Operations Research Research Articles

A note on closed-form spread option valuation under log-normal models

In the papers Carmona and Durrleman [Pricing and hedging spread options in a log-normal model. Technical report: Department of Operations Research and Financial Engineering, Princeton, NJ, Princeton University, 2003] and Bjerksund and Stensland [Closed form spread option valuation. Quant. Finance, 2014, 14(10), 1785–1794], closed-form approximations for spread call option prices were studied under the log-normal models. In this paper, we give an alternative closed-form formula for the price of spread call options under the log-normal models also. Our formula can be seen as a generalization of the closed-form formula presented in Bjerksund and Stensland [Closed form spread option valuation. Quant. Finance, 2014, 14(10), 1785–1794] as their formula can be obtained by selecting special parameter values for our formula. Numerical tests show that our formula performs better for a certain range of model parameters than the closed-form formula presented in Bjerksund and Stensland [Closed form spread option valuation. Quant. Finance, 2014, 14(10), 1785–1794].

Enhancing Decision Support Systems with Hybrid Machine Learning and Operations Research Models

Enhancing Decision Support Systems with Hybrid Machine Learning and Operations Research Models

Does increasing absolute conditioned reinforcement rate improve sensitivity to relative conditioned reinforcement rate?

Previous research has demonstrated several procedural modifications that improve the sensitivity of human behavior to relative rates of conditioned reinforcement or S+ production. Denser rates of reinforcement have proved useful in related human operant research, but the influence of denser rates of S+ production has yet to be evaluated. The purpose of the current study was to evaluate how the absolute S+ production rate influenced sensitivity to relative S+ production rate. Thirty undergraduate students were randomly assigned to three groups for which the absolute rate of S+ production varied but the programmed relative rate of S+ production was held constant across groups. Results similar to those of previous research were obtained with many participants; however, the absolute rate of S+ production exerted no systematic effect on sensitivity or the quality of fits of the generalized matching equation. Exploratory analyses suggest that methods ensuring steady-state responding and improving the predictive value of S+ are important directions for future research.

Navigating the Future: Examining Sustainable and Resilient Drivers Shaping the Integration of Crowdshipping in E‐Commerce Logistics

ABSTRACTThe last mile is often subjected to unique challenges such as congested urban areas, unpredictable customer availability and the need for personalized services. To address these complexities, and to be competitive in the market, companies are increasingly turning towards innovative solutions. One such innovative solution is crowdshipping (CS). The concept of CS was first introduced by Walmart in 2013, where store customers may act as crowdshippers and fulfill the demands of online retailers or shops by transporting packages on their way home with guaranteed compensation. To understand the factors influencing CS and its potential to motivate logistics firms for delivery, this research work determines the 20 key sustainable and resilient factors affecting the adoption of CS in the e‐commerce logistics sector. Furthermore, the Neutrosophic Best‐Worst Method (NBWM) is employed to prioritize these factors, followed by the application of Neutrosophic Interpretive Structural Modeling (NISM) with cross‐impact matrix multiplication applied to classification (MICMAC) analysis to assess the interrelationships amongst the factors. The results of the NBWM reveal that technology for the successful implementation of CS, rating and reward system, delivery cost–time efficiency, CO2 emissions and service quality are the top five highly weighted factors. The results of NISM dictate the technology for the successful implementation of CS, integration of CS with the mainstream (operations research models), government interventions and the usage of back‐up drivers as the factors with the highest driving power. The proposed NBWM and NISM give direction to practitioners and policy managers in planning sustainable and resilient strategies for addressing CS implementation factors in the e‐commerce logistics sector. Further, competent strategies can be developed by the managers based on the prioritization and the driving and dependence power of CS adoption factors.

Operational Research Models for E-Commerce Platforms: New Technology Innovation

New technology innovations and operational research (OR) models can enhance the efficiency and competitiveness of e-commerce platforms. The rapid expansion of e-commerce has led to increased complexity in operations, including dynamic pricing, inventory management, last-mile delivery, and fraud detection. This special issue features nine articles exploring how advanced OR models, such as game-theory-based approaches, machine learning, and big data analytics, offer solutions to these challenges by enabling platforms to make data-driven decisions. As e-commerce evolves, these technological advancements, combined with OR models, will continue to drive platform innovation, improve efficiency, and strengthen resilience in the face of disruptions and fluctuating market demands.

ANALYSIS OF THE IMPACT OF TASK PRIORITIZATION LISTS ON THE POTENTIAL FOR AVOIDING ANOMALIES IN TASK SCHEDULING

This paper addresses relevant issues related to the anomalous deterioration in objective function values when attempting to improve the initial parameters in one of the discrete optimization problems. The primary focus is on investigating the conditions under which it is possible to prevent the occurrence of such anomalies. Contemporary scientific works devoted to schedule optimization and task prioritization management, particularly for location-allocation problems arising in the fields of computer science, engineering, and operations research, are reviewed. A priority dynamic redistribution algorithm is proposed, which al-lows minimizing delays and ensuring efficient resource utilization during parallel task execu-tion. An example of applying the algorithm is provided, and its effectiveness in preventing anomalies.

A Systematic Literature Review of Trauma Systems: An Operations Management Perspective.

Trauma systems provide comprehensive care across various settings, from prehospital services to rehabilitation, integrating clinical and social care aspects. Established in the 1970s, these systems are pivotal yet under-researched in their operational management. This study aims to fill this gap by focussing on the integration of operations management (OM) techniques to enhance the efficiency and effectiveness of trauma systems. By leveraging proven OM strategies from other healthcare sectors, we seek to improve patient outcomes and optimise system performance, addressing a crucial need for innovation in trauma care operations. A systematic literature review was conducted using the PICOTS framework to explore operational aspects of trauma systems across varied settings, from emergency departments to specialised centres. Searches were performed in 5 databases, focussing on articles published from 2006 to 2024. Keywords related to operational research and management targeted both trauma systems and emergency management services. Our method involved identifying, synthesising, and summarising studies to evaluate operational performance, with a specific emphasis on articles that applied operational research/management techniques in trauma care. All eligible articles were critically appraised using 2 quality assessment tools. Employing Donabedian's framework to analyse the quality of trauma systems through structure, process, and outcome dimensions, our systematic review included 160 studies. Of these, 5 studies discussed the application of the Donabedian evaluation framework to trauma systems, and 14 studies examined structural elements, focussing on the location of healthcare facilities, trauma resource management, and EMS logistics. The 63 studies on process indicators primarily assessed triage procedures, with some exploring the timeliness of trauma care. Meanwhile, the 78 outcome-oriented studies predominantly evaluated mortality rates, alongside a smaller number assessing functional outcomes. Existing evaluation metrics primarily focussed on triage accuracy and mortality are inadequate. We propose expanding these metrics to include patient length of stay (LOS) and rehabilitation trajectory analyses. There is a critical gap in understanding patient flow management and long-term outcomes, necessitating focussed research on LOS modelling and improved rehabilitation data collection. Addressing these areas is essential for optimising trauma care and improving patient recovery outcomes.

Nutrition Care Model for Nutritional Assessment and Dietary Counseling of Adults Living with HIV in India

Abstract Background: A high prevalence of undernutrition (40%) exists among adults living with HIV (ALHIV) in India due to their increased metabolic demands. Diminished nutritional status in turn has been associated with increased risk of morbidity and mortality among ALHIV. Nutritional assessment and its management are an integral part of HIV care; however, no strategy exists for screening of undernutrition among ALHIV. Materials and Methods: The nutrition care model has been developed for undertaking nutritional assessment by classifying the severity of undernutrition based on body mass index (BMI) and identification of early signs of undernutrition including anemia and hypoalbuminemia among ALHIV. Results and Conclusion: The model suggests energy and protein intake based on the BMI and indicates desirable body weight corresponding to a BMI of 21 kg/m2. Based on these requirements, the portion size of each major food group is provided for individualized dietary counseling by frontline workers. ALHIV found with signs and symptoms of anemia, low hemoglobin, and serum albumin are referred to the nearest health facility. Large-scale operational research and field validation studies are planned to better understand the effectiveness of the model in improving the nutritional and clinical outcomes and the strengths and limitations as an operational tool in programmatic settings.

Dealing with complexity – the use of problem structuring methods in management science

Purpose This paper aims to examine the current level of application of problem structuring methods, a distinct domain within the wider analytical decision-support discipline of operational research (OR), in dealing with complex problem situations in organisations and societies. Design/methodology/approach The paper has reviewed the relevant literature on problem structuring methods by analysing articles on practical applications that have been published in discipline-based journals over the past 15 years. The aim was to identify the extent to which OR practitioners use particular problem structuring methods and tools and the way that this is done in OR interventions. Findings The results show that the main problem structuring methods and tools have maintained their popularity and are frequently used as analytical approaches within a facilitated modelling environment. At the same time, new methods have been proposed, and these are typically integrated within multi-methodological frameworks that often include the use of more traditional OR techniques. On the other hand, even though problem structuring methods are now quite well-established in Europe and in other parts of the world, they do not seem to enjoy the same level of acceptance or recognition in the USA. Originality/value The paper provides an up-to-date account of the application of problem structuring methods as a means of handling increased problem complexity and highlights the contribution that this important field of work can make in dealing with the complex problems and challenges the world faces in the current turbulent environment.

Optimizing Railway Construction Project through Phase-Based Planning

This paper explores the integration of phase-based planning in the development and maintenance of railway infrastructure, with a specific focus on the Katni railway project. The study emphasizes the importance of strategic planning in optimizing project timelines, cost efficiency, and safety, particularly in large-scale railway projects. The research delves into the challenges faced in railway infrastructure development, including complexities in construction, maintenance, and resource allocation, and addresses how phase-based planning methodologies can provide effective solutions. Key aspects of railway infrastructure, such as the construction of ballasted track systems, the overhead catenary system (OHE), and maintenance practices, are examined to demonstrate their role in ensuring long-term performance and sustainability. The methodology section outlines advanced techniques in planning and categorization of maintenance activities such as routine, preventive, and predictive maintenance, along with the critical factors impacting railway track conditions. The phase-based decision support system is explored, highlighting its potential to streamline planning, mitigate risks, and ensure on-time project completion without accidents. The optimization of railway construction phases, through technical, economic, and constrained optimization models, is discussed in detail, showcasing the integration of operations research to enhance efficiency. The results of the study focus on the Katni Railway project, offering insights into the advantages of strategic planning, comprehensive execution, financial performance, and resource allocation based on phase-based planning methodologies

A Fresh Perspective on Permanent and Retrieved Borrowing Programs Competitiveness: A Journey of Operation Research

Based on the application of businesses research methodology to the finance industry, this study offers a novel method for evaluating the projected To determine the optimal refinancing rate of returns that is likely to increase the bank's expected total revenues, the estimated worth of all revenues for variable and adjustable home loans is calculated. The investigation considers the impact of commonplace, deposits, bankruptcies, and financing rates on bank profitability and emphasizes balancing between potential gains and losses while selecting the lending rate. To ascertain the loan amount lowest reduces the banking institutions expected whole right away. We therefore used mathematics approaches to analysis in addition to fixed-rate or benchmark mortgages models incorporating mortgage rates as the deciding parameter. Findings suggested that although cancelation produced a detrimental effect on earnings of banks, increases in popularity, interest rates, and loan amounts had an advantageous impact. The study emphasizes how crucial it is to thoroughly consider all of the variables that affect income in order to choose the best loan rate that will maximize profitability. With ramifications for cost-benefit studies, fixed-rate and index mortgages, lending rates, fails to repay, and optimum earnings, the findings offer insightful information about the ideal home loan payment as well as the elements that impact a bank's revenue and profitability. This investigation adds to the body of knowledge already available on housing products. It suggests maximizing housing financing costs for greatest shareholder return by taking consideration of the loan rate, demand, and expiration impacts. It also offers banks useful suggestions on handling their home loan programs effectively in order to improve the way they operate.

Examination of risks in circular supply chains using transition management lens: towards a circular economy in emerging markets

We perform a multidimensional and integrated investigation of risks associated with circular supply chains (CSC), drawing on Transition Management Theory (TMT). This research focuses on e-waste from the Indian electronics industry, a waste stream with significant recovery potential and one of the fastest-growing in emerging economies. Drawing on TMT, the study (i) institutionalises risk management activities in circular systems to operationalise the transition towards CE; (ii) quantifies CSC risks at operational, tactical, and strategic levels and measure the total risk exposure of CSCs; (iii) comprehensively cogitates the operational, socio-environmental, and financial implications of CSCs risks and (iv) considers uncertainty in operations research (OR) models by applying a fuzzy set theory, evidential reasoning algorithm, and expected utility theory based model to evaluate and profile the CSCs risks. The proposed model contributes to the application of decision analysis and risk analysis approaches in the sustainability domain and can efficiently model uncertain, subjective, and incomplete data. Our findings reveal that customers’ reluctance to purchase reprocessed products represents the most critical challenge to the effectiveness of CSCs. Furthermore, contrary to conventional perspectives, organizations are strategically shifting toward adopting circular practices. However, they often lack the practical means and resources to implement these strategies effectively.

Operation Research — The Influence of Venture Capital on the Growth of E-Commerce Listed Enterprises

Venture capital (VC) is essential for the growth of small and medium enterprises (SMEs) in developed e-commerce countries. SMEs play a crucial role in the economic development of both developing and developed countries. This paper proposes the influence of VC on the growth of e-commerce in SMEs using the propensity score matching (PSM) method. PSM is defined as the statistical analysis of observational data, where a statistical matching method attempts to evaluate the impacts of a treatment or policy. The experimental results show that the effect of e-commerce on SMEs who received VC financing experience improves the performance of employment growth and sales. Our findings reveal that VC significantly enhances employment rates and sales performance in e-commerce sectors. The study underscores the critical role of VC in driving innovation and growth within SMEs, providing valuable insights for investors and policymakers aiming to foster economic development.

Integrating Systems Methodologies for Australian Undersea Surveillance: A Systematic Literature Review

ABSTRACTAustralia is a maritime nation, relying on access to its surrounding oceans for its economy and security. The Royal Australian Navy conducts undersea surveillance to monitor this vital maritime environment and gain insight into activities beneath the ocean's surface. Australia's maritime jurisdiction is vast, with the world's third‐largest Economic Exclusion Zone, but its resources are constrained, with only 5% of the United States’ defense budget and navy personnel. Fortunately, advancements in unmanned vehicles and networked sensors can address this disparity by increasing the capacity of Australia's naval fleet at a fraction of the cost of manned platforms. However, current applications of unmanned systems only address simplified scenarios without defining how the plethora of unmanned systems should be applied across the full spectrum of surveillance operations. A new naval fleet architecture must be designed using systems methodologies to integrate unmanned systems into this holistic “system of systems”. Systems engineering, systems architecture, system modeling, system dynamics, operations research, and design for Six Sigma are complementary methodologies with unique techniques to solve this complex problem. This systematic literature review critically analyzed current unmanned systems and the utility of systems methodologies to design their application in undersea surveillance. It contributes insights into the benefits and challenges of unmanned systems in Australia's maritime context, whilst demonstrating shortcomings with the currently disparate application of methodologies to design unmanned fleet architectures. Further research is justified to design an integrated framework of systems methodologies that designs fleet architectures for Australian undersea surveillance.

Is Artificial Intelligence a Game-Changer in Steering E-Business into the Future? Uncovering Latent Topics with Probabilistic Generative Models

Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies.

SAT-GATv2: A Dynamic Attention-Based Graph Neural Network for Solving Boolean Satisfiability Problem

We propose SAT-GATv2, a graph neural network (GNN)-based model designed to solve the Boolean satisfiability problem (SAT) through graph-based deep learning techniques. SAT-GATv2 transforms SAT formulas into graph structures, leveraging message-passing neural networks (MPNNs) to propagate local information and dynamic attention mechanisms (GATv2) to accurately capture inter-node dependencies and enhance node feature representations. Unlike traditional heuristic-driven SAT solvers, SAT-GATv2 adopts a data-driven approach, learning structural patterns directly from graph representations and providing a complementary framework to existing methods. Experimental results demonstrate that SAT-GATv2 achieves an accuracy improvement of 1.75–5.51% over NeuroSAT on challenging random 3-SAT(n) instances, highlighting its effectiveness in handling difficult problem distributions, and outperforms other GNN-based models on SR(n) datasets, showcasing its scalability and adaptability. Ablation studies validate the critical roles of MPNNs and GATv2 in improving prediction accuracy and scalability. While SAT-GATv2 does not yet surpass CDCL-based solvers in overall performance, it addresses their limitations in scalability and adaptability to complex instances, offering an efficient graph-based alternative for tackling larger and more complex SAT problems. This study establishes a foundation for integrating deep learning with combinatorial optimization, emphasizing its potential for applications in artificial intelligence and operations research.

A New Method to Find the Balanced Solution to the Transportation Problem with Fuzzy Sets of Type-2 for Supply and Demand

The Transportation Problem is a fundamental problem widely studied in the Operational Research domain. The main objective of this paper is to contribute to a deeper study of the topics of fuzzy transportation problems of linear programming when the supply and demand are represented as a fuzzy set of type-2. Here, we are using a newly proposed method to solve a transportation problem with fuzzy sets type-2 of supply and demand to find a balanced solution. The membership function of a fuzzy set of type-2, which is the set of its feasible solutions, is constructed. The properties of this set are investigated, and the problems of choosing balanced solutions are considered. The newly proposed method of the fuzzy transportation problem is discussed with the help of an illustrative numerical example.

Stochastic Processes and Their Applications: In Honor of Prof. Sally McClean

Stochastic processes are foundational tools in many scientific disciplines, including biology, operational research, social sciences, and stochastic finance, among others [...]

Active guidance systems in maritime evacuations: optimizing the lifeboat allocation for cruise ships

Abstract We study the evacuation of large cruise vessels under hazardous conditions. Based on a new sensor mesh technology, allowing wireless two-way communication also when electricity is lost, we propose an optimization-based procedure that provides real-time guidance to passengers. As a first step, we explore a lifeboat assignment procedure to illustrate some of the aspects linked to pedestrian evacuations on vessels and open the discussion for future work using operations research in a context where human behavior is part of the problem.

Integrating life cycle assessment into supply chain optimization.

Integrating Supply Chain Optimization (SCO) with Life Cycle Assessment (LCA) is essential for creating supply chains that are both economically efficient and environmentally sustainable. While SCO focuses on optimizing network structures and decisions related to product and service delivery, LCA systematically assesses the environmental impacts across the entire supply chain. The existing literature treats SCO and LCA as separate, sequential steps, often leading to inconsistencies in scope and challenges in data transfer and rescaling. Our research presents a novel Supply Chain Life Cycle Optimization (SCLCO) model that integrates SCO and LCA. Our SCLCO model is based on LCA data structures, incorporates multi-time period, closed-loop SCO decisions (e.g. reverse chain management, inventory control, network design), and is capable of considering the three pillars of sustainability: environmental, economic, and social. It includes harmonizing principles, terminology, and notation, thereby bridging the gap between the SCO and LCA communities through a generalized formulation. Computational experiments on a selected SCO model from Operations Research literature validate the SCLCO and demonstrate its effectiveness in providing valuable insights to both SCO and LCA practitioners and researchers. The results emphasize that the simultaneous execution of SCO and LCA in SCLCO minimizes the risk of overlooking decision impacts and facilitates data transfer from existing LCA databases.