The Nigerian Journal of Operations Research (NIJOR) publishes high-quality, peer-reviewed original papers contributing to the field and practice of Operations Research, Management Sciences, Data Science, Industrial Engineering, Decision Analytics and Intelligent Computing.
Operations Research Institute for Decision Sciences & Analytics of Nigeria
Copyright (c) 2026 Nigerian Journal of Operations Research
Table of Contents for Volume 3No. 1 3(1)
A Constrained Demand-Scan Algorithm for the Maximum Sustainable Yield of an Aged Institutional Water Distribution Network
Water distribution networks at expanding African universities are frequently constrained not by design parameters but by the fixed geometry of existing, ageing infrastructure. The conventional pipe sizing problem choosing diameters that satisfy a known demand is therefore inverted: given immovable pipe diameters, lengths and materials, what is the largest daily volume the network can supply while every junction satisfies a 15 m minimum service pressure and every pipe stays below 1.80 m/s velocity. This paper formulates this capacity-frontier problem as a constrained nonlinear optimisation governed by the Hazen–Williams head-loss equation and proposes a coarse fine demand-scan algorithm to solve it on commercially available hydraulic engines. The procedure was applied to the existing water network of Modibbo Adama University, Yola (Adamawa State, Nigeria), a three-reservoir, 18-junction, 20-pipe branched system of 3,454 m total length comprising 15 cast-iron, 3 ceramic and 2 PVC sections. With aged Hazen–Williams coefficients (C = 100, 105 and 135 respectively), the algorithm converged in two scan levels to an optimum daily yield of 1,825 m³/day (21.12 L/s average; 31.68 L/s peak) using 34 calls to the hydraulic solver. At this yield the minimum junction pressure is 15.20 m and the maximum pipe velocity is 1.55 m/s, both just inside the criteria. A three-axis sensitivity analysis demonstrated that the network operates at the very edge of its hydraulic envelope: acting individually, a 5 per cent deterioration in pipe roughness, a 5 per cent growth in demand, or a 2.5 m fall in reservoir level each drives multiple junctions below the pressure criterion. Specifically, the pressure margin is exhausted by a fall in the length-weighted composite C-factor of about 5 per cent (from 104 to 99), by demand growth beyond about 1,825 m³/day, and by a reservoir draw-down of 2.5 m. The result formally quantifies the well-known supply shortfall and provides a transferable algorithm for sustainable-yield assessment of similarly aged distribution networks.
Ultimate Analysis of Charcoal Briquette Produced using Banana and Locust Bean Peels as Binders
This study aims to evaluate the ultimate analysis of charcoal dust briquette (CCD), banana peels (BNP) and locust bean peels (LBP) as sustainable alternatives to conventional fuels. The objective of this work is to analyse the ultimate (composition) of the briquettes. The study involved producing charcoal dust briquettes with varying concentrations of BNP and LBP as binders, followed by detailed analysis of their composition. The methodology entailed the preparation of the briquettes from charcoal dust (CCD) with BNP and LBP as binders in different ratios. The ultimate analysis assessed elemental composition including the percentage of carbon, hydrogen, nitrogen, oxygen, and sulphur content and their average values are: 57.4% ± 3.9% (BNP) and 58.6% ± 1.8% (LBP), 5.4% ± 0.7% (BNP) and 4.7% ± 1.7% (LBP), 4.1% ± 1.3% (BNP) and 4.8% ± 1.6% (LBP), 26.9% ± 4.2 % (BNP) and 26.2% ± 4.2% (LBP) and 6.1% ± 2.5% (BNP) and 5.7% ± 3.0% (LBP) respectively. The ultimate analysis revealed higher carbon content and energy potential in LBP briquettes, making them a more viable alternative for energy production. LBP-based briquettes demonstrate significant advantages over BNP-based briquettes in terms of energy content and performance. The produced samples are not suitable for domestic application but rather industrial usage were sulphur and Nitrogen capture mechanism as in place to reduce the release of such oxides to the atmosphere. The study recommends prioritizing LBP in briquette production and further optimizing the briquette manufacturing process to enhance quality and efficiency. There is a need for further studies on the cause of high sulphur and nitrogen content and the variation of pressure, particle size distribution, and densification temperature.
Towards Real-Time Network Intrusion Detection: An Enhanced Feature Selection Framework Using Variance Analysis and K-Means Clustering
The cybersecurity threat landscape is increasingly dynamic, making Network Intrusion Detection Systems (NIDS) essential to cyber defense. NIDS face challenges such as network traffic imbalance, high false alarm rates, and suboptimal detection accuracy. To address these issues, we integrated variance analysis and k-means clustering, thereby enhancing computational efficiency across seven machine learning models evaluated on the UNSW-NB15 and NSL-KDD datasets. Key metrics included Accuracy, Precision, Recall, F1-Score, Model Training Time (MTT), and Inference Time (IT). Our feature selection technique achieved notably lower MTT and IT than existing methods, particularly with the XGB and RF models, which saw MTT drop by 80% and IT by 60% compared to the baseline. This efficiency is pivotal for real-time intrusion detection and resource-constrained NIDS, enabling swift threat response and optimized resource use. These computational gains emphasize the technique's potential for real-time network security.
A Mamdani Fuzzy Inference System for Intelligent Greenhouse Climate Control: Integrating Temperature, Humidity, and Soil Moisture for Adaptive Precision Agricultural Decision-Making
Climate management in the greenhouses is a non-linear, multi-variable control problem in which air temperature, relative humidity, and soil moisture interact in ways that conventional threshold-based systems cannot effectively resolve. This study designs, implements, and rigorously evaluates a Mamdani-type Fuzzy Inference System (FIS) for coordinated greenhouse climate regulation using three environmental sensor inputs mapped to a seven-class actuator output covering heating, cooling, ventilation, humidification, misting, irrigation, and steady-state maintenance. The system employs triangular and trapezoidal membership functions partitioned across the full sensor range and a 27-rule expert knowledge base validated against established agronomic guidelines. Performance was assessed on a publicly available dataset of 17,164 IoT greenhouse sensor records spanning four seasons. Results show an overall accuracy of 72.1%; because ground-truth labels were generated by a crisp threshold classifier, this figure reflects agreement with a labelling scheme that structurally favours the crisp system rather than a measure of control quality. The evaluation therefore adopts a comparative behavioural framing centred on macro-averaged F1-score, on which the FIS attains 0.55 versus 0.50 for the crisp baseline, with the largest relative gain on the minority Humidify class (F1 = 0.09 vs. 0.00). Confusion matrix analysis confirms that all misclassifications fall within agronomically proximal action pairs, with zero catastrophic control reversals recorded. Three-dimensional control surface analysis validates the monotonicity and continuity of the decision landscape across all 27 rules. The study contributes a semantic proximity evaluation framework for assessing the operational safety of agricultural control AI, a validated reusable rule base and membership function architecture suitable for microcontroller-class embedded deployment, and quantitative evidence that macro F1-score is a more informative metric than overall accuracy for multi-class agricultural control classification.
Handling Multicollinearity in Logistic Regression via PCA, ICA, and GPCA: Theory, Simulation and Application
Multicollinearity poses a serious challenge in logistic regression analysis, leading to inflated standard errors, unstable coefficient estimates, and unreliable statistical inference. This study investigates the comparative performance of three dimension reduction techniques — Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Generalized Principal Component Analysis (GPCA) — as remedies for multicollinearity in binary logistic regression. The theoretical framework establishes the asymptotic properties of maximum likelihood estimators under transformed predictors, including consistency, Fisher information, and asymptotic normality via the Central Limit Theorem. A Monte Carlo simulation study was conducted using eight predictor variables across five sample sizes (n = 50, 100, 500, 1000, 10000) and three levels of inter-predictor correlation (ρ = 0.50, 0.75, 0.95), with 2, 3, and 4 retained components evaluated under each scenario. Model performance was assessed using the Akaike Information Criterion (AIC), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Deviation (MAD). The methods were further validated on a real-life dataset of automobile characteristics from the STATA repository, comprising nine predictor variables exhibiting substantial multicollinearity as confirmed by Variance Inflation Factor (VIF) analysis. Simulation results demonstrate that GPCA consistently achieves superior predictive accuracy over PCA and ICA across varying sample sizes, correlation levels, and numbers of retained components, as evidenced by lower MSE, RMSE, and MAD values. PCA performs competitively under the AIC criterion and is recommended when small samples constrain model complexity. Real-life data analysis corroborates these findings, with GPCA outperforming competing methods on three out of four selection criteria. These results affirm GPCA as the preferred dimension reduction technique for logistic regression in the presence of multicollinearity, with PCA serving as a viable alternative in small-sample settings.
Generalized Principal Component Analysis
Advancing Statistical Model Flexibility through the Development of the Exponentiated Generalized Topp Leone-Fréchet Distribution
We propose a new flexible statistical model called the Exponentiated Generalized Topp Leone-Fréchet (EGTL-F) distribution, obtained by compounding the Exponentiated Generalized Topp Leone-G (EGTL-G) family with the heavy-tailed Fréchet distribution. The EGTL-F distribution combines the skewness-control properties of the Topp Leone transformation with the extreme-value tail behaviour of the Fréchet distribution, making it suitable for modelling positively skewed data with heavy tails. We derive its cumulative distribution function, probability density function, survival function, hazard function, quantile function, ordinary moments, order statistics, and Rényi entropy. Parameters are estimated using the method of Maximum Likelihood Estimation (MLE). A Monte Carlo simulation study shows that the bias and mean squared error (MSE) of the MLEs decrease as the sample size increases from n =10 to n = 100, confirming the consistency of the estimators. Two real datasets, the strength of 1.5 cm glass fibres and the relief times following an analgesic treatment, are used to compare the EGTL-F distribution against three existing competitor distributions: the Exponentiated Fréchet distribution (EFD), the Fréchet distribution (FD), and the Exponential distribution (ED). In both applications, the EGTL-F distribution achieves the highest log-likelihood and the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, indicating a superior fit. These results show that the EGTL-F distribution is a competitive and flexible model for right-skewed, heavy-tailed lifetime data.
Gaussian Process-Homotopy Analysis Method for Tuberculosis Transmission With Stiff Dynamics and Time-Varying Contact Rate
Tuberculosis (TB) models are often stiff because they combine fast bacterial dynamics with slow disease progression, and the contact rate often changes unpredictably over time. To handle both challenges, we developed GP‑HAM, a hybrid method that joins the stable, semi‑analytical Homotopy Analysis Method with Gaussian Process regression to infer the time‑varying contact rate and quantify its uncertainty. Tests on synthetic data and real‑world Nigerian TB records show that GP‑HAM cuts prediction errors by 46–58% compared to standard explicit Runge‑Kutta, gives 95% credible intervals that cover the true values 93.8% of the time, and produces no numerical oscillations. Sensitivity analysis reveals that the contact rate and the progression rate from latent to active TB are the most influential parameters. Overall, GP‑HAM is a ready‑to‑use tool for modelling TB in settings where data are scarce, models are stiff, and uncertainty matters.
A Comprehensive Review of Machine Learning Techniques for Facies Classification in Oil and Gas Exploration: Methods, Challenges, and Future Directions
Facies classification is an important part of reservoir characterization and hydrocarbon exploration, in order to provide an accurate understanding of the reservoir and provide development planning for the field. Traditional approaches relied on manual interpretation of well logs, seismic data, and core samples. This method was time-intensive and introduced interpreter bias in the data, leading to inconsistent results. Machine learning has emerged as a transformative tool in geoscience, making it possible to classify facies automatically and accurately. This review looks at how machine learning is used for facies classification. We group the methods into algorithms, ensemble models, and deep learning architectures. Researchers emphasize the critical importance of data preparation, feature engineering, and multi-modal data integration. We look at what other people have discovered to see how well the models work, what data they need, and if they make sense from a geological point of view. Facies classification and machine learning are closely related. We discuss the challenges we are facing now, what is new and trending, and what we still need to work on. This synthesis provides a roadmap for advancing ML-based facies classification and identifies priorities for future research.
Optimal Control of Typhoid Fever Transmission With Imperfect Diagnosis and Multiple Interventions
Typhoid fever persists in regions with poor sanitation and weak healthcare infrastructure, exacerbated by diagnostic inaccuracies—specifically false-negative test results—that leave infectious individuals untreated and sustain transmission. This study extends a deterministic compartmental model (PSIDT) to incorporate the epidemiological impact of imperfect diagnosis. Applying Pontryagin’s maximum principle, optimal control schedules are derived for four interventions: vaccination, diagnostic improvement, enhanced treatment, and water, sanitation, and hygiene (WASH) programs. Numerical simulations demonstrate that integrating these interventions reduces both infectious and misdiagnosed cases by more than 95% within 50 days. Incremental cost-effectiveness ratio (ICER) analysis reveals that improving diagnostic accuracy alone is the most cost-effective single strategy, costing 4.35 thousand Naira per averted infection, while the combined strategy averts the highest number of infections (950 per 1,000 cases) while maintaining a favorable ICER. Consequently, enhancing diagnostic accuracy should be prioritized alongside comprehensive public health measures in typhoid-endemic regions, as this optimal control framework offers practical, data-driven guidance for resource allocation.
Semi-Analytical Solution of Cervical Cancer Model using the Adomian Decomposition Method
This study presents a semi-analytical solution for a deterministic 8-compartment model of cervical cancer using the Adomian Decomposition Method (ADM). Unlike purely numerical schemes, ADM yields a continuous, exponentially convergent series solution that explicitly reveals the role of each model parameter in disease progression. Novel contributions include the derivation of recursive formulas and Adomian polynomials specific to the nonlinear HPV transmission term, a sensitivity analysis of screening and treatment rates directly from the ADM series, and an assessment of the convergence radius for realistic epidemiological parameters. The ADM solution with only 11 terms achieves a maximum absolute error below 10−0.95 ≈ 0.112 for the recovered population, demonstrating rapid convergence. Validation against the fourth-order Runge-Kutta (RK4) method shows excellent agreement. The continuous nature of ADM allows for a precise estimation of the time to peak infection and the long-term equilibrium, providing actionable insights for public health interventions in high-burden regions such as Nigeria.
Quality Assurance as Individual Responsibility: The Three-Tier Reality of Research Integrity in Nigerian Public Universities
When institutional infrastructure for research quality assurance fails, the consequences extend beyond individual careers to compromise an entire national knowledge system. This paper examines how quality assurance functions when formal mechanisms ethics review boards, plagiarism detection, peer review, and research integrity training are largely absent. Thematic analysis of 45 in-depth interviews at two Nigerian public universities reveals a three-tier quality assurance reality: formal mechanisms exist primarily on paper; informal peer networks serve as the primary operational quality control system for those with access; and individual moral commitment provides the last-resort foundation for those without, at the cost of significant psychological burden. The study documents pervasive policy-practice gaps, including non-functional IRBs (0–30% utilisation), cost-inaccessible plagiarism detection, and essentially absent research integrity training. It identifies a structural equity problem: quality assurance access is distributed along career advantage lines, with senior academics at federal institutions receiving genuine, if informal, quality review while junior academics at state institutions receive essentially none. This inequity is cumulative those with less quality feedback produce weaker research profiles, limiting their access to the very informal networks that provide quality support, and may ultimately exit the profession entirely. The study argues that predatory journal use, typically framed as individual ethical failure, is better understood as rational adaptation to structural conditions including absent quality infrastructure and perverse promotion incentives. Four priority interventions are indicated: IRB operationalisation beyond policy commitment; consortium-based plagiarism detection licensing led by the National Universities Commission; embedded research integrity training for early-career academics; and promotion criteria reform distinguishing publication quality from quantity.
research quality assurance; peer review; institutional review board; research integrity; Nigerian universities; predatory journals; Global South; academic integrity; under-resourced contexts; higher education
Copyright (c) 2026 Nigerian Journal of Operations Research (NIJOR)
Reverse Logistics and Non-Financial Performance: Experience from Selected Manufacturing Firms in Nigeria.
Driven by escalating global environmental consciousness and tightening regulatory frameworks, this study investigates the impact of reverse logistics on the operational performance of manufacturing firms in Nigeria. Additionally, it identifies the primary barriers hindering the comprehensive implementation of reverse logistics practices within the sector. Adopting a quantitative survey research design, primary data were elicited via structured questionnaires from 218 supply chain and logistics professionals across the healthcare, brewery, and flour mill industries. Data were analyzed using descriptive statistics and Partial Least Squares Structural Equation Modeling via SPSS and SmartPLS. The findings reveal that while several factors impede reverse logistics adoption, the most critical barriers include a lack of enforceable laws and policies, inadequate supportive economic policies, and prohibitive financial obligations. Inferential analysis demonstrates that reverse logistics explains 65.2% of the variance in firm performance, exerting a positive and statistically significant influence. Furthermore, the effect size indicates that RL maintains a substantial and critical role in driving organizational outcomes. These results underscore the strategic importance of reverse logistics as a core component of green supply chain management. The study concludes that for Nigerian manufacturing firms to achieve sustainable competitive advantage, stakeholders must institutionalize reverse logistics through dedicated resource allocation and policy alignment.
Adaptation of Artificial Bee Colony Algorithm for Solving Inventory Routing Problem
This study applies the Artificial Bee Colony (ABC) algorithm to the Inventory Routing Problem (IRP), using real-world data from the RAODEF-EURO Challenge 2016 on liquid-gas distribution. The research develops a feasible optimization model for IRP scenarios involving 1–13 drivers and 1–15 trailers delivering products from a central depot to 200–324 customers over planning horizons ranging from one week to one month. The model incorporates standard IRP constraints and aims to minimize the logistic ratio, defined as delivery cost relative to transported volume over time. Addressing IRP is essential in operations research, as it requires balancing varying customer demands with long-term distribution efficiency. Although previous heuristic and hyper-heuristic approaches achieved partial success, many struggled with scalability in large, constraint-intensive instances. To overcome these limitations, this study evaluates both the standard Artificial Bee Colony algorithm and a Modified Artificial Bee Colony (MABC) variant. Their performances are assessed using fifteen benchmark instances (Instance B). Results show that both algorithms generated feasible solutions across all datasets. However, the MABC consistently achieved lower logistic ratios and improved cost efficiency in most instances, outperforming the standard ABC in solution quality. The overall findings demonstrate that the MABC offers a more robust and scalable approach for solving complex IRP scenarios, making it a promising tool for practical logistics planning and decision-making.
Multi-criterion Decision-making Approach to Prioritize Strategies for Combating Smuggling and Border Control Along the North-east Borders of Nigeria
ABSTRACT Smuggling is a worldwide issue that leads to serious problem of insecurity, health risks, money laundering, the extinction of endangered species, and the premature collapse of local industries due to illegal trade. North-East region of Nigeria, is significantly affected by these smuggling challenges. Despite various efforts by stakeholders over time, to find a lasting solution has been difficult. This research aims to identify a more effective approach to address the problem of combating smuggling and border control. The study utilizes the Analytic Hierarchy Process (AHP) approach with multiple respondents to assess different strategies to reducing problem of smuggling and border control along North-East borders of Nigeria. The strategies considered include; Physical Patrol (PP), Border Closure (BC), Perimeter Fencing (PF), Electronic/Digital Camera Surveillance (ED), and Mechanical/Drone Surveillance (MD). These strategies were assessed with the following criteria; Cost Efficiency (CES), Efficiency in Trade Facilitation (ETF), Efficiency in Revenue Generation (ERG), and Efficiency in Combating Smuggling (ECS). Data were collected from four experts respondents (senior customs officers in the north-east geopolitical zone of Nigeria), using a structured Saaty 9-point scale questionnaire. Purposive sampling was employed for respondents’ selection. The experts' judgments were aggregated using the geometric mean. Pairwise comparison matrices were created, and the consistency of each aggregated pairwise comparison judgments were tested. The consistency ratio of pairwise comparison judgements of the criteria by the respondents is 0.0500 and, that of the alternatives with respect to each criterion; Cost Efficiency (CES), Efficiency in Trade Facilitation (ETF), Efficiency in Revenue Generation (ERG), and Efficiency in Combating Smuggling (ECS) were 0.07148, 0.0373, 0.0393, and 0.0239, respectively. The values of the consistency ratios were less than 0.1 indicates, the judgments were consistent. The findings shows that ED ranked first (1St) as the most effective strategy, followed by PP ranked second (2nd), PF and BC were closely ranked third (3rd) and fourth (4th), and MD ranked fifth (5th). The study recommended that Electronic/Digital Camera Surveillance (ED) and Physical Patrol (PP) should be considered for effective combating smuggling and border control along the north-east borders of Nigeria.
Improved Particle Swarm Optimization for Global Optimization with Decaying Adaptive Velocity Limit
This paper introduces an innovative enhanced Particle Swarm Optimization (PSO) technique known as IPSO-AVL. It features a time-adaptive velocity limit and a fitness-weighted personal best centroid. To test how effective this new method is, we conducted a comparative analysis against the traditional PSO and an intermediate IPSO approach using five different test functions: the Sphere function, the Rosenbrock function, the Rastrigin function, the Griewank function, and the Ackley function. The experiments were set up with parameters of D=30, N=30, and T=5000 over 30 runs. The findings indicate that the IPSO-AVL method significantly outperforms the traditional models, particularly for the Sphere and Rastrigin functions.
Graph Modelling of Transaction Networks for Fraud Detection and Systemic Risk Assessment in Nigerian Banks
This study employs a graph modelling approach for the analysis of fraudulent transactions and the assessment of systemic risk in the banking system of Nigeria. Five different approaches of graph modelling; the transaction network analysis, community detection, ego networks, anomaly detection, and multilayer graphs, are utilized for the evaluation of the data set of 1,000 transactions over a period of 41 days. Betweenness centrality of the nodes was identified, where the centrality values range from 0.169 to 0.195, indicating the potential for the identification of liquidity hubs of the system, which are critical for the assessment of systemic risk. In addition, community detection using the greedy modularity approach identified clusters of users displaying high levels of cohesion, indicating the existence of fraud rings. Ego networks identified high levels of behavioural heterogeneity, where some users displayed high transaction frequency but low fraud levels, while other users displayed high fraud levels despite low transaction frequency. Anomaly detection identified high levels of fraud subgraphs and cyclic patterns, where K-cycle detection identified 326 cycles, where the fraud ratio increases from 38% to 58% for cycles of length 2, 3, 4, and 5, respectively. Temporal modularity analysis at seven-time window intervals (Q scores: 0.6428-0.7329) enables the analysis of network evolution and fraud concentration patterns. Multilayer modelling with transaction amount, fraud rates, and frequency/KYC tiers enables the identification of behavioural asymmetries. At the optimal fraud ratio threshold of ρ ≥ 0.5, the K-cycle detection framework yields a true accuracy of 61.0%, precision of 37.4%, recall of 47.9%, and an F1-score of 42.0%, These figures compare favourably with the single-feature baseline, which achieves 58.0% accuracy, 29.9% precision, 31.6% recall, and an F1-score of 30.7% on the same cycle set. The results show the viability of using graph methods for fraud detection and risk management in emerging markets' FinTech systems.
A Graph Analysis of Topological Structure in the Bus Rapid Transit Transport Network of Lagos State
The transportation system in Nigeria's city of Lagos is a complex network characterized by rapid urbanization, significant congestion, and a mix of formal and informal modes of transportation. This paper proposes a graph-theoretic framework for structurally and functionally modelling and analyzing the BRT (Bus Rapid Transit) terminal transportation system in Lagos using a vertex deletion algorithm. The system is represented as an undirected graph representing the Bus Rapid Transit (BRT) Lite system. The analysis yields a scale-free nature in the road network, with susceptibility to interference at nodes of high centrality such as Surulere. The BRT terminal, while successful along its route, lacks strong integration with the overall network. A graph-theoretic model offers a framework for guiding targeted investments to make Lagos’s transport system more efficient, inclusive, and sustainable.
Optimizing Resource Planning via Seasonal Weather Analysis in Climate-Sensitive Regions
Climate variability significantly influences agricultural productivity, water availability, and environmental planning worldwide. Understanding seasonal fluctuations can help address budgeting, planning, and resource allocation issues in advance. This study presents a comprehensive analysis of temperature and rainfall patterns in District Rajouri, Jammu and Kashmir (India) for the year 2023, emphasizing seasonal variability and its implications for short-term agricultural and water resource planning. Monthly data, including maximum and minimum temperatures, rainfall, relative humidity, sunshine duration, wind speed, and evapotranspiration, were collected from the Indian Meteorological Department (IMD), Krishi Vigyan Kendra at Tandwal, Rajouri. Using descriptive statistics and graphical tools in Python, seasonal fluctuations in various weather parameters were analysed. The findings highlight significant intra-annual variability, with peak temperatures and evapotranspiration during pre-monsoon months and maximum rainfall during the monsoon season. While the study focuses on a single year and avoids long-term seasonal variability interpretations, insights provide valuable information for short-term agricultural and water resource planning. The study recommends future research incorporating multi-decadal datasets for seasonal variability assessment. The study also evaluates the interrelationship among key meteorological parameters using statistical analysis to better understand seasonal climatic behaviour and its implications for agricultural and water resource planning.
Copyright (c) 2025 Nigerian Journal of Operations Research (NIJOR)
Reliability Evaluation and Optimization of Solar Based Ammonia Urea Production System: A Study on Lethal and Nonlethal Failures with Coverage Factor Considerations
This study presents a comprehensive reliability evaluation and optimization framework for a solar-based ammonia-urea production system designed for Kano State, Nigeria, emphasizing the effects of lethal and nonlethal failures under varying coverage factor conditions. The proposed system integrates solar photovoltaic (PV) generation with electrolyzers, ammonia synthesis, and urea production units to promote sustainable fertilizer manufacturing suited to the climatic and energy conditions of Kano State. Reliability modeling was conducted using Reliability Block Diagrams (RBD) and Markov analysis to assess system performance under different failure and repair scenarios. Lethal failures, leading to total system shutdown, and nonlethal failures, causing partial performance loss, were analyzed to determine their impact on system availability, reliability, sensitivity and Cost (CARS). The coverage factor was incorporated to capture the influence of fault detection and recovery efficiency. Optimization studies focusing on redundancy allocation and improved fault coverage were performed to enhance system resilience. Results indicate that coverage factor placement significantly decrease system reliability, availability, or cost thereby making the solar-based ammonia-urea production system unviable and unsustainable solution for Kano State’s agricultural and energy needs.
Solar energy systems, Reliability assessment, Coverage factor, CARS approach, Kano Metropolis, Optimization.
Impact of Public Debt on Infrastructural Development in Nigeria (1986–2022)
Nigeria’s persistent infrastructure deficit has remained a major constraint to sustainable development despite rising levels of public borrowing. This study investigates the impact of public debt on infrastructural development in Nigeria from 1986 to 2022. Annual time-series data were analysed using the Autoregressive Distributed Lag (ARDL) bounds-testing approach to examine both the short- and long-run relationships among the variables. The findings reveal that domestic debt and human-capital development exert a positive and significant impact on infrastructural development in the long run, whereas external debt negatively affects infrastructure growth. The estimated error-correction term indicates a rapid speed of adjustment of about 94 percent, confirming long-run equilibrium. The study concludes that judicious use of domestic borrowing and efficient debt management can strengthen infrastructural development. It recommends prioritising debt-financed capital projects with measurable returns, improving transparency in loan utilisation, and fostering sound debt governance to ensure sustainable infrastructure expansion.
Hybrid Reverse Proxy Architectures: A Copula-Theoretic Perspective on System Reliability
This study explores the reliability of hybrid reverse proxy architectures, which combine distributed and centralised load balancing. Due to complex interdependencies among subsystems, traditional reliability models fall short, especially for correlated or cascading failures. The paper introduces a Gumbel copula-based approach to model the joint failure behaviour of these subsystems, effectively capturing the risk of simultaneous extreme failures. The method enables more accurate estimation of complete failure probabilities and supports better fault recovery planning through copula repair strategies, providing a practical framework for improving reliability in complex distributed systems.