Omega-international Journal of Management Science最新文献

{"title":"A mixed integer programming approach to address cumulative threats in multi action management plans for biodiversity recovery","authors":"José Salgado-Rojas ,&nbsp;Eduardo Álvarez-Miranda ,&nbsp;Virgilio Hermoso","doi":"10.1016/j.omega.2025.103282","DOIUrl":"10.1016/j.omega.2025.103282","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103282"},"PeriodicalIF":6.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166784","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":"Homing strategies for asymmetric sellers on differentiated platforms","authors":"Xiang Li,&nbsp;Meiqi Wang","doi":"10.1016/j.omega.2025.103284","DOIUrl":"10.1016/j.omega.2025.103284","url":null,"abstract":"<div><div>With the flourishing development of e-marketplace platforms, sellers’ homing strategies have attracted considerable attention and require careful evaluation of the market environment, especially considering platform competition. This study explores two asymmetric sellers' homing and pricing strategies on two asymmetric platforms. The platforms and sellers are differentiated in their credits and boast varied consumers’ willingness to pay. We identify the competition-mitigating effects of utilizing an inferior platform and adopting single-homing strategies. We indicate that such effects should be carefully considered in balance with the valuation-enhancing effect of choosing a superior platform and the market-expanding effect of adopting multi-homing strategy. Moreover, in some situations, the benefits of seller prioritization of platform occupancy and implementation of an “either-or” policy (i.e., mandatory single-homing policy for sellers) can be less than expected. These results enhance our understanding of homing strategy optimization in the face of competition between sellers and platforms.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103284"},"PeriodicalIF":6.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166786","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":"Strategic buyer stockpiling in supply chains under uncertain product availability and price fluctuation","authors":"Shanshan Li ,&nbsp;Yong He ,&nbsp;Shibo Jin ,&nbsp;Xue Yan","doi":"10.1016/j.omega.2025.103283","DOIUrl":"10.1016/j.omega.2025.103283","url":null,"abstract":"<div><div>Due to concerns about potential product shortages and price increases from an unforeseen production disruption of uncertain length, strategic buyers might decide to stockpile extra products. Such stockpiling behavior could amplify the disruption-induced imbalance between demand and supply, leading to new stockpiling-driven shortages. This paper studies the interaction between product shortages and stockpiling behavior, and the identification and optimization of such stockpiling behavior. Firstly, considering both strategic and non-strategic buyers, the forecasting of stockpiling-driven product shortage is analyzed. Then, by maximizing the perceived value of hoarding for strategic buyers, a stockpiling time model and strategies are proposed. The stockpiling-driven shortages fall into three patterns, mainly depending on the proportion of strategic buyers in the market, the intensity and duration of production interruption, and the product availability linked to the uninterrupted manufacturer. The optimal stockpiling strategy may appear in five scenarios, i.e., “stockpiling at the beginning of disruption”, “stockpiling before the product price rises”,“stockpiling when the product price rises”, “stockpiling after the product price rises”, and “non-stockpiling”. The decision mainly depends on the trade-off between the determined inventory holding cost at the present stage and the uncertain future loss caused by price increases and stock-outs. To be specific, inventory holding cost, the length of product shortages triggered by strategic buyers’ dynamic stockpiling behavior, and the shortfall level in real-time production linked with buyer composition, disruption intensity, and manufacturer's capability.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103283"},"PeriodicalIF":6.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166785","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":"Multi-view reject inference for semi-supervised credit scoring with consistency training and three-way decision","authors":"Haoxin Tang,&nbsp;Decui Liang","doi":"10.1016/j.omega.2025.103280","DOIUrl":"10.1016/j.omega.2025.103280","url":null,"abstract":"<div><div>In credit scoring, reject inference based on semi-supervised learning has shown better performance compared to those based on statistical methods. However, the problem of inconsistent data distribution between accepted and rejected samples still exists during model training, which may violate the smoothness assumption of semi-supervised learning. Besides, multi-view learning has demonstrated its effectiveness, but its validity in reject inference still needs to be verified. Therefore, this paper proposes a multi-view reject inference approach (MRIA) based on three-way decision and consistency training. Specifically, with the aid of three-way decision, we sift valuable rejected samples from the profitability and accuracy objects, which brings the rejected samples better approximate the smooth assumption of semi-supervised learning. Then, based on the above-mentioned two objects, we construct multi-views by utilizing feature selection and train the reject inference model using consistency training, which can enhance the reliability and robustness. Finally, a dynamic fusion method built on the distance to model (DM) is employed for multi-view fusion. This paper not only theoretically demonstrates that high-quality data augmentation consistency training can result in a smaller error bound for the reject inference tasks, but also verifies the effectiveness of MRIA via a series of experimental analysis.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103280"},"PeriodicalIF":6.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166847","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 learning from limited experience takes off: Linear/curvilinear relationships between decision-making comprehensiveness and sustainable supply chain performance under contingent conditions","authors":"Tessa Tien Nguyen ,&nbsp;Angelina Nhat Hanh Le ,&nbsp;Wesley J. Johnston ,&nbsp;Julian Ming Sung Cheng","doi":"10.1016/j.omega.2025.103285","DOIUrl":"10.1016/j.omega.2025.103285","url":null,"abstract":"<div><div>Studies examining the effect of sustainability decision-making comprehensiveness among supply chain alliances on sustainable supply chain management (SSCM) performance have been underrepresented in the current literature. This linking relationship is investigated herein through the theoretical lens of the Organizational Learning Curve (OLC) and Learning from Limited Experience (LLE), and detailed insights under contingent conditions are also provided. The field research is based on a mixed-method approach, including two sequential studies—a quantitative survey followed by a qualitative study in Vietnam. Their results confirm and validate the proposed <em>J</em>-shaped, curvilinear effects of sustainability decision-making comprehensiveness on economic and social sustainability performances and a linear effect on environmental sustainability performance. Moreover, competitive intensity and opportunism moderate these direct effect relationships. We contribute to a crucial gap within the SSCM literature and expand the applications of both OLC and LLE while drawing up a set of practical guidelines in the studied subject matter.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"134 ","pages":"Article 103285"},"PeriodicalIF":6.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143214718","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":"Economic and environmental impacts of ecolabeling under different product cost structures","authors":"Sai Zhao,&nbsp;Hongbo Duan","doi":"10.1016/j.omega.2025.103275","DOIUrl":"10.1016/j.omega.2025.103275","url":null,"abstract":"<div><div>The environmental quality of a product is often a credence attribute for consumers, remaining unobservable even after purchase and use. To enhance consumer trust, firms can adopt ecolabels initiated by third-party organizations. This paper investigates the impact of ecolabeling on firms, consumers, and the environment in a differentiated market under different product cost structures. Two product types are analyzed based on the costs of quality improvement: marginal cost-intensive products (MIPs) and development-intensive products (DIPs). We find that for MIPs, both firms consistently choose the same certification strategy, whereas for DIPs, asymmetric strategies may occur in equilibrium under certain labeling standards. When both firms adopt certification (i.e., full adoption), ecolabeling can (weakly) reduce firm profits and consumer surplus. However, when only one firm adopts the label (i.e., partial adoption), it generally benefits the certified firm and consumers. Although the introduction of ecolabeling helps to improve the environment, a higher labeling standard does not necessarily translate to better environmental outcomes. Actually, in a highly competitive market with strong consumer environmental awareness, inducing partial adoption with a high standard is more effective than achieving full adoption with a low standard. Additionally, we extend our model to incorporate more general settings, such as cost asymmetry, price competition, consumer heterogeneity, and partial consumer trust in uncertified products, to enhance our managerial insights.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103275"},"PeriodicalIF":6.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166849","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":"Using explainable deep learning to improve decision quality: Evidence from carbon trading market","authors":"Yang Zhao,&nbsp;Jianzhou Wang,&nbsp;Shuai Wang,&nbsp;Jingwei Zheng,&nbsp;Mengzheng Lv","doi":"10.1016/j.omega.2025.103281","DOIUrl":"10.1016/j.omega.2025.103281","url":null,"abstract":"<div><div>To achieve the United Nations Sustainable Development Goals (SDGs), reducing global greenhouse gas emissions is a top priority. Academia and industry have recognized the importance of carbon market management in promoting low-carbon development. However, traditional methods exhibit limitations in balancing accuracy and explainability, thereby reducing trust between users and decision-making models. To address this, we develop a data-driven model to enhance decision quality. Specifically, we evaluate and compare deep learning (DL) algorithms of various structures to explore the most appropriate techniques for modeling high-dimensional nonlinear carbon price data. Furthermore, we incorporate model-agnostic interpretation techniques to infer the contribution of the influencing factors to carbon prices. The results indicate that the predictive performance of the DL algorithm after feature selection and parameter optimization significantly improves. The findings reveal Internet big data and geopolitical risks as key features of carbon prices, complementing traditional indicators such as energy prices, economy, and climate, which exhibit lagged effects, regional heterogeneity, and interaction. These findings deepen our understanding of carbon price formation mechanisms and bolster managers’ ability to utilize artificial intelligence for effective decision-making, thereby supporting the achievement of the SDGs.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103281"},"PeriodicalIF":6.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166848","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":"Intelligent forecasting and distribution in cross-border e-commerce import trade: A deep-learning-based iterative optimization approach","authors":"Xuhui Chen ,&nbsp;Yong He ,&nbsp;Golnaz Hooshmand Pakdel ,&nbsp;Chung-Hsing Yeh","doi":"10.1016/j.omega.2025.103277","DOIUrl":"10.1016/j.omega.2025.103277","url":null,"abstract":"<div><div>The dramatic growth of cross-border e-commerce (CBEC) trade promotes the vigorous development of bonded warehouses, providing overseas suppliers with an opportunity to lay out distribution networks to meet the domestic consumers’ growing logistics efficiency requirement. This paper considers the distribution network design problem with iteratively updated demand. Specifically, we first construct a hybrid deep learning model, which integrates a convolutional neural network for extracting recessive features and long short-term memory for a retrograde time extension to forecast the consumers’ demand. Then, a mixed integer linear programming (MILP) model is developed to formulate the distribution network design, which aims to rent the appropriate storage capacities of some warehouses in different locations and make the product allocation plans with minimum operation cost. The Benders decomposition algorithm is appropriately adopted as the solution approach to the proposed model. When the warehouse locations and distribution plan are initially developed, the logistics timeliness of some destinations will be improved, potentially leading to a redistribution of consumers’ demand. Therefore, we integrate the prediction and MILP models to construct a forecasting-distribution iterative optimization process to explore the optimal solution dynamically. A real case study is used to verify the effectiveness of the proposed integrated approach. Our research formulates characteristic distribution network design solution for overseas suppliers engaged in CBEC import trade, providing valuable insight to achieve an iterative optimization process through organically linking deep-learning-based forecasting and optimization processes.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103277"},"PeriodicalIF":6.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166787","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":"Robust doctor–patient assignment with endogenous service duration uncertainty and no-show behavior","authors":"Menglei Ji ,&nbsp;Shanshan Wang ,&nbsp;Chun Peng ,&nbsp;Jinlin Li","doi":"10.1016/j.omega.2025.103279","DOIUrl":"10.1016/j.omega.2025.103279","url":null,"abstract":"<div><div>In practice, patient’s service duration might be influenced by the workload of doctors who need to provide healthcare service. However, most existing studies have overlooked this correlation when scheduling patients and doctors. Motivated by this context, we incorporate the endogenous (decision-dependent) uncertain service duration, the presence of uncertainty dependent on assignment decisions, into the doctor–patient assignment problem with patient no-show behavior and propose a novel modeling framework. Specifically, we employ the distributionally robust optimization (DRO) approach that uses decision-dependent moment information to construct the ambiguity set of the service duration distribution. A novel decision-dependent DRO (DDRO) model is proposed for the doctor–patient assignment problem. The goal is to minimize the sum of the doctor’s assignment cost and penalty cost and the worst-case expected cost of overtime and cancellation cost. To solve this model, we propose an effective nested column-and-constraint generation (C&amp;CG) solution scheme. This approach involves decomposing the model into an outer-level problem and an inner-level problem, both of which can be solved using the C&amp;CG algorithm. This nested scheme enables us to efficiently solve the model and obtain optimal solutions. Numerical results show that the algorithm can solve most realistic-sized problem instances optimally within the two-hour time limit. In addition, to show the effectiveness of our new modeling framework, we also propose the classical DRO and stochastic programming (SP) models as the benchmark models in our out-of-sample test. The extensive numerical results show that when there exists variability in service duration and robustness in the ambiguity set, our DDRO model outperforms the DRO and SP approaches. In addition, when there are relatively enough doctors, the DDRO method is the best option for decision-makers to make assignment plans. We also show that the no-show behavior factor has a large effect on each model, and the decision-maker cannot ignore the factor, especially when the no-show rate is high. Overall, the numerical results demonstrate the importance of taking decision-dependent service duration uncertainty into account for the doctor–patient assignment problem and also provide an alternative modeling tool for healthcare managers to make assignment plans in scenarios where the service duration is influenced by doctors’ assignments.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103279"},"PeriodicalIF":6.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166778","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":"A novel O2O service recommendation method based on dynamic preference similarity","authors":"Lu Xu ,&nbsp;Yuchen Pan ,&nbsp;Desheng Wu ,&nbsp;David L. Olson","doi":"10.1016/j.omega.2025.103278","DOIUrl":"10.1016/j.omega.2025.103278","url":null,"abstract":"<div><div>Recent technological advancements have enabled an increasing number of consumers to select services from online platforms and utilize them in offline stores, a model known as online-to-offline (O2O) e-commerce. This emerging model has garnered significant attention from both business and academic communities. However, with the rapid growth of O2O services, consumers face challenges in selecting services that align with their preferences from a vast array of options. To address this issue, this paper proposes a novel O2O service recommendation method based on dynamic similarity estimation (ReDPS). The dynamic similarity is calculated by tracking changes in consumer preferences over time, providing a more accurate and robust measure of consumer relationships. We validate the ReDPS method using both the Dianping dataset and the publicly available Yelp dataset. Experimental results show that: 1) ReDPS significantly outperforms classical and state-of-the-art recommendation methods, with its effectiveness improving over longer time spans of consumer feature data. 2) Consumer preferences are more strongly influenced by variations in service categories and geographical locations over time than by changes in service evaluations, though all factors are important, and consumers of the same gender tend to exhibit similar preferences. 3) Optimal parameter configurations for ReDPS are identified through the experiments.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103278"},"PeriodicalIF":6.7,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166782","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}