Operational Decision-making Research Articles

Autonomizing Public Universities in Ethiopia: Exploring the Potential Challenges

Background This study examines the challenges faced by Ethiopian public universities in achieving institutional, administrative, academic, and financial autonomy. Institutional autonomy refers to a university’s ability to self-govern, set its mission, and establish policies without external interference. Administrative autonomy concerns decision-making in operations, academic autonomy relates to academic freedom and quality assurance, and financial autonomy involves control over resources and revenue generation. In Ethiopia, centralized governance, political interference, and limited financial strategies create significant barriers to autonomy. Method A qualitative research approach was employed, utilizing semi-structured interviews, key informant interviews, focus group discussions, and direct observations. Data were collected from 59 participants across three public universities. Tools included consent forms, interview guides, focus group discussion guides, and observation checklists. Thematic analysis was used to identify key patterns in the autonomy challenges. The study is grounded in Institutional Theory, Principal-Agent Theory, and Resource Dependency Theory. Result The findings indicate that institutional autonomy is constrained by centralized governance and political interference, limiting universities’ ability to make independent decisions, as explained by Institutional Theory. Administrative autonomy faces similar restrictions, with decision-making influenced by external entities, as highlighted by Principal-Agent Theory. Academic autonomy is challenged by the tension between standardization and institutional diversity, particularly in quality assurance and accreditation. Financial autonomy is limited by restricted revenue sources, economic instability, and a lack of innovative funding strategies, as discussed through Resource Dependency Theory. Conclusion The study concludes that Ethiopian public universities face significant barriers to autonomy. Addressing these challenges requires reducing strict governmental control, improving governance structures, and promoting independent decision-making and sources of incomes. Recommendations The study recommends implementing governance reforms, providing leadership training for university administrators, enhancing academic freedom with flexible quality assurance systems, and diversifying revenue streams through partnerships and expanded university enterprises to ensure financial sustainability.

Evaluating Passing Capacity in High-Speed Rail Hub Stations: Multi-Objective Optimization for Multi-Directional Train Routes

This study presents a comprehensive method for calculating the passing capacity of high-speed rail hub stations, accommodating the complexity of intersecting train paths from multiple directions. Unlike traditional models, this approach distinguishes passing capacity by assessing each train path type individually. Employing a multi-objective framework with the NSGA-III algorithm, we seek to identify optimal trade-offs (Pareto front) in station capacity under different cross-line train configurations. Through detailed numerical simulations with both a small-scale model and Jinanxi Railway Station, the methodology demonstrates its validity and effectiveness. This enhanced capacity representation offers critical insights for strategic operational planning and decision-making in managing railway transport systems.

The Adaptive Human-AI Synergy in Logistics (AHASL) Theory

This paper introduces the Adaptive Human-AI Synergy in Logistics (AHASL) theory, which focuses on integrating artificial intelligence (AI) into logistics and supply chain management. The study takes a qualitative approach, using interviews, observations, and reflexive journaling, and finds that while AI significantly improves operational efficiency and decision-making, human oversight is still required to address AI's limitations, such as bias, lack of transparency, and potential skill erosion. The AHASL model prioritizes Full-Spectrum Explainability (FSE) and a Bias Mitigation Framework (BMF) to ensure that AI-driven decisions are transparent and equitable while simultaneously arguing for retaining human expertise to retain adaptability and resilience in logistical operations. The study's findings emphasize the significance of balanced human-AI collaboration. However, they also call for more research into the long-term effects, scalability, and ethical implications of AI integration in logistics.

China's Belt and Road Initiative: Motivations for Strategic Decision-Making in Global Transport Operations from a Maritime and Marine Engineering Perspective

China’s Belt and Road Initiative (BRI), announced in 2013, is a mega project designed to enhance global transportation infrastructure and stimulate economic development. This study aims to identify and prioritize the key motivational sources driving China's BRI, with a particular focus on economic, political, and strategic considerations. Using PRISMA systematic literature review and focus group methodologies, we identified 26 motivational factors, 16 of which are primarily economic in nature. Among these, 'New business opportunities for Chinese investors' emerged as the most significant driver. In addition to economic motivations, this study highlights the critical importance of marine engineering, ship risk management, and ship machinery innovations in the success of BRI's maritime routes. Safe, efficient maritime transportation is identified as a crucial element for ensuring the operational effectiveness of the Maritime Silk Road. The study further emphasizes the role of maritime safety protocols, infrastructure modernization, and risk mitigation strategies in achieving China's broader geopolitical and economic ambitions through the BRI. Our findings suggest that China’s internal motivations, particularly those related to economic growth and marine transport infrastructure, play a more prominent role than external factors. This underscores the need for policymakers, marine engineers, and transport operators to align their long-term strategies with China’s ambitions, focusing on maritime safety, operational efficiency, and the global competitiveness of China’s marine technologies. The insights gained from this study contribute to a deeper understanding of the strategic importance of maritime infrastructure in shaping the future of global transportation systems under the BRI.

Real-Time Industrial Data Analysis and Visualization

In the era of Industry 4.0, real-time data monitoring and analysis have become essential for enhancing operational efficiency and decision-making in industrial environments. This project focuses on the development of a comprehensive system for visualizing and analyzing real-time industrial data using a DHT11 sensor, which measures temperature and humidity. The collected data is transmitted and visualized through a dynamic web interface built on MyPHPAdmin, enabling users to access and interpret data seamlessly. The visualization aspect of the project aims to present the data in an intuitive format, allowing stakeholders to monitor environmental conditions effectively. Following the visualization phase, the data will undergo thorough analysis using advanced tools like Tableau and Excel, providing insights into patterns, trends, and anomalies in the industrial setting. This project not only demonstrates the integration of hardware and software technologies for real-time data management but also emphasizes the importance of data visualization and analysis in optimizing industrial processes. Ultimately, it serves as a foundation for further research and development in the field of industrial automation and data-driven decision-making.

Advanced Applications and Methodologies in Mathematical Optimization and Operations Research: Insights into Linear Programming, Nonlinear Programming, and Decision-Making Frameworks

Mathematical optimization and operations research are pivotal disciplines in solving complex decision-making problems across industries. This research delves into advanced methodologies within these fields, with a focus on linear programming (LP), nonlinear programming (NLP), and their applications in optimizing processes and resource allocation. Linear programming, with its capacity to model and solve large-scale problems, remains a cornerstone for optimization, particularly in logistics, finance, and manufacturing. Nonlinear programming, characterized by its ability to handle complex, real-world systems with non-convex functions, expands the scope of optimization to include dynamic and intricate challenges such as energy management and machine learning. This study investigates state-of-the-art algorithms, including interior-point methods, dual simplex techniques, and gradient-based approaches, to enhance the efficiency and accuracy of solutions. By addressing theoretical advancements and practical implementations, this research bridges the gap between mathematical rigor and real-world applications. The insights gained are expected to contribute significantly to operational efficiency, strategic planning, and decision-making in diverse fields.

AIOps and DevOps : Catalysts of Digital Transformation in the Age of Automated Operations

This article examines the pivotal roles of Artificial Intelligence for IT Operations (AIOps) and Development Operations (DevOps) as key drivers of digital transformation in contemporary organizations. As businesses increasingly rely on complex, distributed IT systems, the integration of AIOps and DevOps emerges as a critical strategy for maintaining operational efficiency, scalability, and innovation. Through a comprehensive analysis of current literature and industry case studies, we explore how these technologies are reshaping the landscape of IT operations, accelerating software delivery cycles, and enabling more resilient and adaptive infrastructures. The article delves into the evolving nature of release engineering, where automation and machine learning are becoming central to ensuring agile and robust IT systems. Furthermore, we critically assess the ethical implications of automated decision-making in operations, including issues of transparency, bias, and job displacement. Our findings suggest that while AIOps and DevOps offer significant benefits in terms of operational optimization and cost reduction, their successful implementation requires careful consideration of organizational culture, skill development, and ethical guidelines. This article contributes to the growing body of knowledge on digital transformation strategies and provides insights for practitioners navigating the complex intersection of artificial intelligence, automation, and IT operations.

Directors from related industries and corporate diversification

ABSTRACT This paper aims to examine the impact of directors from related industries on corporate diversification. Director social networks, as a form of informal institution, have significant effects on firms’ production, operation, and strategic decision-making. However, few studies have focused on the economic effects of directors from related industries. We select Chinese A-share listed companies from 2012 to 2021 as our research sample and empirically examine the impact of directors from related industries on corporate diversification. The study finds that directors from related industries can promote corporate diversification. Mechanism tests that they can influence corporate diversification through the information channel. Heterogeneity analysis shows that the impact of directors from related industries on corporate diversification is more pronounced in firms with strong absorptive capacity, non-state-owned enterprises, and in regions with poor institutional environments. Additionally, directors from related industries can strengthen the positive correlation between diversification and corporate investment efficiency.

Research on TBM parameter optimization based on failure probability

This study addresses the over-reliance on operator experience in Tunnel Boring Machine (TBM) operations by proposing a failure probability-based decision-making method to enhance the scientific accuracy and reliability of construction decisions. In large deformation soft rock zones, TBMs are significantly affected by surrounding rock stress and deformation, which can lead to reduced tunneling speed or even shield jamming, severely impacting construction progress and safety. To reduce subjective biases in operator decision-making, this study combines the stress release coefficient with a failure probability model to establish a limit equilibrium equation. Using Monte Carlo simulations, failure probabilities under varying geological conditions are evaluated, and the optimal tunneling parameters are selected by analyzing the cumulative effect over time. Additionally, the study incorporates a Bayesian updating method, dynamically adjusting model parameters based on periodic monitoring data, further reducing uncertainty and improving the accuracy of the decision support system. The results show that while higher tunneling speeds increase the instantaneous failure probability, considering the cumulative effect over time, the overall failure index decreases with increased speed. Conversely, lower speeds result in a lower instantaneous failure probability but prolonged exposure to high-risk conditions increases the overall failure index. With this decision-making method, operators can quantitatively and in real-time adjust tunneling parameters under complex geological conditions, minimizing failure risks and improving construction efficiency.

Impact of temperature humidity index-based climate services for Murrah buffaloes of India on operational decision-making and economic outcome of the farm

Climate change is likely to affect both the productive and reproductive functions of Murrah buffaloes, the major milch breed of India. Hence, in the present study, weekly modules of Temperature Humidity Index (THI) based climate services were prepared and disseminated to the farmers of experimental villages of Haryana, a state of North-Western part of India through either WhatsApp, Text SMS, and Mobile Application. Difference-in-Difference (DiD), a quasi-experimental research design was used to study the impact of climate services on operational decision-making as well as the economic outcome of the farm. The findings of the study revealed that the climate services had a positive effect on the number of farmers adopting the practices like use of oil cakes, minerals, and feed additives in the animal diet. Treatment effect was found to be significant on quantity of oilcake (0.39, 0.45 and 0.51 kg/animal/day); concentrates during both summer (0.48, 0.56, 0.59 kg/animal/day) and in winter (0.35, 0.40 and 0.42 kg/animal/day); and mineral mixture (9.47, 12.34 and 13.08 gm/animal/day) in Text SMS, WhatsApp and MobileApp group, respectively. The treatment effect of climate services on economic outcome indicators such as summer milk yield was also found to be significant (0.38, 0.44, and 0.50 L/animal) from Text SMS, WhatsApp, and MobileApp, respectively. Overall, the benefit-to-cost ratio of climate services was found to be 1:1.8. Hence, the exclusive climate services developed for Murrah buffalo farmers were found to be an effective adaptive mechanism for helping vulnerable dairy farming to adapt to the climate of today and of the future.

Hybrid Decision-Making for Intelligent High-Speed Train Operation: A Boundary Constraint and Pre-Evaluation Reinforcement Learning Approach

Hybrid Decision-Making for Intelligent High-Speed Train Operation: A Boundary Constraint and Pre-Evaluation Reinforcement Learning Approach

A Hybrid Fuzzy Mathematical Programming Approach for Manufacturing Inventory Models with Partial Trade Credit Policy and Reliability

This study introduces an inventory model for manufacturing that prioritizes product quality and cost efficiency. Utilizing fuzzy logic and mathematical programming, the model integrates fuzzy numbers to describe uncertainties associated with manufacturing costs and quality control parameters. The model extends beyond conventional inventory systems by incorporating a dynamic mechanism to halt production, employing fuzzy decision variables to optimize the economic order quantity and minimize total costs. Key innovations include the application of approaches related to graded mean integration for defuzzification and the use of Kuhn–Tucker conditions to ensure optimal solutions under complex constraints. These approaches facilitate the precise management of production rates, inventory levels, and cost factors, which are essential in achieving a balance between supply and demand. A computational analysis validates the model’s effectiveness, demonstrating cost reductions while maintaining optimal inventory levels. This underscores the potential of integrating fuzzy arithmetic with traditional optimization techniques to enhance decision making in inventory management. The model’s adaptability and accuracy indicate its broad applicability across various sectors facing similar challenges, offering a valuable tool for operational managers and decision makers to improve efficiency and reduce waste in production cycles.

A 4IR-Driven operational risk model for CO2 storage in deepwater abandoned hydrocarbon reservoirs

This study presents the development of an advanced operational risk model that leverages Fourth Industrial Revolution (4IR) technologies to optimize carbon dioxide (CO2) storage within deepwater abandoned hydrocarbon reservoirs. The model systematically combines Artificial Neural Networks (ANN) with the optimization capabilities of Genetic Algorithms (GA) and the probabilistic analysis strengths of a Bayesian Network (BN) to perform dynamic and comprehensive risk assessments. By applying the model to a dataset covering a 200-year timeframe, it effectively forecasts CO2 storage capacities while simultaneously evaluating associated risks across different operational scenarios. One of the key innovations of this model is the introduction of a novel loss function designed to precisely manage forecast deviations and enhance the efficiency of operational processes. This function is critical in ensuring that the model remains robust and accurate in real-time risk assessments, allowing for more reliable decision-making in CO2 storage operations. In addition, the study conducts an economic evaluation that underscores the crucial role of 45Q tax credits in bolstering the financial sustainability of carbon sequestration projects. The analysis highlights how these credits significantly reduce the economic barriers to adopting carbon utilization, storage, and sequestration (CUSS) technologies, making large-scale implementation more feasible. The model's performance is underscored by its ability to achieve a 49% CO2 retention rate over two centuries, with an impressively low average error margin of 0.249%. These results highlight the model's impressive efficiency and accuracy, while also demonstrating its capacity to markedly improve the predictability of CO2 storage outcomes. The findings suggest that this model could play a pivotal role in advancing global sustainability efforts by optimizing CO2 storage processes, thereby contributing to the reduction of atmospheric CO2 levels and supporting long-term climate goals.

Model uncertainty quantification of a degradation model of miter gates using normalizing flow-based likelihood-free inference

Degradation modeling is essential for failure prognostics of miter gates and subsequent risk-informed maintenance or operational decision-making. Typically, degradation models are formulated based on certain assumptions and simplifications and may not fully capture the complexity of underlying degradation mechanisms. Even minor errors in such models may lead to significant discrepancies in failure prognostics due to error accumulation over time. Aiming to improve the accuracy of the degradation model and ensure reliable failure prognostics, this article proposes a degradation model updating framework for miter gates using normalizing flow-based likelihood-free inference, accounting for both model parameter uncertainty and model form uncertainty (i.e., model bias). The proposed work consists of two phases: the offline training phase and the online updating phase. During the offline training phase, the unknown model bias term is modeled as a random noise with uncertain standard deviation. Synthetic data are then generated using a physical model based on prior knowledge of the uncertain model parameters and model bias. The synthetic data are utilized to train an inference model, which has a summary network for data compression and a conditional invertible neural network for parameter estimation. In the online updating phase, the trained inference model uses strain observations to obtain posterior samples. The Gaussian mixture model and dual particle filter techniques are utilized to recursively update posterior samples, thereby improving the estimation accuracy. Subsequently, model bias is inversely estimated using the degradation model, which uses the updated model parameters and the gap length from the inference model. Following that, a regression model is constructed to correct the biases inherent in the simplified degradation model. This process is implemented iteratively over time to perform continuous model updating and failure prognostics. Results of a case study demonstrate the efficacy of the proposed framework in reducing both model parameter uncertainty and recovering model biases and thereby improving the accuracy of failure prognostics of miter gates.

The Impact of Digitalization on Maritime Operations: Exploring Emerging Technologies in Management and Education

This research explores the transformative impact of digitalization on maritime operations, focusing on emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain. Through qualitative research and descriptive analysis, perspectives from maritime professionals, lecturers, and graduates were examined to understand the integration of these technologies in maritime management, operations, and education. The findings reveal that digital tools significantly enhance operational efficiency, decision-making, and sustainability while highlighting challenges in workforce training and the gap between theoretical knowledge and practical application in maritime education. Despite the benefits, full optimization of these technologies is still developing due to varied adoption levels across the industry. The research emphasizes the need for investment in infrastructure, continuous professional development, and educational reform to fully leverage digitalization in the maritime sector.

ESG Performance and Enterprise Competitive Advantage Construction: A Case Study of Walmart

This essay opens with an in-depth discussion of the growing importance of ESG concepts in modern corporate management and highlights their significant role in promoting long-term corporate value creation. With the increasing global focus on sustainability, ESG concepts have become an integral part of corporate strategic planning and operational decision-making. Next, this article selects the global retail giant Walmart as a case study and uses SWOT analysis and other management tools to analyse in detail how Walmart has effectively constructed and consolidated its competitive advantages in the highly competitive market through the implementation of an active ESG strategy. The article specifically describes Walmart's practices on several ESG dimensions, including but not limited to the implementation of environmental protection measures, active support to the community, the enhancement of employee welfare, and the optimisation of corporate governance structure. In terms of environmental protection, Walmart has significantly reduced the environmental impact of its operations through measures such as reducing carbon emissions, improving energy efficiency and promoting sustainable products. In terms of community support, the company has strengthened its ties with local communities and enhanced its brand image through various community programmes and charitable activities. Employee well-being has been achieved through improved working conditions and career development opportunities, which have not only increased employee satisfaction but also enhanced the company's attractiveness. The article concludes by summarising the important contribution of Walmart's ESG practices to the company's sustainability and long-term competitive advantage and suggests possible future strategic directions and recommendations for Walmart in ESG based on current market trends and company performance. These recommendations are intended to provide valuable references and insights for Walmart and other companies in their future strategic planning and decision-making processes, especially in terms of how to balance economic efficiency and social responsibility to achieve long-term sustainable development. However, due to the limitation of data and the depth of research on Walmart ESG, the article still has limitations.

Functioning in the hot seat: exploring the competencies of police incident commanders

PurposeIncident commanders (ICs) are senior police officials tasked with being the key operational decision-makers and leading a command team during major, critical and public order incidents (e.g. bomb threats, hostage situations and protests). Such events are often characterized by heavy time pressures and little information, requiring a highly skilled, integrated emergency response across multiple agencies and significant police planning, organizing and management. ICs must possess the necessary skills, traits and behaviors to deal with such incidents and their challenges. Therefore, the purpose of this study is to identify the competencies of ICs.Design/methodology/approachWe conducted a thematic analysis of thirty-eight (n = 38) in-depth interviews with police personnel who have professional experience as ICs.FindingsResults provided information on the competencies important for effective incident command, including a range of skills, traits and behaviors these police personnel should exhibit, such as command presence, decision-making, confidence, risk assessment, teamwork, task management, stress management, humility and others.Originality/valueWe provide a novel literature contribution by proposing a framework of police IC competencies and their interrelatedness based on first-hand interviews with experts in the field. Practitioners and policymakers need to know actionable strategies for developing standardized training and assessment curricula for ICs. The next steps are to delineate what skills, traits and behaviors are trainable and need to be selected for at the time of recruitment (e.g. occupational aptitude, personality). We provide the foundation upon which researchers and practitioners can make actionable decisions on moving forward to attain these important goals.

Data analytics for visualization of business insights for an online retail store using Python

In today’s world, data is becoming an organization's most important asset. Businesses have easy access to large amounts of data from a range of sources, and when those data are processed quickly and reliably, the chances of obtaining useful insights to inform data-driven decision-making are significantly increased. Modern corporate strategy must include data analytics because it offers critical insights that improve operational efficiency and decision-making. Using data analytics to visualize company insights can significantly improve a number of areas for an online retail store, including marketing strategy, customer experience, inventory management, and sales. This paper aims a thorough examination at the main areas, methods, and approaches that may be used to delve into business insights using data analytics, as well as the advantages it has for online retailers using a sample online retail dataset.. Keywords: Data Analytics, Data Visualization, Inventory, Sales Analysis, Descriptive Analytics.

Leveraging explainable machine learning for enhanced management of lake water quality

Freshwater lakes worldwide suffer from eutrophication caused by excessive nutrient loads, particularly nitrogen (N) and phosphorus (P) from wastewater and runoff, affecting aquatic life and public health. Using a large (1800 km2) subtropical lake as an example (Lake Okeechobee, Florida, USA), this study aims to (1) predict key water quality parameters using machine learning (ML) algorithms based on easily measurable variables, (2) identify spatial patterns of these parameters, and (3) determine environmental drivers influencing turbidity levels. The study employs four ML algorithms—Extreme Gradient Boosting (XGB), Light Gradient-Boosting Machine (LGBM), Support Vector Regression (SVR), and Random Forests (RFs)—to predict total phosphorus (TP), total nitrogen (TN), nitrate + nitrite (NOx-N), and turbidity, via station-specific and lake-wide modeling approaches. The station-specific models uncover spatial patterns, while the lake-wide models support operational decision-making. Results indicated that lake stage (water level), water temperature, and, most notably, turbidity were the main nutrient predictors, with XGB demonstrating superior prediction performance. Spatial analysis using K-means clustering identified three distinct lake regions based on nutrient levels and turbidity. Due to its importance, SHapley Additive exPlanations (SHAP) were employed to identify and quantify environmental factors affecting turbidity. Inflows and lake stage were found as primary drivers of turbidity near lake inlets, while wind speed and air temperature affected turbidity in the middle of the lake. This research advances the understanding of lake water quality dynamics, emphasizing the importance of frequent monitoring of turbidity and its environmental drivers for enhanced management and future mitigation efforts.

A Dynamic System to Control the Entry of Non-Authorized Visitors and Detect Superspreader Farms in Strongly Interconnected Systems.

This study explores the critical challenges the livestock sector faces, particularly those related to biosecurity, animal welfare, and antibiotic use restrictions. It highlights the need to implement advanced information and communication technologies to enhance operational sustainability and decision-making. We introduce the Biorisk® External platform, a cloud-based visit control system designed to optimize biosecurity management by accurately tracking visitor activity through QR codes and GPS geolocation. During a 6-month study period from July to December 2023, we analyzed visits to 142 different swine production sites and 30 vehicle movement patterns. The analysis revealed trends in visitation patterns and compliance with biosecurity SOPs. The software categorized visits as authorized (A), not authorized with access (NAWA), and not authorized without access (NAWOA), providing a framework to assess biosecurity risks. Additionally, network analysis identified interconnected farms, which were classified as 'superspreaders', highlighting their considerable risk of disease transmission. This study advocates for the integration of digital systems in livestock operations to improve biosecurity measures, facilitate real-time data input, and support informed decision-making. By enhancing biosecurity protocols through technology, the livestock industry can better safeguard animal health, increase operational efficiency, and reduce potential economic losses associated with disease outbreaks.