Omega-international Journal of Management Science最新文献

{"title":"Behavior-Based Pricing strategy of quality-differentiated products with imperfect customer recognition capability","authors":"Tao Jiang ,&nbsp;Kaigeng Shen ,&nbsp;Youwei Guo ,&nbsp;Lei Guan","doi":"10.1016/j.omega.2025.103488","DOIUrl":"10.1016/j.omega.2025.103488","url":null,"abstract":"<div><div>The development of data technology has enabled firms to identify and analyze consumers’ purchase history and classify them into new and old customers for price discrimination. However, this type of price discrimination tends to cause customer dissatisfaction and lead to resistance against firms’ data-driven pricing practices. Consequently, it challenges firms’ customer recognition capability. This paper develops a two-period pricing model in which two firms, differing in product quality, investigate the impact of behavior-based pricing (BBP) on firms with varying quality levels when they possess imperfect customer recognition capability. We find, first, that imperfect customer recognition capability causes different effects on price in each period for firms of different quality, depending on the level of product quality differentiation. Second, quality advantages do not always lead to more markets for high-quality firms; low-quality firms are also in a position to gain more markets. Third, due to firms’ customer recognition capability and quality differentiation, when both firms adopt BBP, they will both achieve higher profits and reach a ”win–win” situation. Conversely, if only one firm adopts BBP, it will result in reduced profitability for both firms, leading to a ”lose–lose” scenario. Finally, we have examined consumer surplus and social welfare, and the findings provide a theoretical foundation and policy recommendations for governments to develop regulatory measures against price discrimination in the digital economy era.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103488"},"PeriodicalIF":7.2,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884133","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-objective optimization with order acceptance for the cumulative job shop scheduling problem in agribusiness","authors":"Florian Linß ,&nbsp;Mike Hewitt ,&nbsp;Janis S. Neufeld ,&nbsp;Udo Buscher","doi":"10.1016/j.omega.2025.103504","DOIUrl":"10.1016/j.omega.2025.103504","url":null,"abstract":"<div><div>Developing new crop species is crucial for addressing global food challenges and improving agricultural efficiency. In agribusiness, this process involves systematically growing and assessing numerous crop variants under controlled conditions to determine their yield potential and adaptability. Formally, this is a job shop scheduling problem because the crops can be understood as jobs that may have different processing sequences on the resources (e.g., greenhouses). However, since the resources can process several jobs simultaneously, a cumulative job shop problem arises. The primary objective is to maximize the number of accepted jobs from a job pool with given release and due dates. The secondary objective is to minimize delays in job processing, i.e., the jobs’ waiting times, as earlier completion of jobs allows for faster feedback and refinement of future crop variants, ultimately improving the overall testing and development process. In this paper, we formulate this problem as a mixed integer and constraint programming problem. We also show how it can be solved with a flexible hierarchical approach, even for very large problem instances. Comprehensive computational experiments first show that available machine capacity has a greater influence on the objectives than the length of the processing time windows, resulting from the difference between the due and release dates. Secondly, a deviation from the maximum number of accepted jobs disproportionately reduces delays.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103504"},"PeriodicalIF":7.2,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884134","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":"Feature-based profitability evaluation for newsvendor-type products","authors":"Yuying Zhang,&nbsp;Shiming Deng,&nbsp;Wanpeng Wang","doi":"10.1016/j.omega.2025.103508","DOIUrl":"10.1016/j.omega.2025.103508","url":null,"abstract":"<div><div>We study how firms selling newsvendor-type products determine order quantities to maximize the probability of achieving a target profit, referred to as profitability. Unlike existing literature, we assume decision-makers have access to historical demand data and related feature data. To integrate feature information into the optimization model, we propose a weighted sample average approximation method that resolves the inherent inconsistency of traditional SAA approaches. This feature-based model is reformulated as a mixed integer programming for efficient solution. We further prove the consistency and asymptotic optimality of the ordering policy derived from our method. For high-dimensional feature settings with irrelevant features, we develop a decision-based feature selection method within the nonparametric optimization framework. Additionally, we introduce a nonparametric bootstrap method to estimate conservative profitability, mitigating overestimation risks caused by sampling errors. Numerical experiments using both synthetic and real data are conducted to demonstrate the effectiveness of our proposed methods. Notably, as the sample size increases, our feature selection method consistently identifies all relevant features, meaning the probability of correctly selecting the model approaches 1. Furthermore, in real-data experiments, our feature-based method improves profitability by more than 50% compared to the SAA method.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103508"},"PeriodicalIF":7.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884131","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":"The stochastic production routing problem with adaptive routing and service level constraints","authors":"Ali Kermani,&nbsp;Jean-François Cordeau,&nbsp;Raf Jans","doi":"10.1016/j.omega.2025.103496","DOIUrl":"10.1016/j.omega.2025.103496","url":null,"abstract":"<div><div>Demand uncertainty poses a challenge to most companies in manufacturing and services as it can lead to significant profit losses if not addressed properly. To deal with this risk, companies may adopt specific service level targets to satisfy at least a certain proportion of their demand while considering operational constraints and minimizing the total cost. In this study we address the stochastic production routing problem (PRP) with adaptive routing and service level constraints. The PRP unifies the production, inventory and routing decisions into an integrated problem aimed at improving coordination across different parts of the system. We consider four different types of service levels, where each type uses a specific metric based on assumptions aligning with the needs of the company. These metrics encompass aspects such as the occurrence of stockouts or allowed ratios of backlogs or backorders to average demand. A two-stage stochastic formulation is proposed for each type of service level. Setup decisions are made in the first stage, and production, inventory, and routing decisions are adapted after demand realization. Considering routing decisions in the second stage increases flexibility while lowering overall costs. However, the resulting optimization problem is more challenging to solve than the case where routing decisions are made in the first stage. To address this issue, we introduce an iterative matheuristic algorithm designed to yield high-quality solutions within a reasonable computation time. The effectiveness of the proposed heuristic algorithm is demonstrated through extensive experiments, highlighting its potential to assist companies in managing demand uncertainty and enhancing operational efficiency.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103496"},"PeriodicalIF":7.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840660","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":"Ecosystem risk management: A MIP approach to spatial prioritization of multiple management actions","authors":"Matías Moreno-Faguett ,&nbsp;José Salgado-Rojas ,&nbsp;Virgilio Hermoso ,&nbsp;María José Martínez-Harms ,&nbsp;Bárbara Larraín-Barrios ,&nbsp;Eduardo Álvarez-Miranda","doi":"10.1016/j.omega.2025.103507","DOIUrl":"10.1016/j.omega.2025.103507","url":null,"abstract":"<div><div>Healthy ecosystems are essential for conserving biodiversity and supporting human well-being, yet human activities impose significant pressures, risking ecological features such as species, habitats and ecosystem services. While Systematic Conservation Planning (SCP) has been widely used to address these issues, traditional approaches often result in risk-averse solutions, focusing management efforts on low-impact sites and potentially leading to suboptimal conservation outcomes. To address this gap, we propose a mixed-integer programming (MIP) approach designed to explicitly reduce ecosystem risk by prioritizing cost-effective actions and sites to manage multiple stressors, while accounting for spatial needs such as connectivity and socio-political boundaries. Our framework integrates a state-of-the-art ecological risk assessment tool (InVEST-Habitat Risk Assessment) with an SCP-based mathematical programming tool (<span>prioriactions</span> <em>R</em> package) for guiding risk management. We applied this framework to Chilean Patagonia, using giant kelp (<em>Macrocystis pyrifera</em>) forests as a proxy for coastal ecosystems under pressure from stressors such as aquaculture, vessel activities, and ocean temperatures. The framework was assessed across five planning scenarios, each with different spatial needs, and was compared with traditional approaches. The results demonstrated that our approach consistently outperforms traditional ones, ensuring risk reduction in all scenarios. In key comparisons, the traditional approaches failed to exceed 47% of the risk reduction target. In addition to meeting higher risk reductions, our approach successfully overcame spatial needs in all scenarios. However, the inclusion of these constraints increased computational difficulty by 36 to 150 times and solution costs by up to 71%. These findings highlight the flexibility of the framework, but also emphasize the need for coordinated planning. This work aims to bridge the gap between risk assessment and risk management in conservation planning by explicitly incorporating risk as a primary objective instead of a secondary outcome. Our framework is applicable to any multi-stressor context, providing a flexible tool for designing cost-effective management strategies. By focusing on risk reduction and incorporating spatial needs, this approach enhances long-term ecosystem resilience.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"142 ","pages":"Article 103507"},"PeriodicalIF":7.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint scheduling policy for volunteers and materials in multi-organizational disaster response","authors":"Bo Feng,&nbsp;Qingchun Meng,&nbsp;Guodong Yu","doi":"10.1016/j.omega.2025.103505","DOIUrl":"10.1016/j.omega.2025.103505","url":null,"abstract":"<div><div>In disaster response, logistics management requires efficient matching among workforce, materials and tasks. Uncertain and non-stationary task arrivals, heterogeneity in workforce skill levels and uncertainty in task execution times, together with the need to coordinate materials with workforce deployment, jointly render the disaster response process highly stochastic; meanwhile, multi-organizational participation further introduces cross-organizational resource coordination challenges. Existing approaches struggle to address these jointly—stochastic demand, cross-organizational coordination, and inefficiencies from decoupled workforce–material scheduling—and often require frequent manual retuning that limits responsiveness and scale. We develop an adaptive, cross-organizational decision system that co-optimizes volunteer assignment, material allocation, and replenishment in real time. System evolution is governed by task arrivals, service completions, and inventory decay, while assignment, material allocation, and replenishment act directly on these drivers. We adopt a Markov Decision Process (MDP) framework to integrate multi-organizational collaboration, real-time resource management and task allocation, and implement an end-to-end controller via hierarchical deep reinforcement learning(HDRL) that jointly optimizes volunteer assignment, material allocation, and replenishment. Across varied demand regimes, scales, and perishability levels, the proposed joint controller consistently outperforms common queueing heuristics: task backlogs decrease by about 30–85% and personnel costs by 16–42%, while logistics and resource-usage costs remain broadly comparable, with occasional modest logistics increases that relieve congestion. Relative to short-horizon rolling dynamic programming, it achieves lower backlog and total cost with less manual re-tuning, millisecond-level inference latency, and smooth scaling.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103505"},"PeriodicalIF":7.2,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884132","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":"Group-and-cut approach for dynamic programming with Fréchet-distributed quantizers","authors":"Anna Timonina-Farkas","doi":"10.1016/j.omega.2025.103502","DOIUrl":"10.1016/j.omega.2025.103502","url":null,"abstract":"<div><div>Multi-stage stochastic optimization is a well-known quantitative tool applied in a wide variety of decision-making problems. In this article, we focus on generalized flood risk management problems with Fréchet distributions used to describe the uncertainty. Theoretical solutions of such problems can be found explicitly only in exceptional cases due to their variational form and interdependency of uncertainty in time, e.g., due to cascading impacts of extreme floods. Nevertheless, numerical methods based on Monte Carlo sampling are inaccurate, as the Law of Large Numbers must hold for sufficient approximation quality. To overcome this shortcoming, we introduce an approximation scheme that computes and groups together <em>optimal</em> quantizers of Fréchet distributions. The groups are distinguished by a particular risk threshold and differentiate between higher- and lower-impact floods. We consider optimality of quantization methods in the sense of the minimal Kantorovich–Wasserstein distance. Depending on the group, to which a quantizer belongs, and on the form of the optimization problem, we propose two dynamic programming schemes: with accelerated dynamics and with non-accelerated dynamics. For the accelerated method, the groups of quantizers are used to cut scenario trees and guarantee optimality gaps close to zero. For the non-accelerated method, the probabilities of quantizers are used to weight value functions and bound the approximation error with convergence guarantees. Global solution is guaranteed under convexity and monotonicity conditions on the value functions. Considering cases with and without circular economy indicators able to reduce <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions, we apply the methods we developed to the governmental budget allocation problem under flood risk in Austria.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103502"},"PeriodicalIF":7.2,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840661","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 shift scheduling with discretionary rest breaks","authors":"Sara Mattia ,&nbsp;Fabrizio Rossi ,&nbsp;Stefano Smriglio","doi":"10.1016/j.omega.2025.103500","DOIUrl":"10.1016/j.omega.2025.103500","url":null,"abstract":"<div><div>Shift scheduling is a critical task in workforce management, which consists of covering the staffing needs by feasible work shifts at minimum labor cost. Work shifts must include rest breaks according to national regulations and collective agreements, significantly impacting the expected Level of Service (LoS) provided to the customers. Therefore, managers tend to preserve the LoS by rigidly scheduling breaks at a centralized level. This affects the well-being of employees, who, especially in large organizations, require more discretion in choosing rest breaks to reduce work stress. This paper presents a two-stage robust optimization model with decision-dependent uncertainty to handle discretionary break assignment. This is a challenging problem both in theory and practice. We formulate it as a <em>bilevel program</em> and develop a tailored heuristic algorithm to find high-quality shift schedules for staffing patterns of several service systems. This methodology allows managers to assess the cost of discretion and to finely determine the best trade-off among discretion, LoS, and labor cost.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103500"},"PeriodicalIF":7.2,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788042","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 bi-objective sustainable EOQ model with all-units discounts","authors":"José M. Gutiérrez,&nbsp;Antonio Sedeño-Noda","doi":"10.1016/j.omega.2025.103503","DOIUrl":"10.1016/j.omega.2025.103503","url":null,"abstract":"<div><div>A bi-objective sustainable Economic Order Quantity (EOQ) model considering all-units discounts is addressed. Two criteria are considered in the model, namely, the classical cost function of the EOQ and a <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> emission function, which must be simultaneously minimized. Moreover, all-units discounts are taken into account since they are a very common practice in real world wholesalers/sellers businesses to encourage higher volume sales in order to reduce the inventory level. Therefore, the aim of the new model is to identify the non-dominated solution set (Pareto optimal set).</div><div>It will be shown that the Pareto optimal solution set for the new sustainable EOQ variant is not always a convex set (or equivalently, the nondominated solution set could occasionally be non-continuous) and this set is defined by means of cases study (depending on the problem’s variables values).</div><div>The new characterization of the Pareto optimal set of the new extension of this bi-objective sustainable EOQ model allows us to define the corresponding Pareto frontier. These findings have been implemented in <span>Python</span> and tested for a randomly generated set of instances.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103503"},"PeriodicalIF":7.2,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788091","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":"Strengthening omnichannel supply chain resilience through circular economy integration: A case-based analysis of disruption preparedness strategies","authors":"Ehsan Torshizi ,&nbsp;Mobina Mousapour Mamoudan ,&nbsp;Maziar Yazdani","doi":"10.1016/j.omega.2025.103466","DOIUrl":"10.1016/j.omega.2025.103466","url":null,"abstract":"<div><div>The increasing frequency and severity of global disruptions have underscored the structural vulnerabilities of conventional supply chains, particularly those optimized solely for efficiency. This study addresses a critical gap at the intersection of omnichannel logistics and circular economy integration by proposing a unified, resilience-oriented optimization model. The integration of these two paradigms is essential: while omnichannel networks inherently involve complex bidirectional flows, circular economy strategies provide built-in redundancy and resource recirculation capabilities. However, existing literature largely overlooks their combined potential for proactive disruption preparedness. To bridge this gap, we develop a mathematical model that simultaneously optimizes forward logistics (including facility siting, inventory allocation, cross-docking, and multimodal transport) and reverse flows (returns handling, material recovery, and recycling). Circular economy decisions are modeled as endogenous variables that dynamically adapt to disruption scenarios, allowing real-time reconfiguration of fulfillment strategies across online, in-store, and humanitarian channels. The model is validated through a real-world application in a national-scale canned food supply network. Results suggest the model maintains performance across varying disruption intensities and operational scales, revealing key trade-offs between profit efficiency, environmental impact, and service responsiveness. The findings highlight that resilience is most effectively achieved through anticipatory strategies such as capacity flexibility and fulfillment agility, rather than through reactive or compliance-driven measures alone. This study offers a scalable decision-support tool for practitioners and policymakers seeking to design sustainable, adaptive, and disruption-resilient omnichannel supply chains.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103466"},"PeriodicalIF":7.2,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840662","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}