Data-Driven Services and Operations
The OR Spectrum Department Data-Driven Services and Operations aims at publishing high-quality research bridging the gap between data analysis and optimization. In the last decade, through process automation, the ubiquitous availability of the internet, and the rise of new business models, large amounts of operational data have been collected at almost no cost, for instance, in the areas of health care, manufacturing, transport, mobility, and retail. At the same time, methods of prescriptive analytics such as dynamic and stochastic optimization have been developed further, but how to aggregate operational data to consider it in state-of-the-art decision making remains a challenging question.
The department is especially interested in papers that analyze and aggregate large amounts of secondary operational data or primary data (e.g., from surveys or lab experiments) to foster innovative decision-making approaches that benefit from this data. Ideally, both perspectives – data analysis and optimization – are considered to some extent. Example applications include, but are not limited to, all kinds of data-driven services and operations in the areas listed above. Applications which are based on new business models like online-to-offline services, platform services, and sharing services are, in particular, welcome. Example methods include, but are not limited to, customer choice modelling, approximate dynamic programming, machine and reinforcement learning, stochastic optimization, simulation-based optimization, and revenue management.
The primary focus of the Decision analysis (DA) department is dedicated to advancing the theory and application of all aspects of decision analysis which is a form of decision-making that involves identifying and assessing a decision and taking actions based on the decision that produces the most favorable outcome. The main focus of this department is to develop and study operational decision-making methods, drawing on all aspects of decision theory, decision analysis with the ultimate objective of providing practical guidance for decision-makers. Specifically, we encourage submissions that bridge the theory and practice of decision analysis, facilitating communication and the exchange of knowledge among decision analysts in academia, business, industry, and government.
The fight against climate change and the transition to a clean, carbon-neutral society is one of the most urgent issues of our time. The energy sector is at the heart of this transformation. The Energy Department invites papers that contain innovative contributions in Operations Research that help to understand and support these developments by providing quantitative methods and tools for research, companies, and policymakers.
The department endorses a broad view on energy-related problems encompassing the analysis and design of technical infrastructure, quantitative strategies for energy trading, risk management, investment and capacity planning, mechanism and market design, as well as policy analysis.
While we recognize the cross-disciplinary nature of energy research, successful submissions have a strong methodological core based on quantitative methods of operations research. Papers may either contain real-world applications of operations research or propose a novel theory motivated by the mentioned challenges in the energy sector.
The ORSP Finance Department promotes the use of operations research, optimization methods, applied mathematics, and decision analytics tools to address financial planning, risk measurement, and evaluation problems in finance. Research in this area has traditionally emphasized issues related to portfolio selection, asset-liability management and asset pricing applications. More recent developments in the field include risk capital allocation and comprehensive risk management problems with an increased emphasis on computationally challenging applications arising under very general decision paradigms and probabilistic assumptions. Thus employing from stochastic to robust optimization, to more recent optimization approaches under ambiguity as well as heuristic and statistical learning methods.
The scope of OR for Finance research encompasses the above wide range of applications and focuses on numerical methods, optimizations, and approximation methods addressing mainstream and innovative financial optimization problems with a strong applied philosophy and a potential for efficient decision making in the finance sector, from specific valuation problems to decision support tools.
This department welcomes papers based on innovative research and applications, surely relying on OR-based and computational approaches which enhance our understanding of increasingly complex finance conditions and promote the spread of best practices.
These may include modeling of existing and emerging finance applications, methodological contributions to the solutions of industry problems, as well as empirical studies that validate existing theory or examine novel financial trends and market phenomena. In line with the editorial mission of OR Spectrum, the department emphasizes relevance of submitted papers from a joint finance and operations research perspective.
Freight Transport and Mobility
The OR Spectrum Department Freight transport and Mobility aims to be the outlet for high-quality research work in the broad field of transport, logistics, and mobility, with a strong methodological ground in Operations Research. Papers that work on interesting and challenging relevant practical applications and cases, with a strong and sufficient methodological contribution, are particularly welcomed. The Department Freight transport and Mobility includes within its topical scope all decisions (operational, tactical, and strategic) of transportation analysis related to both people (mobility) and freight transport. This involves in the broadest sense, without being complete, logistics systems, mobility, traffic engineering, location theory, user behavior, transport demand modeling, crowd and on-demand logistics, air operations, rail operations, multi-modal transportation, warehousing, etc. The methodological scope involves the broad domain of Operations Research methods and techniques. The published work is primarily based on the strong mono-disciplinary nature. However, multidisciplinary research, presenting cutting-edge high-quality work on the interface with other disciplines (e.g. game theory, machine learning, information systems, Artificial Intelligence, behavior, ethics) is strongly encouraged.
Game Theory and Multi-Agent Systems
The focus of the Game Theory and Multi-Agent Systems Department within OR Spectrum lies on multi-disciplinary research on distributed systems in which autonomous agents interact. The bandwidth of possible topics and applications is large, including the modeling and analysis of biological systems, communication networks, digital markets, energy networks and markets, transportation and logistics networks as well as social networks. Besides theoretical or computational work, the department also covers research on agent's behavior, including experiments and field studies. We seek papers that contribute to methodologically oriented research such as new game-theoretic models, structural insights, learning or optimization algorithms, as well as empirical work reporting on case studies or related topics at the interface of game theory and multi-agent systems. Submissions on related fields such as bilevel optimization, complementarity problems, or variational inequalities are also welcome.
Health Care & Humanitarian Logistics
Healthcare Management: The department welcomes submissions that advance the state of the art in decision-making related to the design and management of healthcare services. Submissions are expected to apply OR methodology rigorously and may also draw on methodology across disciplines. The department encourages submissions that benefit from data analytics. We solicit novel contributions in a broad range of areas including but not limited to health policy making, health services design and management, clinic operations management, medical decision making, personalized treatment, chronic patients care, preventive services, genomics, and precision medicine. Other possible topics relate to models and investigations in healthcare economics. Submissions at the intersection of healthcare management and crisis management or humanitarian operations areas are encouraged as well.
Humanitarian Logistics: Disruptive events, such as disasters, humanitarian crises, and epidemics, face our societies with huge challenges. In the last years, quantitative approaches to disaster management and humanitarian aid have found increasing interest. The development of methodology for these applications is demanding in view of complex problem characteristics, conflicting objectives, and a typically high degree of uncertainty. Optimization models, decision analysis methods, and data analytics technologies have already been established for this field. Still, much has yet to be done to further develop these approaches, to explore new ideas, and to integrate the methodological progress into the practical work of governmental and non-governmental humanitarian organizations. We invite methodological and application-oriented research in the field of humanitarian operations. A clear relation to the operations research paradigm is expected. Moreover, contributions should foster new insights, and they should have a possible impact on practice by being rooted in real problems.
The focus of the Optimization Department is the advancement of mathematical programming that contributes to the field of Operations Research. The research can range from theoretical results to computational aspects, including application studies. We welcome submissions with a discrete or continuous nature, and from all subdisciplines, such as, e.g., combinatorial optimization, robust optimization, conic optimization or mixed-integer (non)linear programming. Guided by the mission statement of the journal, we value papers that will appeal to a broad audience of OR community, balancing rigor with practical relevance.
Production Management and Scheduling
Production management and scheduling are certainly among the classics of operations research and have drawn the attention of researchers for many decades. In turn, research in this area has contributed to the development of both the theory and practice of operations research and management science beyond production management and scheduling. The rapid pace of innovation in areas such as industry 4.0, internet of things, additive manufacturing, human-robot cooperation, and artificial intelligence (to name just a few) offer significant opportunities for new advances and continued far reaching impact for production management and scheduling research. The production management and scheduling department seeks to be the home for this research.
We welcome manuscripts laying emphasis on the formulation and analysis of new stylized problems settings or application-motivated problem settings, development and analysis of original algorithmic approaches, customization of generic algorithmic frameworks, or new modelling attempts. Papers should be concise in style and rigorous in their analysis. We expect authors to detail their contribution, put it into perspective with respect to the state of the art, and lay out the premises they start from, i.e., a precise problem definition.
List of topics:
- Warehousing/ Material Handling
- Facility layout/ Capacity planning
- Lot sizing
- (Project, Machine, Logistics) Scheduling
The Stochastic Models area of OR Spectrum aims to publish papers where modeling and analysis of uncertainty are main characteristics. Stochastic Models area welcomes and treats equally papers with modeling, application, theory, or computational focus. Guided by the mission statement of the journal, we value papers that will appeal to a broad audience of OR community, balancing rigor with practical relevance, and offering insights on modeling and optimization of stochastic systems.
Supply Chain Management
The Supply Chain Management Department welcomes original research papers that use innovative methods to study the strategic and operational challenges of managing the flows of products and information across organizations. The consumers' expectations of having personalized, affordable and high-quality products require joint efforts of all supply chain partners to make sure that the products are available at the right moment at the right place. Additional challenges rise due to competition, profitability, and sustainability concerns. The Supply Chain Management Department aims at providing the supply chain partners with practical and scientifically grounded methods, algorithms, and insights to help them tackle these challenges. To this end, the department welcomes submissions on the following (by no means complete) list of topics:
- Supply chain design and coordination
- Sourcing strategies
- Inventory management
- Risk management
- Order fulfillment and distribution strategies
- Demand management
- Digitalization and e-commerce
- Globalization of supply chains
- AI and Machine Learning in SCM
The manuscripts are required to display rigor and managerial relevance. They should address original research problems and help stimulate future research. Significant theoretical and domain-specific contributions are expected.
See the Department Editors and Associate Editors here.