Omega-international Journal of Management Science最新文献

{"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}

{"title":"Bilevel portfolio optimization with ordered pricing models","authors":"Stefano Benati ,&nbsp;Marina Leal ,&nbsp;Justo Puerto","doi":"10.1016/j.omega.2024.103274","DOIUrl":"10.1016/j.omega.2024.103274","url":null,"abstract":"<div><div>Pricing problems include, among others, those of determining prices to charge for some services or products taking into account the customers’ reaction to such prices, as they could modify their purchases to avoid costs. Here we consider a pricing problem in the financial industry, namely, between the broker and the investor, as the former fixes the transaction costs on assets that are purchased by the latter. We assume that the broker defines a pricing policy based on ordering its fees and, depending on their amount, the investor either fully or partially owes fees. As a result, the function describing the total transaction costs is the ordered median and, to maximize its profit, the broker must determine the optimal weights of that function. In the model formulation, the broker must take into account investor reaction, resulting in a bilevel optimization model. In this model, the first level is the broker’s optimal choice that is followed in the second level by the investor optimal choice. We show how to formulate and solve the bilevel problem assuming two different sets of possible prices: discrete and continuous. In the former case, the nonlinear formulation is reduced into a mixed-integer linear model, while in the latter case, the resulting model remains as a mixed-integer nonlinear model. The solution obtained allows us to suggest some managerial insights of pricing policies, as broker profits and investor reaction to prices can be predicted and described. We will see that there is a trade-off between broker profits and investor risk, as pricing policies favorable to the broker increases the financial risks of the investor. This suggests the broker must exercise caution when implementing these policies, if investor gains and losses are of concern to the broker.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103274"},"PeriodicalIF":6.7,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166779","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":"Optimal cause marketing strategies for online platforms and third-party sellers: A spokes model analysis","authors":"Qian Su ,&nbsp;Xiaoyang Zhou ,&nbsp;Qi Liu ,&nbsp;Kai Zhang","doi":"10.1016/j.omega.2024.103271","DOIUrl":"10.1016/j.omega.2024.103271","url":null,"abstract":"<div><div>Under the advocacy of the platform, an increasing number of third-party sellers have implemented cause marketing (CM) strategies. The platform and sellers have achieved cumulative social benefits while pursuing profit. However, not all third-party sellers choose the CM strategy. To better understand the impact of online CM on pricing, profitability, and social welfare, we set up a stylized CM spokes model and analyze the impact of the unit donation amount, the number of cause marketing sellers, and consumers’ warm-glow effect on the optimal CM strategy. We find that the profitability of cause-related products is not always higher than that of non cause-related products when there exists sufficient competition between cause-related and non cause-related products. It is also found that higher unit donation amounts do not necessarily lead to greater total donations and social welfare. Moreover, having more sellers choose CM does not necessarily result in a higher total donation amount. Additionally, we explore another donation form, the donation rate, to determine the best CM strategy for sellers. The results show that a fixed donation is more flexible for sellers. Overall, our findings offer new insights into how platforms and sellers can optimally implement CM strategies.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103271"},"PeriodicalIF":6.7,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166833","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":"Maintaining E-commerce supply chain viability: Addressing supply risks with a strategic live-streaming channel","authors":"Qingying Li ,&nbsp;Shuo Zhu ,&nbsp;Tsan-Ming Choi ,&nbsp;Bin Shen","doi":"10.1016/j.omega.2025.103276","DOIUrl":"10.1016/j.omega.2025.103276","url":null,"abstract":"<div><div>Under the recent COVID-19 pandemic and operational disruption, we have witnessed the importance of maintaining supply chain viability. Exploring the channel effect (e.g., the use of innovative sales channels) in addressing supply risks is essential to understanding the viability of e-commerce supply chains. Motivated by the emergence of live-streaming channels in the “new normal”, in this study, we consider an e-commerce supply chain facing supply risks. Capturing supply risks by a random supply viability, we investigate the channel decision on whether to introduce live streaming and the platform's optimal selling format selection (between reselling and marketplace). We prove that live streaming can help address supply risks because it can utilize the streamer's sales effort to impact the live-streaming retail price and create more flexibility. We confirm that a sufficiently high live-streaming efficiency is crucial for introducing a live-streaming channel. Interestingly, we uncover that high volatility of supply risks incentivizes the adoption of live streaming. We also find that incorporating live streaming may change the e-tailer's selling format preferences because the reselling format exhibits a higher acceptance of live streaming than the marketplace one, which is further influenced by the combined influence of live streaming and supply risks. Our results imply that live streaming plays a positive role in maintaining supply chain viability by enhancing the supply chain's overall ability to address supply risks. This paper provides scientific evidence on the importance of having a live-streaming channel in addressing supply risks in the new normal.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103276"},"PeriodicalIF":6.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166850","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 an iterative procedure of maximum likelihood estimations to solve the newsvendor problem with censored demand","authors":"Johan Bjerre Bach Clausen ,&nbsp;Christian Larsen","doi":"10.1016/j.omega.2024.103273","DOIUrl":"10.1016/j.omega.2024.103273","url":null,"abstract":"<div><div>This paper proposes a new data-driven solution approach for solving a newsvendor problem, where the parameters of the demand distribution are unknown and only sales (censored demand) can be observed. The procedure can be applied to different demand distributions. Compared to the previous parametric literature our approach allows the value at which demand is censored to vary, and we design an iterative solution procedure where the newsvendor updates their order size when new sales data is observed. The core of the procedure is an estimation phase where the newsvendor finds an optimal order size, using a novel maximum likelihood approach, which explicitly incorporates censored data. Moreover, the maximum likelihood part of the procedure is not specific to the newsvendor problem, and can therefore be used to solve other inventory management problems in future research or practice. In this paper, we explore numerically both the negative binomial distribution and the Poisson distribution, and we show that our log-likelihood function is concave for the Poisson distribution. In our comprehensive numerical experiments, we show that the procedure generally arrives at the optimal order size in short sales seasons with 25 to 100 periods. Moreover, by the 100th period the 25% and 75% quantiles of our experimental data are close to the optimal order size. We also introduce and discuss the regret of the algorithm and compare the algorithm to algorithms designed to minimize regret.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103273"},"PeriodicalIF":6.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166816","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":"Clearance sale or not? Benefits from multi-behavioral factors of strategic customers","authors":"Yanan Song ,&nbsp;Dong Xie ,&nbsp;Wei Gu","doi":"10.1016/j.omega.2024.103265","DOIUrl":"10.1016/j.omega.2024.103265","url":null,"abstract":"<div><div>We consider a seller’s decision problem when facing a stochastic number of customers who are strategic. The seller first determines a strategy of either clearance sale or fixed price, and then stocks the corresponding quantity of products to maximize profit. Because customers potentially face a shortage if they wait for a discount, behavioral factors arise. Derived from prospect theory, risk aversion, reference point, and loss aversion are considered to capture the behavior of strategic customers. We investigate the behavioral impacts and find that customers’ loss aversion always benefits the seller who chooses a clearance sale strategy, but risk aversion does not. The seller should also consider the cost of the product (or profit margin) to choose either a clearance sale or fixed-price. From the result of numerical analysis, the potential profit improvement from considering behavioral factors can be as high as 18.55% and is affected by the product cost and the discount rate. Furthermore, we point out that inaccurately estimating the behavioral factors may result in great profit losses, but when the product cost is high or the product cost is moderate with a high discount rate, a seller need not worry about the inaccuracy because it causes limited profit consequences.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103265"},"PeriodicalIF":6.7,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166780","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 decision making and risk management in stock index futures markets under the influence of global geopolitical volatility","authors":"Jie Gao ,&nbsp;Chunguo Fan ,&nbsp;Liang Xu ,&nbsp;Hongni Chen ,&nbsp;Hangyu Chen ,&nbsp;Zhilei Liang","doi":"10.1016/j.omega.2024.103272","DOIUrl":"10.1016/j.omega.2024.103272","url":null,"abstract":"<div><div>In the context of escalating global geopolitical turmoil, geopolitical risks have increasingly significant impacts on financial markets, particularly intensifying market volatility in China's stock market, which is dominated by individual investors. These risks present substantial challenges for investors and policymakers. Existing research often treats the stock market as a static entity, lacking integration with quantitative decision-making, and relies on traditional methods that may not capture the complexities of market dynamics. This study aims to innovate trading strategies and risk management methods in the stock index futures market to effectively respond to the unknown risks brought about by geopolitical fluctuations. Firstly, we propose an innovative data-driven market state division strategy. By analyzing market data to quantitatively derive cyclical parameters of market states, we effectively reduce the market risks that may arise from subjective choices inherent in traditional methods. Secondly, we design a real-time trading system that combines the Geopolitical Risk Index with commonly used trend and oscillation indicators in the stock market. This system can identify and adapt to the market's changing trends, achieving precise grasp of market dynamics and flexible application of trading strategies. Additionally, we extend the traditional one-dimensional time trend analysis to a multidimensional data-driven perspective by utilizing Convolutional Neural Networks to automatically identify more diverse market features. To enhance the training effectiveness, generalization ability, and robustness of deep learning models, we introduce image augmentation strategies. By repeatedly emphasizing specific features without increasing training complexity, we enhance the model's ability to learn high-level representations, significantly improving overall performance. Through these innovative methods, this study not only deepens the understanding of the relationship between geopolitical risks and financial market dynamics but also provides more precise and scientific data support for financial market decision-making. It lays a solid foundation for the development of risk management and trading strategies in future high-volatility environments.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103272"},"PeriodicalIF":6.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166835","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":"Linking outcomes to costs: A unified measure to advance value-based healthcare","authors":"Joke Borzée ,&nbsp;Brecht Cardoen ,&nbsp;Laurens Cherchye ,&nbsp;Bram De Rock ,&nbsp;Filip Roodhooft","doi":"10.1016/j.omega.2024.103270","DOIUrl":"10.1016/j.omega.2024.103270","url":null,"abstract":"<div><div>Guided by the Value-Based Healthcare framework, the healthcare sector increasingly aims to maximize patient value by improving the quality of care while containing costs, which requires aligning the interests of patients, health providers and payers. This study addresses the need for advanced patient value measurement techniques to navigate this complex balance by introducing a four-step framework that combines Data Envelopment Analysis (DEA) and Time-Driven Activity-Based Costing. The framework starts by defining Decision-Making Units and specifying the treatment pathway (Step 1), followed by selecting the relevant inputs (i.e., costs) and outputs (i.e., health outcomes) (Step 2). Next, the DEA model is tailored to fit the specific medical context (Step 3), ultimately translating the value equation into unified, individual value scores that rank patients by perceived value (Step 4). Unlike traditional healthcare evaluations, the multiple health outcomes are connected to granular costing information without relying on monetary values or subjective weighting. Using real-life data from a case study focused on psoriasis, we demonstrate that value assessments significantly differ when considering a comprehensive set of health outcomes, rather than relying on a single primary outcome or treating costs and outcomes separately. These holistic value scores are used to pinpoint inefficiencies on an individual level, analyse patterns of health improvements through cluster analysis, and assess the impact of contextual variables on value creation using econometric analysis. Our results revealed the complex interplay between outcomes and costs by identifying factors like the presence of comorbidities, which had no direct influence on costs or outcomes, as overall value driver. In summary, this research proposes an intuitive metric for value benchmarks across time, health providers and treatments, ultimately contributing to the effective delivery of personalized and value-based healthcare.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103270"},"PeriodicalIF":6.7,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166814","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}