Omega-international Journal of Management Science最新文献

{"title":"Robust framework for the joint learning of consumer preferences and market segmentation","authors":"Shobeir Amirnequiee ,&nbsp;Joe Naoum-Sawaya ,&nbsp;Hubert Pun","doi":"10.1016/j.omega.2025.103410","DOIUrl":"10.1016/j.omega.2025.103410","url":null,"abstract":"<div><div>Learning consumer preferences is essential to maximize profits. To optimize the product line, accurately segmenting the market and eliciting consumer preferences in each segment are critically important. We present a robust framework to simultaneously segment the customer base and learn each segment’s preferences. We build upon ideas from machine learning and mathematical programming and propose a robust preference elicitation model. Our model accounts for robustness against feature noise (i.e., perturbations caused by consumers inaccurately comparing alternatives), and handles label noise (i.e., inconsistent consumer choices) using a weighting scheme that determines the relevance of the past choices in predicting future ones. The proposed framework has three appealing characteristics. First, it simultaneously segments the market and learns the segments’ preferences. Second, it extends an ML-based preference learning method that has been proven to be effective. Third, the decision maker can choose the level of robustness and has the option to focus on the parsimony of the solution. We perform extensive experiments and show that the proposed framework offers better prediction accuracy and lower variability in the predictions.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103410"},"PeriodicalIF":7.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891980","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":"Values of private traffic for community group-buying platform: A game-theoretic model of subsidy strategy choice","authors":"Lingli Shu,&nbsp;Xuedong Liang,&nbsp;Pengkun Wu","doi":"10.1016/j.omega.2025.103414","DOIUrl":"10.1016/j.omega.2025.103414","url":null,"abstract":"<div><div>Community leader, by leveraging highly sticky private traffic, has become crucial force in the market expansion of community group-buying platforms. Subsidizing community leader’s private traffic can effectively enhance the conversion rate from traffic to orders. Prior research has predominantly focused on platform subsidy strategies for public traffic, with limited attention to private traffic, particularly in comparative analyses of subsidies for both types of traffic. By adopting a unique perspective on the value of private traffic, we develop a game-theoretic model to investigate how private traffic influences the operational strategies of community group-buying platforms, with particular emphasis on new entrant. Our findings reveal that an entry platform tends to subsidize the user group with greater traffic upon market entry. Although such subsidies incur higher subsidy costs, the benefits are more substantial. Under both subsidy strategies, the incumbent platform generally responds by lowering prices, resulting in a decline in its profits. Notably, other market parameters, such as the maximum changing cost, exert asymmetric positive or negative moderating effects on the value of private traffic, collectively influencing platform market performance and profitability. These results provide novel insights into future research on private traffic and the market entry of community group-buying platforms.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103414"},"PeriodicalIF":7.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886011","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":"Two-Stage robust optimization approach for supplier selection, development, and order allocation under uncertainty","authors":"Guyu Dai ,&nbsp;Xicai Zhang ,&nbsp;Ren-Qian Zhang","doi":"10.1016/j.omega.2025.103413","DOIUrl":"10.1016/j.omega.2025.103413","url":null,"abstract":"<div><div>Effective supplier management in competitive markets involves more than selecting the most capable suppliers. It requires a comprehensive approach that combines supplier selection with long-term development efforts to ensure competitive advantages. However, traditional models often emphasize short-term efficiency and overlook supplier development as a critical strategy for achieving sustained performance improvement. This study proposes a novel two-stage robust optimization framework that unifies strategic and operational decisions to address this limitation. Different from traditional approaches, the proposed model strategically integrates supplier development programs (SDPs) with supplier selection in the first stage to minimize total costs, followed by order allocation in the second stage under the realization of worst scenarios. To capture the inherent uncertainty associated with supplier development, a budgeted uncertainty set is constructed to characterize potential variations in performance improvement. Methodologically, a multi-cut version of the column-and-constraint generation (C&amp;CG) algorithm is developed to accelerate convergence by generating multiple cutting planes per iteration and linearizing nonconvex dual subproblems. Numerical experiments verify the computational advantages of the proposed algorithm, which achieves superior efficiency in solving large-scale instances compared to the standard C&amp;CG algorithm. Furthermore, a procurement case study is conducted to demonstrate the practical applicability of the proposed model. The findings reveal a nonlinear relationship between uncertainty and supplier development, and highlight significant differences in management strategies for domestic and foreign suppliers.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103413"},"PeriodicalIF":7.2,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864696","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":"Unleashing resilience through digitalization: Do upstream and downstream firms differ?","authors":"Zishun Qian ,&nbsp;Tingting Yan ,&nbsp;Jianbin Li ,&nbsp;Yicheng Zhang","doi":"10.1016/j.omega.2025.103411","DOIUrl":"10.1016/j.omega.2025.103411","url":null,"abstract":"<div><div>Does digitalization enable a firm to gain resilience after a broad, large-scale supply chain disruption? The literature is not conclusive about this question. Furthermore, the mechanisms through which digitalization affects firm resilience and whether these mechanisms vary between upstream and downstream firms along a supply chain remain unclear. Drawing on resource orchestration theory, this study examines how upstream and downstream firms differ in supply chain reconfiguration strategies, enabled by digitalization, for increasing resilience. We construct a multisource secondary data set on 3043 firms to test the hypotheses. Using the COVID-19 pandemic as the empirical context, this study shows that digitalization enhances the resilience of upstream firms primarily through customer concentration growth, while for downstream firms, supplier concentration reduction is the main mechanism. Theoretically, these findings provide a resource orchestration rationale for understanding differences between upstream and downstream firms in supply chain resource reconfiguration strategies for enhanced resilience after a broad, large-scale disruption. These findings also provide practical guidance to enable firms to exploit the resilience-enhancing power of digitalization better.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103411"},"PeriodicalIF":7.2,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908808","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":"Advance selling in the presence of valuation uncertainty and demand correlation","authors":"Jianhong Li ,&nbsp;Chen Hu ,&nbsp;Mingzhuo Dai ,&nbsp;Wenjing Shen ,&nbsp;Zhiyuan Chen","doi":"10.1016/j.omega.2025.103407","DOIUrl":"10.1016/j.omega.2025.103407","url":null,"abstract":"<div><div>We consider a problem where a seller presells a product and the demands in the advance and spot periods are stochastic and correlated. There are two types of consumers, informed and uninformed consumers, in the market, depending on their arrival time. Informed consumers who arrive in the advance period are uncertain about their valuation of the product, and the uncertainty will be resolved until the spot period. Considering the correlation between the two types of consumers, by offering a proper advance strategy, the seller can use the advance order information to update his forecast for the spot-period demand and make better inventory decisions accordingly. In this paper, we consider two preorder strategies: the preorder strategy with a price guarantee and the one without a price guarantee. We investigate the seller’s optimal advance and spot pricing decisions under the two strategies and investigate the impact of consumers’ valuation uncertainty on the seller’s decisions. We also conduct numerical experiments to show that the seller should offer a price guarantee when the informed demand uncertainty is large.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103407"},"PeriodicalIF":7.2,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864695","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-period fourth-party logistics network design with promised service time and regret behavior","authors":"Yuxin Zhang ,&nbsp;Min Huang ,&nbsp;Zhiguang Cao ,&nbsp;Xingwei Wang ,&nbsp;Zhiqi Shen ,&nbsp;Jie Zhang","doi":"10.1016/j.omega.2025.103400","DOIUrl":"10.1016/j.omega.2025.103400","url":null,"abstract":"<div><div>Promised service time and regret behavior arising from deviations between promised and actual performance significantly influence fourth-party logistics (4PL) network design. This paper proposes a novel multi-period 4PL network design problem incorporating the promised service time decision and decision-makers’ regret behavior. First, promised service time ranges are determined by predicting transportation times of third-party logistics providers, enabling cost-effective promises to customers. A mixed integer non-linear programming model is formulated to maximize profit by characterizing the decision-makers’ regret behavior through regret theory. Second, an equivalent reformulation model is developed and solved using the exact solver CPLEX, efficiently addressing small and medium-scale regional networks. Moreover, a Q-learning based collaborative hyper-heuristic with global and local-spaces classification (QLCHH-GLSC) algorithm framework is proposed, ensuring suitability for larger-scale networks. Specifically, local search spaces are dynamically classified based on the solution obtained from the construction heuristic selected by global-driven Q-learning. Subsequently, local-driven Q-learning is designed to select the most suitable perturbation heuristic for each individual within these spaces. Finally, the effectiveness and efficiency of the proposed algorithm are demonstrated through numerical results compared to CPLEX and commonly used methods. Furthermore, some managerial insights are provided for 4PL managers. Strategically deciding on promised service time while considering regret behavior can enhance both service punctuality and profitability. Interestingly, in markets with a low impact from service deviations, regret-averse decisions driven by high-level regret ensure service quality and long-term profitability, while in high-impact markets, excessive conservatism will lead to profit losses without significantly improving punctuality.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103400"},"PeriodicalIF":7.2,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858283","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":"Battery-leasing strategies for electric vehicles considering a performance guarantee","authors":"Wei Xie ,&nbsp;Guoxin Han ,&nbsp;Yuanguang Zhong","doi":"10.1016/j.omega.2025.103398","DOIUrl":"10.1016/j.omega.2025.103398","url":null,"abstract":"<div><div>Electric vehicles (EVs) have gained significant popularity due to their eco-friendly benefits. However, EV batteries are capital-intensive and their performance deteriorates with usage, which undermines their market competitiveness compared to gas-powered cars. To address this issue, EV manufacturers have introduced battery-leasing services (BLS) to reduce the upfront cost for consumers and have provided a performance-guaranteed warranty policy (PGWP). Nevertheless, the high upfront production costs and maintenance expenses associated with PGWP impose a financial burden and risk on manufacturers. Therefore, based on real-world cases, this study introduces a battery asset company (BAC) to explore related pricing strategies for providing BLSs under PGWP and to examine the impact of various collaboration models with BAC on manufacturers. The findings reveal that the relationship between the total discount factors of consumers and firms significantly influences the pricing strategies of EV bodies and BLS. When consumers’ total discount factor is relatively low, the “razor-blade effect” emerges. Furthermore, we identify that the cost advantages of BAC are the primary drivers of manufacturers’ collaboration strategies. Interestingly, although the collaboration model where manufacturers sell batteries to BAC while retaining BLS operational responsibilities is theoretically the least favorable option, it can yield the highest short-term returns and cash flow protection when battery maintenance costs are relatively high. Additionally, under cost-revenue ratio constraints, this collaboration model can also effectively alleviate manufacturers’ financial pressure, enabling them to achieve maximum profitability.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103398"},"PeriodicalIF":7.2,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860874","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 predict-and-prescribe framework for dynamic course scheduling toward strategic university scaling","authors":"Özge Aygül ,&nbsp;Teodor Hellgren ,&nbsp;Shima Azizi ,&nbsp;Andrew C. Trapp","doi":"10.1016/j.omega.2025.103406","DOIUrl":"10.1016/j.omega.2025.103406","url":null,"abstract":"<div><div>Universities play vital roles in educating current and future generations. Universities that intend to survive in competitive academic markets where there are evolving enrollment trends must responsibly manage resources. Planning processes for instructional spaces can benefit from optimization and lead to more effective resource utilization, yet the use of trends in major and course demands to inform long-term planning remains largely unexplored. We propose a novel mathematical optimization framework that appears to be the first to use trend predictions to guide long-term dynamic course scheduling decisions and effective resource allocation. We begin with a baseline formulation that assigns course sections to time patterns and classroom spaces while considering instructor preferences, as well as constraints related to conflicts and capacity. We then extend this to a dynamic formulation that addresses bottleneck courses while prioritizing classroom utilization and honoring instructor preferences. Our dynamic formulation optimizes instructional space allocation for an academic period over the entire university, and solving for sequential independent academic periods reveals valuable insights into the efficiency of physical resource utilization on the horizon. Our framework is flexible, accommodating the objectives of faculty, schedulers, and administrators through a hierarchical multi-objective approach that integrates these diverse priorities. Our formulation addresses 76% and 81% of bottleneck sections for back-to-back semesters, with a substantial number of unutilized locations that could potentially be repurposed to accommodate the remaining bottleneck sections or other purposes. Through extensive experiments with varying university enrollment scenarios, we examine the resulting tradeoffs among objectives and highlight a variety of implications.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103406"},"PeriodicalIF":7.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828149","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 performance evaluation and improvement: Opening the black-box of weight space","authors":"Xiyang Lei ,&nbsp;Weiyan Qiu ,&nbsp;Jianping Li ,&nbsp;Qianzhi Dai","doi":"10.1016/j.omega.2025.103409","DOIUrl":"10.1016/j.omega.2025.103409","url":null,"abstract":"<div><div>Data envelopment analysis (DEA) may fall into the extreme weights during the performance evaluation process. To obtain robust evaluation results, a possible way is opening the black-box of weight space. In this scenario, there are two main challenges: i) how to ensure the repeatability of performance evaluation, and ii) how to find the optimal performance improvement path. In this paper, we first propose a grid generation algorithm that can sample the input-output weights non-randomly, which means the sampling result can be reproducible. Based on this, we can approach the efficiency ratio distribution curve of each decision-making unit (DMU), which reveals more detailed insights into efficiency assessment. To describe the evaluation result, we replace the concept of efficiency dominance by dominance efficiency probability (DEP), and analyze the ranking interval by employing ranking expectation and ranking variance. Several important properties are further proved. Moreover, we propose a step-by-step performance improvement algorithm based on the principle of efficiency elasticity maximization. Finally, we illustrate the availability of our method by a real-world case study. The main contributions include that i) we propose a weight sampling algorithm to open the black-box of weight space, which can ensure the evaluation result is reproducible; ii) we extend the performance evaluation indicator to the framework of DEP, ranking expectation and ranking variance, which can provide evaluation information for decision makers more comprehensively and robustly; iii) we propose a performance improvement algorithm from the new perspective of efficiency elasticity.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103409"},"PeriodicalIF":7.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842416","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 multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty","authors":"Babooshka Shavazipour ,&nbsp;Theodor J. Stewart","doi":"10.1016/j.omega.2025.103405","DOIUrl":"10.1016/j.omega.2025.103405","url":null,"abstract":"<div><div>Many real-world decision-making problems involve multiple decision-making stages and various objectives. Besides, most decisions need to be made before having complete knowledge about all aspects of the problem, leaving some sort of uncertainty. Deep uncertainty happens when the degree of uncertainty is so high that the probability distributions are not confidently knowable. In this situation, using wrong probability distributions leads to failure. Scenarios, instead, should be used to evaluate the consequences of any decisions in different plausible futures and find a robust solution. In this study, we proposed a novel multi-stage multi-scenario multi-objective optimisation framework for adaptive/dynamic robust decision-making under deep uncertainty using a more flexible definition of robustness by incorporating the risk attitude of the decision-makers. In this definition, a robust decision is one that performs relatively well (acceptable) in a broad range of scenarios. Two approaches, named multi-stage multi-scenario multi-objective and two-stage moving horizon, have been proposed and compared. Finally, the proposed approaches are applied in a case study of sequential portfolio selection under deep uncertainty, and the robustness of their solutions is discussed.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"138 ","pages":"Article 103405"},"PeriodicalIF":7.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772001","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}