Omega-international Journal of Management Science最新文献

{"title":"Lean operations and firm resilience - contrasting effects of COVID-19 and economic recession","authors":"Nagihan Çömez-Dolgan ,&nbsp;Başak Tanyeri-Günsür ,&nbsp;Feng Mai ,&nbsp;Xuying Zhao ,&nbsp;Sarv Devaraj","doi":"10.1016/j.omega.2025.103308","DOIUrl":"10.1016/j.omega.2025.103308","url":null,"abstract":"<div><div>In recent times, there has been a call in the Operations Management discipline to study the effect of operations strategies during pandemics. Firms adopt various strategies to sustain their competitiveness and reduce the likelihood of financial distress. Operating lean is one of these strategies to achieve sustainable efficiency and success. However, there is little empirical evidence on whether lean is an effective strategy for reducing future financial distress and remaining resilient and viable. In this study, we examine if a firm's operational leanness along three dimensions – inventory, property/plant/equipment (PPE), and supply chain – impacts the probability of future financial distress and if the effects from these dimensions are substitutable. An equally interesting and related question is whether and how this relationship is affected by challenging macroeconomic times that cause shocks to the supply chain. Specifically, we study the contextual effect of 2020 COVID-19 and also the 2001 and 2008 economic recessions. Our results address the impact of operating lean as well as highlight the differential effects of the pandemic and the economic recession on the relationship between operational leanness and the likelihood of financial distress.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"135 ","pages":"Article 103308"},"PeriodicalIF":6.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When and how to introduce live streaming for video game? Consumers’ trade-off between buying and watching","authors":"Lingyu Qiao,&nbsp;Wansheng Tang,&nbsp;Guangwei Hao,&nbsp;Yi Xia,&nbsp;Jianxiong Zhang","doi":"10.1016/j.omega.2025.103306","DOIUrl":"10.1016/j.omega.2025.103306","url":null,"abstract":"<div><div>Video game industry has developed as a significant sector of entertainment, with game providers now exploring new business opportunities as the industry matures. Among them, the emerging trend of live streaming has captured considerable attention. However, the interaction between video game and video game live streaming forms a nuanced relationship of coopetition — live streaming expands the market while cannibalizes it, which has sparked numerous debates on game providers’ motivation to introduce live streaming. In this paper, leveraging a sequential game-theoretic model in which there is one game provider and one livestreamer that consumers can experience the game by purchasing it or watching live streaming, we study the game provider’s live streaming introduction and revenue sharing decisions and their impact on the profitability of the game provider and the livestreamer. In contrast to the finding of prior research that live streaming always benefits the game provider, our study reveals that the game provider may not introduce live streaming when her initial informed consumer base is large. However, revenue sharing mechanisms, including the game provider sharing revenue and the livestreamer sharing revenue, can effectively increase the game provider’s profitability, inducing her to introduce live streaming. And both the revenue sharing mechanisms under their specific conditions would become the game provider’s optimal choice. Interestingly, different from prior research examining coopetition within supply chain management, we find that the game provider can benefit from giving up her whole market share in the sales channel with the livestreamer sharing revenue. This outcome highlights the experiential nature of video game products. Furthermore, both mechanisms may lead to win–win situation for the game provider and the livestreamer.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"135 ","pages":"Article 103306"},"PeriodicalIF":6.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deployment and pricing strategies for different generations of battery swap stations","authors":"Yudi Zhang ,&nbsp;Bangdong Zhi ,&nbsp;Xiaojun Wang ,&nbsp;Yang Shen","doi":"10.1016/j.omega.2025.103302","DOIUrl":"10.1016/j.omega.2025.103302","url":null,"abstract":"<div><div>With an acceleration of electric vehicle uptake, battery swapping services, which offer quicker energy replenishment than plug-in charging services, are becoming increasingly vital. However, the mass adoption of battery swapping services relies heavily on the establishment of adequate energy replenishment infrastructure to address customer concerns regarding travel costs, service availability, and waiting time. In this study, we explore the optimal deployment strategy for different generations of battery swap stations, where the battery swapping service provider has two options: an <em>incremental deployment strategy</em>, which involves constructing more current-generation stations over next-generation ones to achieve early expansion, or a <em>leapfrog deployment strategy</em>, which prioritizes building more next-generation stations on top of current ones to facilitate late expansion. Our results illustrate a two-sided network effect, (i.e., <em>service-to-user effect</em> and <em>user-to-service effect</em>), where increasing the number of current-generation stations can incentivize the deployment of next-generation stations. This cycle is referred to as forward infrastructure momentum. We also demonstrate a <em>backward infrastructure momentum</em>, indicating that the deployment of next-generation stations can also create momentum for the early establishment of current-generation stations, but this occurs if and only if the service provider is more strategic. Our research provides valuable insights for managers on pricing and deployment of next-generation stations. For instance, technological improvements could decelerate the pace at which service providers deploy next-generation battery swap stations. Continuous improvements in service speed offered by next-generation stations might motivate the service provider to prioritize immediate expansion by constructing more current-generation stations to leverage the user-to-service network effect to achieve profit-maximization. Such an expansion allows them to attract more demand with higher service price.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"134 ","pages":"Article 103302"},"PeriodicalIF":6.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Managing equitable contagious disease testing: A mathematical model for resource optimization","authors":"Peiman Ghasemi ,&nbsp;Jan Fabian Ehmke ,&nbsp;Martin Bicher","doi":"10.1016/j.omega.2025.103305","DOIUrl":"10.1016/j.omega.2025.103305","url":null,"abstract":"<div><div>All nations in the world were under tremendous economic and logistical strain as a result of the advent of COVID-19. Early in the epidemic, getting COVID-19 diagnostic tests was a significant difficulty. Furthermore, logistical challenges arose from the restricted transportation infrastructure and disruptions in international supply chains in the distribution of these testing kits. In the face of such obstacles, it is critical to give patients' needs top priority in order to provide fair access to testing. In order to manage contagious disease testing, this work proposes a bi-objective and multi-period mathematical model with an emphasis on mobile tester route plans and testing resource allocation. In order to optimize patient scores and reduce the likelihood of patients going untreated, the suggested team orienteering model takes into account issues like resource limitations, geographic clustering, and testing capacity limitations. To this aim, we present a comparison between quarantine and non-quarantine scenarios, introduce an equitable categorization based on disease backgrounds into “standard” and “risky” groups, and cluster geographical locations according to average age and contact rate. We use a Multi-Objective Variable Neighborhood Search (MOVNS) and a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve our problem. Due to the superiority of MOVNS, it is applied to a case study in Vienna, Austria. The results demonstrate that, over the course of several weeks, the average number of unserved risky patients in the prioritizing scenario is consistently lower than the usual number of patients. In the absence of prioritization, the average number of high-risk patients who remain untreated rises sharply and exceeds that of regular patients, though. Furthermore, it is clear that waiting times are greatly impacted by demand volume when comparing scenarios with and without quarantine.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"135 ","pages":"Article 103305"},"PeriodicalIF":6.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal sizing and location of energy storage systems for transmission grids connected to wind farms","authors":"Arya Sevgen Misiç ,&nbsp;Mumtaz Karatas ,&nbsp;Abdullah Dasci","doi":"10.1016/j.omega.2025.103301","DOIUrl":"10.1016/j.omega.2025.103301","url":null,"abstract":"<div><div>Although modern renewable power sources such as solar and wind are increasing their share of the world’s power generation, they need to grow faster to replace a greater share of coal and gas power generation and thus, help prevent CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> and other greenhouse gas emissions to reach critical levels. Renewable energy generation must be coupled with energy storage systems, which are unfortunately expensive investments. However, substantial cost savings may be possible if a system-wide solution is sought. This paper presents such an attempt for a transmission grid that has a mixture of renewable and non-renewable sources. The particular problem is to find the type, location and size of the storage systems in the grid, as well as the structure of the transmission network, to minimize total investment and system-wide operating costs of power generation, transmission and storage. A mixed integer linear programming formulation is devised for the problem, which can be very large because various operational decisions are made at short intervals. Hence, we develop a “divide-and-conquer” type solution approach based on time decomposition, wherein the problem is first solved in monthly time segments. Subsequently, optimal or near-optimal monthly generation schedules are merged to construct the greater portion of a grand schedule for the whole year. Although still considerably large, the model can be solved effectively after another set of heuristically developed restrictions on the transmission network structure. The formulation and solution method are implemented on a series of realistic instances for a modest-sized transmission grid adapted from Sardinia Island of Italy to demonstrate the effectiveness of the approach and the insight into related design decisions.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"134 ","pages":"Article 103301"},"PeriodicalIF":6.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supply chain network viability: Managing disruption risk via dynamic data and interaction models","authors":"Sha-lei Zhan ,&nbsp;Joshua Ignatius ,&nbsp;Chi To Ng ,&nbsp;Daqiang Chen","doi":"10.1016/j.omega.2025.103303","DOIUrl":"10.1016/j.omega.2025.103303","url":null,"abstract":"<div><div>This study addresses the challenge of enhancing viability of an interconnected supply chain network, particularly in the context of low-probability high-impact events that recur unpredictably. We re-examine the viability from the views of agility, resilience, and sustainability, and propose a novel hybrid approach which integrates dynamic network data and multi-echelon interaction. Diverging from traditional static approaches, we introduce a dynamic decision-making framework that strategically maintains long-term survival by coordination between timely response actions and the risk of overreaction. A data-driven hidden Markov model is built to update the risk forecasting via dynamic network data. A Bayesian network game theoretical model is developed to support collaborative risk mitigating via the multi-echelon interaction. The main findings are as follows. In the short term, we encourage enterprises to engage in collaborative risk mitigating to significantly increase the likelihood of reaching a consensus on achieving a more cost-efficient level of risk mitigation, marked by an intriguing interplay between weakened individual fairness and the tendency to mitigate network-wide risk more economically. In the long term, we advocate building a data-driven, structure-dynamic, and interaction-focused risk response timing system to enable the network to adapt to changes swiftly and achieve desired viable levels efficiently.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"134 ","pages":"Article 103303"},"PeriodicalIF":6.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced efficiency assessment in manufacturing: Leveraging machine learning for improved performance analysis","authors":"Maria D. Guillen ,&nbsp;Vincent Charles ,&nbsp;Juan Aparicio","doi":"10.1016/j.omega.2025.103300","DOIUrl":"10.1016/j.omega.2025.103300","url":null,"abstract":"<div><div>This paper introduces EATBoosting, a novel application of gradient tree boosting within the Data Envelopment Analysis (DEA) framework, designed to address undesirable outputs in printed circuit board (PCB) manufacturing. Recognizing the challenge of balancing desirable and undesirable outputs in efficiency assessments, our approach leverages machine learning to enhance the discriminatory power of traditional DEA models, facilitating more precise efficiency estimations. By integrating gradient tree boosting, EATBoosting optimizes the handling of complex data patterns and maximizes accuracy in predicting production functions, thus improving upon the deterministic nature of conventional DEA and Free Disposal Hull methods. The practicality of our approach is demonstrated through its application to a PCB assembly process, highlighting its capacity to discern subtle inefficiencies that traditional methods might overlook. This methodology not only enriches the analytical toolkit available for operational efficiency analysis but also sets a precedent for incorporating advanced machine learning techniques in performance evaluation across various industries. Looking forward, the continued integration of such innovative methods promises to revolutionize efficiency analysis, making it more adaptive to complex industrial challenges and more reflective of real-world production dynamics. This work not only broadens the scope of DEA applications but also invites further research into the integration of machine learning to refine performance measurement and management.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"134 ","pages":"Article 103300"},"PeriodicalIF":6.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic bus bridging strategy in response to metro disruptions integrated with routing, timetabling and vehicle dispatching","authors":"Yin Yuan ,&nbsp;Shukai Li ,&nbsp;Shi Qiang Liu ,&nbsp;Andrea D’Ariano ,&nbsp;Lixing Yang","doi":"10.1016/j.omega.2025.103287","DOIUrl":"10.1016/j.omega.2025.103287","url":null,"abstract":"<div><div>Unplanned metro disruptions always result in severe confusion and delays, while bus bridging can provide a promising resolution by efficient evacuating stranded passengers. This article investigates the dynamic bus bridging problem under metro disruptions to generate the routing, timetabling and vehicle dispatching schemes for bus bridging services in an online fashion. Specifically, we formulate a mixed-integer non-linear programming model for each decision stage, with the objective of minimizing passenger travel times and operational costs. This model focuses on the role of multimodal transportation in improving the overall urban public transportation network’s responses to metro disruption emergencies, which involves the utilization of temporary bus bridging services and the spare capacity of unaffected metro lines, passenger transfers and path choices. To address the model complexity, we propose a two-level decomposition approach to split the original problem into the master problem and subproblem. The approach can ensure the optimal solution in finite iterations. To further improve the performance of the solution approach, we design multiple acceleration techniques (i.e., customizing integer cuts supporting parallel computation, solution adjustment, domain reduction for the master problem, warm start and bound contraction for the subproblem) without compromising optimality. Extensive experiments verify that the proposed method can effectively evacuate stranded passengers, improving passenger satisfaction and meanwhile reducing operational costs. The proposed two-level decomposition approach with multiple acceleration techniques demonstrates higher computational efficiency than the common commercial solver and standard two-level decomposition approach, facilitating timely disruption responses. Additionally, according to the computational results, we derive a series of managerial insights for decision-makers.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"134 ","pages":"Article 103287"},"PeriodicalIF":6.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint optimization of order- and rack-scheduling in KIVA picking systems","authors":"Xiying Yang ,&nbsp;Guowei Hua ,&nbsp;Li Zhang ,&nbsp;Tai Chiu Edwin Cheng ,&nbsp;Tsan-Ming Choi","doi":"10.1016/j.omega.2025.103286","DOIUrl":"10.1016/j.omega.2025.103286","url":null,"abstract":"<div><div>We study the order processing operations in KIVA robot-assisted warehouses, where racks are delivered to multiple workstations by robots so that pickers at each workstation just focus on retrieving items from the racks to fulfill the orders. In this context, the order- and rack-scheduling, including their assignment and sequencing decisions, should be considered integrally as they are closely related and can enhance systemic efficiency by leveraging their synergy. We thus consider the comprehensive problem of jointly allocating orders and racks to workstations under workload balancing and sequencing their interlinked processing flows. We formulate it as a mixed-integer programming model to minimize the total number of rack visits. To tackle this model, we present a simulated annealing search framework, which builds on a relaxation model and a best-first-search heuristic exploiting the problem structure. Computational studies show that our proposed approach performs well on small-sized instances. On a large scale, it outperforms both the rule-based policy and two other state-of-the-art algorithms in terms of solution quality. We also conduct sensitivity analysis to generate some managerial insights for real-world warehouse operations.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"135 ","pages":"Article 103286"},"PeriodicalIF":6.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT-driven dynamic replenishment of fresh produce in the presence of seasonal variations: A deep reinforcement learning approach using reward shaping","authors":"Zihao Wang ,&nbsp;Wenlong Wang ,&nbsp;Tianjun Liu ,&nbsp;Jasmine Chang ,&nbsp;Jim Shi","doi":"10.1016/j.omega.2025.103299","DOIUrl":"10.1016/j.omega.2025.103299","url":null,"abstract":"<div><div>Internet of things (IoT) has been transforming inventory management disruptively by linking and synchronizing inventory products together. It is one of the driving forces for the prevailing innovation of AgriTech. For fresh produce replenishment in the presence of its inherent seasonal variations, not only can IoT devices capture bidirectional seasonal information of lead time and demand, but also detect fresh produce loss and waste (FPLW) caused by deterioration. With the aid of the massive data collected by IoT, we propose a data-driven deep reinforcement learning (DRL) approach using reward shaping, called DQN-SV-RS, to optimize the dynamic replenishment policy for a fresh produce wholesaler, specifically addressing the challenge posed by seasonal variations. Experimental results show that our DQN-SV-SR approach yields significant improvements for fresh produce supply chain (FPSC) inventory management, especially achieving a remarkable reduction in FPLW. As a core innovation in our DQN-SV-SR approach, the introduced reward shaping can significantly mitigate lost sales and inventory holding, thereby lowering the total cost. Furthermore, with numerical experiments based on real business data, our proposed approach is demonstrated with plausible robustness and scalable applicability.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"134 ","pages":"Article 103299"},"PeriodicalIF":6.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}