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Call for Papers - Special Issues We are planning several special issues and you are welcome to contribute to any of them. Call for Papers – Special Issue on "Supply Chain Disruption Risk Investigation and Resilience Improvement Strategies under New Circumstances" Guest Editors: Xiaoyang Zhou, Xi’an Jiaotong University, China, zhouxiaoyang@xjtu.edu.cn  Zhong-Zhong Jiang, Northeastern University, China, zzjiang@mail.neu.edu.cn Benjamin Lev, Drexel University, USA, bl355@drexel.edu Aims and Scope In the context of the contemporary highly interconnected and globalized economy, supply chains serve as the backbone of production and distribution networks in all sectors^[1,2]. However, the global supply chain formed in the past three decades is facing fundamental restructuring and historic changes due to factors such as big-power politics, geopolitical tensions, and pandemics^[3]. These disruptions not only threaten the continuity of supply chain operations, but also have cascading effects on global markets and overall economic stability. Supply chain disruption risks can be defined as the subset of all supply chain risks with potential consequences that impede the supply chain at least temporarily of achieving its operational goals and/or jeopardize the existence of one or more supply chain partners^[4]. In the context of the global supply chain, the disruptions evolved over enterprises located in different countries or districts, leading to more profound effects on the global supply chain and world economy^[2,5]. As a result, managing supply chain disruption risk and maintaining a resilient supply chain under new circumstances has beccome a common concern for both academic research and practical implementation in the business world. In essence, supply chain resilience refers to the ability of a supply chain to quickly restore its normal functions following unexpected events and continue operating effectively in a new environment^[6]. It enables stakeholders to anticipate, absorb, and recover from disruptions effectively, thereby safeguarding their competitive advantage and ensuring long-term sustainability^[7]. Recent developments in the literature, particularly in the fields of management science (MS) and operations research (OR), have highlighted the need for better theorization in the assessment of supply chain disruption risks, analysis of risk propagation, strategies for ex ante and ex post risk management, and methods of supply chain resilience improvement^[8,9]. However, existing research lags significantly behind practical needs. Therefore, there is an urgent need to explore methods for investigating and responding to global supply chain disruption risk under new circumstances, in order to deepen the understanding of the theoretical foundation of supply chain risk management.  This special issue aims to attract the latest research on the supply chain disruption risk analysis and resilience improvement strategy, leading to an exploration of several crucial research domains using different MS/OR methodologies. All submissions must have clear managerial contributions and be built on rigorous research methods. In particular, the following research directions are in the scope of this Special Issue: Supply chain risk propagation analysis based on complex network Economic shock of supply chain risk Data-driven supply chain resilience assessment Resilient supply chain network Financial tools for supply chain risk mitigation Supply chain risk prevention and resolution Operational strategies for supply chain risk mitigation Multi-source risk supply chain early warning mechanisms Supply chain risk and industry security International logistics resilience under disruptive influences We expect novel and innovative papers in the areas that include but are not limited to the above. Any new models, theories and methods related to supply chain resilience fit the mission. To prepare their manuscript, authors should closely follow the “Guide for Authors” of Omega - The International Journal of Management Science. Authors should submit their paper via the OMEGA online submission and editorial system at https://www.journals.elsevier.com/omega and select “Special Issue: Supply chain disruption risk investigation and resilience improvement strategies under new circumstances” as the “Article Type”. Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Manuscripts will be strictly refereed based on the standards of Omega - The International Journal of Management Science. Publication Schedule The submission deadline: 2025/07/31 The final decision notification: 2025/12/31 Publication date (tentative): 2026/03/31 References [1] Inoue H., & Todo Y. (2019). Firm-level propagation of shocks through supply-chain networks. Nature Sustainability, 2(9), 841-847. [2] Chakraborty A., Reisch T., Diem C., Astudillo-Estévez P., & Thurner S. (2024). Inequality in economic shock exposures across the global firm-level supply network. Nature Communications, 15(1), 3348. [3] Feng P., Zhou X., Zhang D., Chen Z., & Wang S. (2022). The impact of trade policy on global supply chain network equilibrium: A new perspective of product-market chain competition. Omega, 109, 102612. [4] Heckmann I., Comes T., & Nickel S. (2015). A critical review on supply chain risk–Definition, measure and modeling. Omega, 52, 119-132. [5] Zhou X., Sun J., Fu H., Ge F., Wu J., & Lev B. (2024). Resilience analysis of the integrated China-Europe freight transportation network under heterogeneous demands. Transportation Research Part A: Policy and Practice, 186, 104130. [6] Sawik T. (2022). Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study. Omega, 109, 102596. [7] Liu M., Ding Y., Chu F., Dolgui A., & Zheng F. (2023). Robust actions for improving supply chain resilience and viability. Omega, 123, 102972. [8] Gao S. Y., Simchi-Levi D., Teo C. P., & Yan Z. (2019). Disruption risk mitigation in supply chains: the risk exposure index revisited. Operations Research, 67(3): 831-852. [9] Jiang Z.-Z., Zhao J., Sun M. (2024). Joint optimization of order picking and delivery in ergonomic picking systems with due dates for sustainability and resilience. Transportation Research Part E: Logistics and Transportation Review, 191, 103727. Call for Papers – Special Issue on “Multi-period Decision-Making with AI Synergy” Guest editors: Xiang Li, Chang’an University, China. lixiangbuct@163.com Ziyan Feng, Beijing University of Chemical Technology, China. fengzy@buct.edu.cn Aims and Scope This special issue aims to attract state-of-the-art research and practical applications of multi-period decision-making through artificial intelligence (AI) synergy, exploring several crucial research domains (transportation, logistics, manufacturing, supply chain, healthcare, energy, finance, investment, for instance) using different AI synergy methodologies to reduce uncertainty related to future periods and address the computational challenges inherent in solving multi-period optimization problems. Special issue information: Multi-period decision-making spans many domains, including production planning, inventory management, manufacturing, supply chain, transportation, logistics, healthcare, and energy management. These problems are characterized by periodicity, involving multiple periods with similar problem structures, as well as state transitions between periods—such as inventory levels in production planning. As such, decisions made in the current period have a direct impact on future states. Conversely, future information also significantly influences current decisions. Without considering future insights, decision-makers may resort to greedy strategies that could deviate from global optimality. Moreover, rapid developments in international relations, economic factors, and urban dynamics introduce increasing complexity and uncertainty into decisions, complicating efforts to accurately obtain future information. Therefore, the primary challenges in multi-period decision-making arise from two aspects: the uncertainty surrounding future information and the heightened difficulty in solving multi-period optimization problems. This special issue aims to explore the synergistic role of AI in reducing future information uncertainty and improving problem-solving efficiency throughout the multi-period decision-making process. By integrating machine learning, reinforcement learning, and advanced optimization techniques, organizations can make more accurate and forward-looking decisions at multiple periods, thereby enhancing their flexibility and adaptability. As AI technologies continue to advance rapidly, investigating their potential applications in long-term decision-making will yield new insights for both academia and practice. Directions and content of research expected in this Special Issue: 1. AI-Driven Predictive Modeling––Investigate how AI can process large datasets and recognize complex patterns to enhance the accuracy of predicting future trends, thereby supporting multi-period decision-making. 2. Prediction & Optimization Paradigm––Develop the integrated paradigm for prediction and optimization in multi-period decision problems, aiming to minimize decision errors, rather than prediction errors. 3. AI Synergy for Improving Problem Solving––Investigate algorithms that integrate AI to enhance efficiency in solving multi-period decision-making problems and ensure effective implementation in real-world production scenarios. 4. Real-Time Decision-Making––Combine AI capacity to analyze real-time data with optimization’s ability to generate efficient solutions (e.g., approximate dynamic optimization algorithm, reinforcement learning) for multi-period decision-making, particularly in dynamic and rapidly changing environments. 5. Multi-Period Decomposition Approach––Develop AI-based decision support tools (e.g., decision support systems, rolling horizon approach) to simplify the complexity of multi-period decision problems to enhance managers’ decision-making capabilities. 6. Case Studies and Empirical Analysis––Conduct empirical case studies to examine how organizations successfully implement AI technologies to optimize multi-period decision-making processes. Manuscript submission information: The Journal’s submission system is open for submissions to our Special Issue. When submitting your manuscript please select the article type “VSI: Multi-period Decision-Making with AI Synergy”. Please submit your manuscript before 30^thJune 2025. The submission link is: https://www.editorialmanager.com/Omega/default.aspx All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be marked and branded as Special Issue articles. Papers submitted to the Special Issue will be subjected to the rigorous review process of OMEGA. Please see an example here: https://www.sciencedirect.com/journal/omega Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and link to submit your manuscript is available on the Journal’s homepage at: https://www.sciencedirect.com/journal/omega/publish/guide-for-authors Suggested Schedule - Important Dates Submission deadline: 30^th June 2025 First decision notification: 31^st August 2025 Final decision notification: 31^st December 2025 Expected publication year (tentative): 2026 Inquiries, including questions about appropriate topics, may be sent electronically to Xiang Li (lixiangbuct@163.com), Ziyan Feng (fengzy@buct.edu.cn) Call for Papers – Special Issue on “Supply Chain Viability in the post-COVID era” Guest Editors Dmitry Ivanov, Berlin School of Economics & Law, Germany, dmitry.ivanov@hwr-berlin.de Yan Tu, Wuhan University of Technology, China, tuyan_belle@163.com Aims and Scope Supply chain viability has appeared as a new and distinct area in operations research and management science during the COVID-19 pandemic (Ivanov and Keskin 2023). The key idea was to represent and analyse new quality of resilience, i.e., the ability of supply chain to “maintain itself and survive in a changing environment through a redesign of structures and re-planning of performance with long-term impacts’ (Ivanov 2022). Two recent special issues on supply chain viability in Omega (Ivanov and Keskin 2023) and IJPR (Ivanov et al. 2023) with over 30 published and more than 100 submitted papers in total have confirmed the growing interest in this area. Viability has been a central idea for development of several new notions and concepts such as the LCN (low certainty need supply chain) framework (Ivanov and Dolgui 2019), supply chain viability (Ivanov and Dolgui 2020), the viable supply chain model (Ivanov 2022), and AURA (active usage of resilience assets) framework (Ivanov 2021). A viable supply chain and its associated frameworks comprise not only the supply chain itself, but also the intertwined supply network (ISN). An ISN is an ‘entirety of interconnected supply chains which, in their integrity secure the provision of society and markets with goods and services’ (Ivanov and Dolgui 2020). Being born at the times of the COVID-19 pandemic supply chain viability theory and practice are getting more and more important in the post-COVID era. This era is characterized by new and unprecedented challenges to supply chain and operations management because of highly volatile demand, supply, and capacities. It becomes clear that viability takes a much deeper role in supply chain and operations research as just a survival during severe disruptions. In the transitional, stability-based view of resilience, models for resilient network design and recovery are usually triggered by disruptions and performance deviations to return to some “normal” states (Hosseini et al. 2019, Aldrighetti et al. 2023). This view accounts for known-known uncertainty which was fairly justified in the pre-COVID era. The post-COVID era, supply chain managers had to deal with long-term crises characterized by highly volatile demand, capacity, and supply instead of predictable disruptions of a short-term durations (Brusset et al 2023, Choi et al. 2023, Graves et al. 2023, Li et al. 2023b). Managing resilience at such crises times is challenged by unpredictable appearance and scaling of disruptions, recovery in the presence of (and not after) disruptions, and missing possibilities to bounce back to original states (Ivanov 2020). The focus thus shifts to viability confronting supply chain resilience modeling and optimization with novel challenges, e.g.: • Long-lasting crisis with hardly predictable scaling sequences of disruptions • Simultaneous disruptions in supply, demand, and logistics • Recovery in the presence of disruptions and the ripple effect • Simultaneous and/or sequential openings and closures of suppliers, facilities, and markets • Response and recovery strategies taking potential crisis recurrence and setbacks into consideration. From the viability perspective, modeling and optimization of resilience is difficult to perform using predictions of possible disruption scenarios. At the times of long-term crises, we have to cope with a low or even missing availability of past data to forecast future disruptions and their probabilities, recovery is performed in the presence of disruptions and the ripple effect, and the optimization objective is to ensure survival rather than minimizing performance deviations after a disruption. As noted in Ivanov (2023), the viability-based view shifts the focus from avoiding oscillations and recovering some stable states toward proactive adaptation and performance persistence. The adaptation-based view aims at designing structurally adaptable networks with process flexibility and actively used redundancy. It considers resilience from the value-creation perspective accounting for unknown-unknown uncertainties.” Viability considers resilience as a property or quality to ensure the network health and existence over a long period of time. Viability focuses not so much on maintaining some stable states but rather on supply chain adaptation ensuring performance persistence in the presence of disruptions, i.e., the ability to survive, to exist. This is the adaptation-based view (ABV) triggered by the ideas of supply chain viability (Ivanov 2023). Viability takes a proactive, foresight-oriented adaptation (Ivanov and Dolgui 2020, MacCarthy et al. 2022). In this Special Issue we call for papers seeking to advance state-of-the-art and practical applications of supply chain viability. We aim at collating research on resilience analytics in supply chain and manufacturing systems stemming from the fields of mathematical optimization under epistemic (known-unknown) and deep (unknown-unknown) uncertainty, network theory, game theory, complex adaptive systems, control theory, digital technology, and data-driven analytics. We stress that papers about resilience to singular-event disruptions of some known probabilities and random uncertainties are out of scope. Directions and content of research expected in this Special Issue: · Optimization objectives – adaptability and performance persistence; viability, existence, and survivability in the long term · Disruption frequency and time scale - medium- and long-term crises · Capabilities under examination – dynamically adaptable and structurally flexible networks and processes · Disruption data availability – low past data availability; focus on timely disruption recognitions and proactive, foresight-oriented adaptability rather than on hindsight-oriented disruption predictions. First, we note that “the resilience approach to random uncertainty that is based upon prediction of disruptions having “zero harm” to the planned system behaviors as final objective is difficult to apply to deep uncertainty” (Ivanov 2023). The adaptation-driven approaches are therefore in the forefront of this Special Issue that accept insufficiency of our knowledge and a large scope of “unknown-unknown” in the models aiming to ensure performance continuity and system existence. Second, a fundamental question is what we understand under “normal” operation and how we measure supply chain resilience. In the deep uncertainty setting, “success in resilience management is measured not by the number of disruptions which have been successfully recovered but by the number of disruptions which have been proactively mitigated by redundancy, flexibility and adaptability and did not hit the supply chain” (Ivanov 2023). Here it is important to explore the indicators which can inform managers about viability. We do not limit our Special Issue to the COVID-19 pandemic only and extrapolate the setting shown above to other possible supply chain crises that can happen in future and driven, e.g., by long-term shortage of critical materials (e.g., semiconductors), long-term political crises, global financial crises, recessions, and climate change. Creating relevant knowledge for these future challenges now is imperative and of vital importance not only for supply chain resilience but also for viability of the ISNs and entire business ecosystems (Ivanov and Dolgui 2022). Under such settings, we call the research community for developing modeling and optimization techniques that account for such settings and guide firms in building adaptable, reconfigurable, resilient, and viable supply chains, ISNs, and business ecosystems. Review Process and Timeline Review Process Manuscripts should comply with the scope, standards, format, and editorial policy of the OMEGA: The International Journal of Management Science. All papers must be submitted through the official OMEGA submission system https://www.editorialmanager.com/Omega/default.aspx with a clear selection indicating that the submission is for this Special Issue. Before submission, authors should carefully read over the journal’s “Guide for Authors”. Papers submitted to the Special Issue will be subjected to the rigorous review process of OMEGA. Publication Schedule · Deadline for paper submission: 31 July 2024 · Deadline for notification of first decision: 31 October 2024 · Final manuscripts decision: 31 March 2025 All queries about the special issue should be sent to the Guest Editors. We expect novel and innovative contributions with high practical relevance, ideally motivated by an industrial and healthcare context that are in line with OMEGA scope and policy. We invite OR/MS researchers to explicitly incorporate specifics of epistemic and deep uncertainty toward supply chain viability. As a final remark, we note that a combination of random, epistemic, and deep uncertainties in resilience modeling and optimization is an essential future research direction. Thus, the essential shift from a hindsight-oriented notion “how to react if a disruption happened?” to a foresight-driven notion “how to absorb disruptions through adaptability?” should be visible in submissions. To obtain a more extended view on the viability ideas underlying this Special Issue, we recommend the following literature. References Aldrighetti R., Battini D., Ivanov D. (2023). Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments. Omega, 117, 102841. Brusset X., Ivanov, D., Jebali, A., La Torre, D., Repetto, M. (2023). A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic. International Journal of Production Economics, 263, 108935. Choi, TY., Netland, T., Sanders, N., Sodhi, MMS, Wagner SM (2023). Just-in-time for supply chains in turbulent times. Production and Operations Management, 32(7), 2331-2340. Choi, T., Shi, X. (2022). Reducing supply risks by supply guarantee deposit payments in the fashion industry in the “new normal after COVID-19”. OMEGA: The International Journal of Management Science 109, 102605. Dolgui, A., Gusikhin O., Ivanov, D., Li, X., Stecke, K. (2023) A Network-of-Networks Adaptation for Cross-Industry Manufacturing Repurposing. IISE Transactions, https://doi.org/10.1080/24725854.2023.2253881. Graves, SC, Tomlin, BT, Willems SP (2023). Supply chain challenges in the post–Covid Era. Production and Operations Management 31 (12), 4319-4332. Hosseini, S., Ivanov, D., Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research: Part E, 125, 285-307. Ivanov D. (2021) Introduction to Supply Chain Resilience, Springer Nature, Cham Ivanov D. (2022). Viable Supply Chain Model: Integrating agility, resilience and sustainability perspectives – lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 319, 1411-1431. Ivanov, D. (2023). Two views of supply chain resilience. International Journal of Production Research, DOI 10.1080/00207543.2023.2253328. Ivanov, D. (2023). Intelligent Digital Twin (iDT) for Supply Chain Stress-Testing, Resilience, and Viability. International Journal of Production Economics, 263, 108938. Ivanov, D., Dolgui, A., Blackhurst, J., Choi, TM. (2023). Toward supply chain viability theory: from lessons learned through COVID-19 pandemic to viable ecosystems. International Journal of Production Research, 61(8), 2402-2415. Ivanov D., Keskin B (2023). Post-pandemic adaptation and development of supply chain viability theory. Omega, 116, 102806. Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922. Ivanov, D., Dolgui, A. (2022). The shortage economy and its implications for supply chain and operations management. International Journal of Production Research, 60(24), 7141-7154. Ivanov D., Dolgui A. (2020). Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles towards Survivability. A Position Paper Motivated by COVID-19 Outbreak. International Journal of Production Research, 58(10), 2904-2915. Liu, M., Liu, Z., Chu, F., Dolgui, A., Chu, C., Zheng, F. (2022). An optimization approach for multi-echelon supply chain viability with disruption risk minimization. Omega, 112, 102683. MacCarthy, B., Ahmed, W., Demirel, G. (2022). Mapping the supply chain: Why, what and how? International Journal of Production Economics, 108688. Ramani V, Ghosh D, Sodhi M (2022) Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry. Omega 113:102720. Rozhkov, M., Ivanov, D., Blackhurst, J., Nair, A. (2022). Adapting supply chain operations in anticipation of and during the COVID-19 pandemic. Omega, 110, 102635. Sawik, T. (2023). A Stochastic Optimization Approach to Maintain Supply Chain Viability under the Ripple Effect. International Journal of Production Research, 61(8), 2452-2469. Call for Papers – Special Issue on “Production and Service Operations Management in Digital Economy” Guest Editors (listed alphabetically) Zhong-Zhong Jiang, Northeastern University, China, zzjiang@mail.neu.edu.cn Liming Yao, Sichuan University, China, lmyao@scu.edu.cn Xiaoyang Zhou, Xi’an Jiaotong University, China, x.y.zhou@foxmail.com . Aims and Scope With the rapid rise of emerging information technologies, such as artificial intelligence, big data and analytics, mobile internet, cloud computing, internet of things and blockchain, the digital economy is playing an increasingly important role in the global economy^[1]. In 2020, the global digital economy reached $32.6 trillion, accounting for 43.7% of GDP^[2]. The emergence of the digital economy is accelerating the transformation of traditional economy modes^[3]. Pretty much all businesses today strive for digital competency. Online markets are rising and thriving. The major e-commerce and network platforms that carry online economic activities have also sprung up. The increased use of digitization is changing the business models, collaborations, and work practices in product and service provision^[4,5]. It also challenges social and market institutions at all levels—societal, industry, professional, organizational, group, and individual^[6-8]. Consequently, effectively promoting the digital transformation and further improving the production and service operations management (PSOM) are critical problems for enterprises. This special issue is to explore the development mechanism of the digital economy, enhance the understanding of the theoretical basis, innovate research methods based on digital technology, and conduct in-depth research on PSOM in the digital economy. The rapid development of the digital economy not only provides new opportunities and challenges for efficient and scientific operation and management in industry, but also nurtures new research topics and methods in academia. The digital economy has four core characteristics: digital information, modern information networks, digital technologies, and new business models. First, digital information, such as images, words, and sounds, refers to information stored on virtual carriers and can be used repeatedly. Second, the modern information network, which refers to the virtual network connecting all participants, is the carrier formed by the Internet for market organization and transmission of digital information. Third, digital technology, such as artificial intelligence, blockchain, cloud computing, and big data, is a tool for analyzing and processing digital information. Fourth, integrating with the traditional economy, digital technology induces new business models, such as platform supply chains, pay per use services, service-oriented manufacturing, sharing manufacturing^[9-10]. Recent developments in the literature, particularly from the MS and OR disciplines, have highlighted the need for digital industry transformation to be better theorized^[3-7]. However, this field is still emerging. The existing research lags far behind the practical needs of providing decision support and policy suggestions in the digital economy. To this end, it is urgent to explore the development mechanism of the digital economy, enhance the understanding of the theoretical basis, innovate research methods based on digital technology, and conduct in-depth research on PSOM in the digital economy. This special issue aims to attract state-of-the-art research on PSOM in the digital economy, leading to an exploration of several crucial research domains using different MS/OR methodologies. All submissions must have clear managerial contributions and be built on rigorous research methods. In particular, the following research directions are in the scope of this Special Issue: Data management and governance: e.g., valuation of data assets, information sharing Platform operations: e.g., supply and demand matching, pricing and revenue management Optimization decision: e.g., networked collaboration, resource matching, dynamic optimization Application/adoption of digital technologies, such as big data, artificial intelligence, data-driven Analysis of the new economic models, such as the gig economy, sharing economy, social e-commerce Analysis of the new global supply chain management model in the digital economy Risk control and management in the digital economy Enterprise sustainability in the digital economy We expect novel and innovative papers in the areas that include but are not limited to the above. Any new models, theories and methods related to the digital economy fit the mission. To prepare their manuscript, authors should closely follow the “Guide for Authors” of Omega - The International Journal of Management Science. Authors should submit their paper via the OMEGA online submission and editorial system at: https://www.journals.elsevier.com/omega and select “Special Issue: Production and Service Operations Management in Digital Economy” as the “Article Type”. Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Manuscripts will be strictly refereed based on the standards of Omega - The International Journal of Management Science. Publication Schedule The submission deadline: October 1, 2023. The final decision notification: May 1, 2024. Publication date (tentative): Late 2024 References [1] OECD. A roadmap toward a common framework for measuring the digital economy. Report for the G20 Digital Economy Task Force, 2020. [2] China Academy of Information and Communications Technology. White paper on the global digital economy, 2021. [3] Avi Goldfarb, Catherine Tucker. Digital economics. Journal of Economic Literature, 2019, 57(1): 3-43. [4] Zhong-Zhong Jiang, Guangwen Kong, Yinghao Zhang. Making the most of your regret: Workers’ relocation decisions in on-demand platforms. Manufacturing & Service Operations Management, 2021, 23(3): 695-713. [5] Xiaoyang Zhou, Kexin Chen, Haoyu Wen, Jun Lin, Kai Zhang, Xin Tian, Shouyang Wang, Benjamin Lev. Integration of third-party platforms: Does it really hurt them? International Journal of Production Economics, 2021, 234: 108003. [6] Susan V. Scott, Orlikowski Wanda. The digital undertow: How the corollary effects of digital transformation affect industry standards. Information Systems Research, 2022, 33(1): 311-336. [7] Qing Zhang, Xiuqi Jiang, Yini Zheng. Blockchain adoption and gray markets in a global supply chain. Omega, 2023, 115: 102785. [8] Balan Sundarakani, Aneesh Ajaykumar, Angappa Gunasekaran. Big data driven supply chain design and applications for blockchain: An action research using case study approach. Omega, 2021, 102: 102452. [9] Zhong-Zhong Jiang, Guangqi Feng, Zelong Yi, Xiaolong Guo. Service-oriented manufacturing: A literature review and future research directions. Frontiers of Engineering Management, 2022, 9(1): 71-88. [10] Xiaoyang Zhou, Chong Gao, Ding Zhang. Product service supply chain network competition: An equilibrium with multiple tiers and members. International Journal of Production Research, 2022, DOI: 10.1080/00207543.2022.2060771. Call for Papers – Special Issue on “Intelligent Decision analysis based on online data” Guest Editors Huchang Liao, Business School, Sichuan University. liaohuchang@163.com Luis C. Dias, University of Coimbra, CeBER and Faculty of Economics. lmcdias@fe.uc.pt. Aims and Scope With the advent of the digital era, more decision-making activities begin to use the massive, high-dimensional, heterogeneous, and multi-source data generated by online activities of Internet users. A well-established trend in the era of digital transformation is the increasingly extensive use of data analytics and machine learning tools in decision making, both at strategic and operational levels. For example, businesses now routinely collect large volumes of fine-grained data to analyze consumers’ behavior, and consumers can also track changes in firms’ offers to make informed purchasing decisions. Thus, by efficiently and quickly mining Internet big data and incorporating it in their decision analysis processes, firms have an opportunity to stand out in increasingly competitive and rapidly changing markets, while consumers can obtain a better consumption experience [1, 2]. This special issue is related to applying intelligent decision analysis techniques to analyze online data. Among such data, user-generated content, i.e., personalized creative content that Internet users display to other users through a network platform, is increasingly important. As one of the main forms of spreading online word-of-mouth, user-generated content can not only reflect the actual attributes and consumers experience of products/services, but also provide information such as product/service defects that platforms will not mention [3]. However, user-generated content is characterized by massive, high-dimensional, multi-source, heterogeneous and unstructured features. Humans cannot directly process user-generated content due to the limited cognitive and information-processing capabilities. In the digital era, decision analysis methods need to be flexible, responsive, and dependable, and can manage a large amount of complex information. In this special issue, we focus on intelligent decision analysis techniques [4, 5] based on the integration of digital and intelligent methods. Specifically, we focus on the combination of machine learning methods with multiple criteria decision analysis methods. Data mining techniques can help to efficiently process high-dimensional, multi-source, heterogeneous and unstructured data [6]. Multiple criteria decision analysis [7, 8] can analyze advantages and disadvantages of products/services from multiple dimensions, which can help consumers make satisfactory purchasing decisions, and help enterprises understand consumer markets and then adjust their business strategy. Thus, based on state-of-the-art computer technology, developing corresponding intelligent decision analysis techniques can improve the efficiency of decision-making activities based on online data, accelerate the transformation of enterprise digitalization, and promote the process of integration of digitalization and intelligence. This special issue seeks to promote state-of-the-art research on the integration of digital and intelligent methods in decision analysis, leading to an exploration of several crucial research domains using different OR/MS methodologies. In particular, the scope of this special issue includes (but is not limited to) the following research directions: · Hybrid decision-making frameworks combining machine learning and multiple criteria decision-making. · Intelligent information representation methods based on user-generated content. · Intelligent information fusion methods based on user-generated content. · Preference modeling and preference learning model based on big data. · Explainable deep preference learning model for consumers. · Dynamic intelligent decision-making methods based on big data. · Intelligent decision analysis methods for modeling and forecasting based on user-generated content. We expect innovative contributions with high practical relevance that are methodically rigorous and rooted in data-driven analytics, multiple criteria decision analysis, machine learning, and optimization for these research domains. We especially welcome papers addressing sustainable development challenges (healthcare, environment, etc.).  To prepare a manuscript, authors should closely follow the “Guide for Authors” of Omega - The International Journal of Management Science. Authors should submit their manuscript via the OMEGA online submission and editorial system at: https://www.journals.elsevier.com/omega and select “Special Issue: Decision analysis techniques based on online data” as the “Article Type”. The submitted manuscript should not have been previously published nor be currently under consideration for publication elsewhere. Manuscripts will be strictly refereed based on the high standards of Omega - The International Journal of Management Science. Publication Schedule    The submission deadline is June 30, 2023.    The final decision notification: December 31, 2023.    Publication date (tentative):  early 2024 References [1] Wu, X. L., & Liao, H. C. (2021). Model personalized cognitions of customers in online shopping, Omega, 104, 102471. [2] Liu, F., Liao, H., & Al-Barakati, A. (2022). Physician selection based on user-generated content considering interactive criteria and risk preferences of patients. Omega, 102784. Doi: org/10.1016/j.omega.2022.102784. [3] Timoshenko, A., & Hauser, J. R. (2019). Identifying customer needs from user-generated content. Marketing Science, 38(1), 1-20. [4] Guo, M., Zhang, Q., Liao, X., Chen, F. Y., & Zeng, D. D. (2021). A hybrid machine learning framework for analyzing human decision-making through learning preferences. Omega, 101, 102263. [5] Martyn, K., & Kadziński, M. (2022). Deep preference learning for multiple criteria decision analysis. European Journal of Operational Research. Doi: org/10.1016/j.ejor.2022.06.053 [6] Vanhala, M., Lu, C., Peltonen, J., Sundqvist, S., Nummenmaa, J., & Järvelin, K. (2020). The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research. Journal of Business Research, 106, 46-59. [7] Dyer, J. S., & Smith, J. E. (2021). Innovations in the science and practice of decision analysis: The role of management science. Management Science, 67(9), 5364-5378. [8] Dias, L. C., Dias, J., Ventura, T., Rocha, H., Ferreira, B., Khouri, L., & do Carmo Lopes, M. (2022). Learning target-based preferences through additive models: An application in radiotherapy treatment planning. European Journal of Operational Research, 302(1), 270-279. A Special Issue on “Post-pandemic Adaptation and Viability of Supply Chains” Guest Editors Dmitry Ivanov, Berlin School of Economics and Law /Hochschule für Wirtschaft und Recht Berlin, divanov@hwr-berlin.de (corresponding editor) Burcu B. Keskin, The University of Alabama, bkeskin@cba.ua.edu The Special Issue Appeared A Special Issue on “Decision Making based on Big Data” Guest Editors Yi Liao, Southwestern University of Finance and Economics, yiliao@swufe.edu.cn (corresponding editor) Joe Zhu, Worcester Polytechnic Institute, jzhu@wpi.edu Gang Kou, Southwestern University of Finance and Economics, kougang@swufe.edu.cn The Special Issue Appeared A Special Issue on “Finance-Operations Interface Mechanisms and Models” Guest Editors Desheng Dash Wu, Stockholm University, and University of Chinese Academy of Sciences, dash.wu@gmail.com David L. Olson, University of Nebraska, dolson3@unl.edu Shouyang Wang, Chinese Academy of Sciences, sywang@amss.ac.cn The Special Issue appeared Volume 88. A Special Issue on “Human judgment in supply chain forecasting and multi-tier operations” Guest Editors Behnam Fahimnia, The University of Sydney Business School, Australia behnam.fahimnia@sydney.edu.au Nada Sanders, Northeastern University, USA  n.sanders@neu.edu Enno Siemsen, Wisconsin School of Business, University of Wisconsin-Madison, USA esiemsen@wisc.edu The Special Issue appeared Volume 87. A Special Issue on “Customized Assembly Systems” Guest Editors Olga Battaïa, ISAE-SUPAERO, France olga.battaia@isae.fr Alena Otto, University of Siegen, Germany alena.otto@uni-siegen.de  Erwin Pesch, University of Siegen, Germany erwin.pesch@uni-siegen.de Fabio Sgarbossa, University of Padua, Italy  fabio.sgarbossa@unipd.it The Special Issue appeared Volume 78. A Special Issue on “New Research Frontiers in Sustainability” Guest Editors Chialin Chen, Queen’s University, Canada cchen@business.queensu.ca Sean Zhou, Chinese University of Hong Kong, Hong Kong, China zhoux@baf.cuhk.edu.hk Joe Zhu, Worcester Polytechnic Institute, USA jzhu@wpi.edu The Special Issue appeared Volume 66 Par B. A Special Issue on “Network-DEA in the Service Sector” Guest Editors Necmi Kemal Avkiran, The University of Queensland, UQ Business School, Brisbane, Australia, n.avkiran@business.uq.edu.au Kaoru Tone, National Graduate Institute for Policy Studies, Tokyo, Japan, tone@grips.ac.jp The Special Issue appeared Volume 60. A Special Issue on “Business Analytics” Guest Editors Michael Doumpos Technical University of Crete, Greece mdoumpos@dpem.tuc.gr Constantin Zopounidis Technical University of Crete, Greece & Audencia School of Management, France kostas@dpem.tuc.gr The Special Issue appeared Volume 59, Part A. A Special Issue on “Decision Making in Enterprise Risk Management (ERM)” Guest Editors Desheng Dash Wu, University of Toronto, Canada, dwu@fields.utoronto.ca Alexandre Dolgui, Ecole des Mines de Saint-Etienne, France, alexandre.dolgui@mines-stetienne.fr David L. Olson, University of Nebraska, USA, Dolson3@unl.edu The Special Issue appeared Volume 57, Part A. A Special Issue on "Management Science and Environmental Issues” Guest Editors Chiang Kao, National Cheng Kung University, Taiwan, ckao@mail.ncku.edu.tw Hsihui Chang, Drexel University, USA, hc336@drexel.edu Rong-Ruey Duh, National Taiwan University, Taiwan, rrduh@management.ntu.edu.tw The Special Issue appeared Volume 41, Issue 2. A Special Issue on "Data Envelopment Analysis: The Research Frontier- This Special Issue is dedicated to the memory of William W. Cooper 1914-2012” Guest Editors Wade D. Cook, York University, Canada, wcook@schulich.yorku.ca Lawrence M. Seiford, University of Michigan, USA, seiford@umich.edu Joe Zhu, Worcester Polytechnic Institute, USA, jzhu@wpi.edu The Special Issue appeared Volume 41, Issue 1. A Special Issue on "Forecasting In Management Science” Guest Editors John E. Boylan, Buckinghamshire New University, UK john.boylan@bucks.ac.uk Aris A. Syntetos, University of Salford, UK a.syntetos@salford.ac.uk The Special Issue appeared Volume 40, Issue 6. A Special Issue on "Empirical Research in the EU Banking Sector" Guest Editors Fotios Pasiouras, University of Bath, UK f.pasiouras@bath.ac.uk Constantin Zopounidis, Technical University of Crete, Greece kostas@dpem.tuc.gr The Special Issue appeared Volume 38, Issue 5. A Special Issue on "Ethics and Operations Research" Guest Editors Marc Le Menestrel, marc.lemenestrel@upf.edu Luk N. Van Wassenhove,  luk.van-wassenhove@insead.edu The Special Issue appeared Volume 37, Issue 6. A Special Issue on "Role of Flexibility in Supply Chain Design and Modeling" Guest Editor Charu Chandra, charu@umich.edu Associate Guest Editor Janis Grabis, grabis@itl.rtu.lv The Special Issue appeared Volume 37, Issue 4. A Special Issue on “Management Science Research in China: A Special Issue Dedicated to the 2008 Beijing Olympic Games” Guest Editor Joe Zhu, jzhu@wpi.edu The Special Issue appeared Volume 36, Issue 6. A Special issue on "Logistics: New Perspectives and Challenges" Guest Editors Angappa Gunasekaran, agunasekaran@umassd.edu T. C. Edwin Cheng, lgtcheng@polyu.edu.hk The Special Issue appeared Volume 36, Issue 4. A Special issue on "Multiple Criteria Decision Making for Engineering" Guest Editors Margaret M. Wiecek, wmalgor@clemson.edu Matthias Ehrgott, m.ehrgott@auckland.ac.nz Georges Fadel, fgeorge@clemson.edu José Rui Figueira, figueira@ist.utl.pt The Special Issue appeared Volume 36, Issue 3. A Special issue on "Knowledge Management and Organizational Learning" Guest Editor William R. King, billking@katz.pitt.edu Associate Guest Editors T. Rachel Chung, rachel.chung@gmail.com Mark H. Haney, mhaney@katz.pitt.edu The Special Issue appeared Volume 36, Issue 2. Cluster Papers from INFORMS Conference in Atlanta October 2003 Guest Editors Donna C. Llewellyn, donna.llewellyn@cetl.gatech.edu Paul Griffin, pgriffin@isye.gatech.edu The Special Issue Section appeared Volume 36, Issue 1. A Special issue on "Telecommunications Applications" Guest Editors Rina Schneur, rina.schneur@verizon.com Hui Liu, hui.liu@verizon.com The Special Issue appeared Volume 35, Issue 6. A Special issue on "Reverse Production Systems: Disassembly and other Reverse Manufacturing related practices" Guest Editor B. Adenso-Diaz,  ADENSO@epsig.uniovi.es The Special Issue appeared Volume 34, Issue 6. A Special Issue on selected papers from APORS Conference in New Delhi, India, December 2003 Guest Editor M.C. Puri, mcpuri@maths.iitd.ernet.in The Special Issue appeared Volume 33, Issue 5.