Omega-international Journal of Management Science最新文献

英文中文

Deployment and pricing strategies for different generations of battery swap stations

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-02-25 DOI: 10.1016/j.omega.2025.103302

Yudi Zhang , Bangdong Zhi , Xiaojun Wang , Yang Shen

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 incremental deployment strategy, which involves constructing more current-generation stations over next-generation ones to achieve early expansion, or a leapfrog deployment strategy, 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., service-to-user effect and user-to-service effect), 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 backward infrastructure momentum, 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.

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引用次数: 0

Managing equitable contagious disease testing: A mathematical model for resource optimization

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-02-25 DOI: 10.1016/j.omega.2025.103305

Peiman Ghasemi , Jan Fabian Ehmke , Martin Bicher

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.

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引用次数: 0

Optimal sizing and location of energy storage systems for transmission grids connected to wind farms

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-02-22 DOI: 10.1016/j.omega.2025.103301

Arya Sevgen Misiç , Mumtaz Karatas , Abdullah Dasci

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

_{2}

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.

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引用次数: 0

Supply chain network viability: Managing disruption risk via dynamic data and interaction models

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-02-19 DOI: 10.1016/j.omega.2025.103303

Sha-lei Zhan , Joshua Ignatius , Chi To Ng , Daqiang Chen

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.

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引用次数: 0

Enhanced efficiency assessment in manufacturing: Leveraging machine learning for improved performance analysis

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-02-15 DOI: 10.1016/j.omega.2025.103300

Maria D. Guillen , Vincent Charles , Juan Aparicio

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.

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引用次数: 0

Dynamic bus bridging strategy in response to metro disruptions integrated with routing, timetabling and vehicle dispatching 针对地铁中断的动态公共汽车桥接战略，与路线、时间安排和车辆调度相结合

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-02-14 DOI: 10.1016/j.omega.2025.103287

Yin Yuan , Shukai Li , Shi Qiang Liu , Andrea D’Ariano , Lixing Yang

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.

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引用次数: 0

IoT-driven dynamic replenishment of fresh produce in the presence of seasonal variations: A deep reinforcement learning approach using reward shaping

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-02-12 DOI: 10.1016/j.omega.2025.103299

Zihao Wang , Wenlong Wang , Tianjun Liu , Jasmine Chang , Jim Shi

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.

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引用次数: 0

Acknowledgement to Reviewers

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-02-12 DOI: 10.1016/S0305-0483(25)00022-2

引用次数: 0

Variable range measure: A new range measure for super-efficiency model based on DDF in presence of nonpositive data

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-02-05 DOI: 10.1016/j.omega.2025.103295

Hsuan-Shih Lee

In order to handle the nonpositive data and increase the discrimination power, we propose a new DDF super-efficiency model called variable range measure (VRM). VRM is translation-invariant and unit-invariant. VRM is feasible when data set contains zero or negative data. The super-efficiency obtained by VRM is less than or equal to two. Range adjusted measure (RAM) makes input contraction and output expansion along the direction vector in a balanced way, but it is target-invariant. The range directional model (RDM) for super-efficiency might be infeasible, but it is target-variant. We combine the advantages of RAM and RDM into VRM so that VRM is target-variant and feasible under super-efficiency. Output vector of the direction vector proposed by Lin and Liu (2019) (LL model) might be zero for some DMUs. VRM overcomes the shortcomings of the LL model. We show that the VRM direction vector is a good proxy of the RAM direction vector by examples.

引用次数: 0

A mixed integer programming approach to address cumulative threats in multi action management plans for biodiversity recovery

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science

Pub Date : 2025-01-28 DOI: 10.1016/j.omega.2025.103282

José Salgado-Rojas , Eduardo Álvarez-Miranda , Virgilio Hermoso

Traditionally, most of the prioritization models used by researchers and practitioners, rely on spatially dichotomous settings for threats, for species and for actions’ benefit; i.e., threats and species are present with equal intensity in some territorial units (while in the other units are not present at all), and actions have impact only on those units where they are applied. However, when dealing with ecological phenomena on large and complex territories, characterized by different areas (such as multiple realms or large river basins) and different spatial connectivity patterns among them, such a dichotomous setting does not capture the spatial (cumulative) diffusion of threats and thus actions’ benefits. Hence, common conservation planning tools are likely to misestimate the benefits of actions and the impact of threats, yielding less effective solutions. In order to address this issue, we develop a framework for designing multi-action prioritization plans featuring threats and actions’ benefit spatial diffusion. Our framework relies on a mathematical programming model that identifies priority areas for the implementation of management actions for multiple threats across a complex and large landscape. We consider the particular case an ecological setting characterized by different realms, multiple threats, and multiple species. We use the Tagus River (Iberian Peninsula) as a case study, including four realms (terrestrial, freshwater, estuary, and marine), where we integrate three different types of spatial connectivity: longitudinal along rivers, and multidimensional in the estuary and marine realms. We simulate the spatial diffusion of threats across the study area using four types of decay models (dispersal kernels): one exponential kernel, two negative triangular kernels (medium and high), and no dispersal. The results show how the MIP-based methodology offers a flexible and practical strategy for incorporating the cumulative effects of threats into action management planning. Furthermore, the primal-MIP heuristic was demonstrated to be a noteworthy alternative for finding good bounds of the original MIP model.

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