explores two approaches for handling defective products, incorporating time-proportioning for the backordering cost and a penalty cost for each lost unit. The classical optimization technique is applied to determine the optimal policy. A numerical example demonstrates the theory, with results showing that partial recovery of customer loyalty and product repair are more profitable approaches.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"351 3","pages":"1941 - 1988"},"PeriodicalIF":4.5,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-025-06669-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Learning ensembles of interpretable simple structure","authors":"Gaurav Arwade, Sigurdur Olafsson","doi":"10.1007/s10479-025-06674-w","DOIUrl":"10.1007/s10479-025-06674-w","url":null,"abstract":"<div><p>Decision-making in complex systems often relies on machine learning models, yet highly accurate models such as XGBoost and neural networks can obscure the reasoning behind their predictions. In operations research applications, understanding how a decision is made is often as crucial as the decision itself. Traditional interpretable models, such as decision trees and logistic regression, provide transparency but may struggle with datasets containing intricate feature interactions. However, complexity in decision-making stems from interactions that are only relevant within certain subsets of data. Within these subsets, feature interactions may be simplified, forming simple structures where simple interpretable models can perform effectively. We propose a bottom-up simple structure-identifying algorithm that partitions data into interpretable subgroups known as simple structures, where feature interactions are minimized, allowing simple models to be trained within each subgroup. We demonstrate the robustness of the algorithm on synthetic data and show that the decision boundaries derived from simple structures are more interpretable and aligned with the intuition of the domain than those learned from a global model. By improving both explainability and predictive accuracy, our approach provides a principled framework for decision support in applications where model transparency is essential.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"353 2","pages":"841 - 869"},"PeriodicalIF":4.5,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145296613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ala-Eddine Yahiaoui, Mikael Rönnqvist, Jean-François Audy
{"title":"A mathheuristic approach for the vehicle routing problem with queuing considerations","authors":"Ala-Eddine Yahiaoui, Mikael Rönnqvist, Jean-François Audy","doi":"10.1007/s10479-025-06647-z","DOIUrl":"10.1007/s10479-025-06647-z","url":null,"abstract":"<div><p>Queuing in vehicle routing problems happens when a given node requires to be visited by several vehicles, whereas only a limited number of vehicles can perform the service simultaneously. Hence, some vehicles must wait until the node is available. We present in this paper a mathheuristic approach to solve the problem. This approach incorporates two phases. The first phase executes a rolling horizon heuristic multiple times to generate an initial set of solutions. Those generated solutions are used to initialize a pool of routes. In the second phase, a column-generation based procedure is used to generate new routes. The contribution of our paper can be summarized as follows. (1) We implemented an efficient set partitioning model that allocates pre-determined slots of time to service operations of vehicles. (2) We proposed fast pricing heuristics to generate new routes with negative reduced costs. (3) The newly generated routes are based on existing ones, keeping the same physical description but the starting times of service operations are modified to better fit the queuing aspects. Performance evaluation has been conducted using instances derived from data provided by forest companies. Experiments proved the effectiveness of the proposed approach, by recording low route duration and achieving almost zero queuing times compared to the initial pool of solutions.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"350 3","pages":"1307 - 1330"},"PeriodicalIF":4.5,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Emergency preparedness: optimal pharmacy purchasing strategies","authors":"Renbang Shan, Li Luo, Jie Xiang","doi":"10.1007/s10479-025-06677-7","DOIUrl":"10.1007/s10479-025-06677-7","url":null,"abstract":"<div><p>This study examines the purchasing decision-making of retail pharmacies when the potential for emergencies arises within a single cycle. Beyond accounting for conventional demand, retail pharmacies also need to plan for emergency demand. This paper employs the classic newsvendor model as a benchmark (PN) and explores three pre-purchasing strategies: a combination of conventional procurement and option procurement (POM), one-time procurement taking possible emergencies into account (PNO), a combination of conventional procurement and emergency procurement (PNE). Through an analysis of these procurement strategies, we find that, while POM usually performs better as a strategy, its position is affected by emergency shortage cost, exercise price, inventory cost, and the timing of emergency situations. Especially, exercise price changes does not always benefit retail pharmacies. Furthermore, neither PNO nor PNE provide any absolute advantages. PNO performs excellently when faced with higher emergency wholesale prices or lower emergency shortage costs. On the contrary, under certain conditions, PNE becomes a favorable choice for retail pharmacies. Specifically, when the emergency demand of retail pharmacies increases significantly, PNE is most suitable for retail pharmacies, while PNO is more advantageous when emergency situations occur near the end of the cycle.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"350 3","pages":"1207 - 1252"},"PeriodicalIF":4.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145162457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roger X. Lera-Leri, Filippo Bistaffa, Tomas Trescak, Juan A. Rodríguez-Aguilar
{"title":"Computing job-tailored degree plans towards the acquisition of professional skills","authors":"Roger X. Lera-Leri, Filippo Bistaffa, Tomas Trescak, Juan A. Rodríguez-Aguilar","doi":"10.1007/s10479-025-06678-6","DOIUrl":"10.1007/s10479-025-06678-6","url":null,"abstract":"<div><p>Sensibly planning the subjects to study during a university degree is one of the most crucial tasks that impact the future professional life of a student. Nonetheless, to the best of our knowledge, no automated solution is available for students who want to plan their desired degree path and maximize the skills required by desired or target job(s). In this paper, we consider the <i>Degree Planning Problem</i> (DPP), which aims at computing degree plans composed of university subjects for students during the completion of an undergraduate degree. Specifically, we aim to obtain the best set of skills matching the requirements of students’ preferred job(s). To achieve this objective, we propose a flexible and scalable approach that solves the DPP in real-time by means of a non-trivial formalization as an optimization problem that can be solved with standard solvers. Finally, we employ real data from our University’s Bachelor in Information and Communications Technology to show, through several use cases, that our approach can be a valuable decision-support tool for students and curriculum designers.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"351 3","pages":"2095 - 2128"},"PeriodicalIF":4.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-025-06678-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}