Faculty of Accounting and Informatics

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Bio-inspired optimisation of a new cost model for minimising labour costs in computer networking infrastructure
(2024) Nketsiah, Richard Nana; Millham, Richard Charles; Agbehadji, Israel Edem
This thesis revolves around the bio-inspired optimisation of a newly formulated cost model tailored for initial installation of a user-specified computer networking infrastructure, motivated by requirements of networking industries, with a focal point on minimising labour costs. The new cost function of this infrastructure installation incorporates essential decision variables related to labour, encompassing the daily requirements and costs of both skilled and unskilled workers, their respective hourly rates, installation hours, and the overall project duration. This deliberate emphasis on labour-centric factors aim to offer nuanced insights into the intricacies of project budgeting and resource allocation. The research critically evaluates the effectiveness of the cost function by examining various factors, such as daily fixed costs, a size and complexity factor tailored to individual scenarios, and a penalty coefficient aimed at ensuring compliance with project schedules. Significantly, the deliberate exclusion of equipment, material, maintenance and operational costs underscores the focused examination of labour-related expenditures, providing a unique contribution to the optimisation landscape within the installation of the user-specified computer networking infrastructure projects. Utilising advanced bio-inspired optimisation techniques, alongside real-world data, this study endeavours to gauge the effectiveness of the new cost model in minimising labour expenses while upholding optimal network performance. The anticipated outcomes of this study extend beyond theoretical contexts to practical implications, providing actionable insights and recommendations for network infrastructure planners. The significance of labour-centric considerations in project planning and design is underscored, providing a more encompassing perspective that aligns with the evolving landscape of modern technological infrastructures. By giving attention to labour-intensive aspects within installation of computer networking infrastructure projects, the thesis aspires to enhance budgeting accuracy and streamline resource allocation processes, thereby fostering more efficient and cost-effective project outcomes.
Optimization of hybrid renewable energy generation using a nature-inspired algorithm with advanced IoT analytics
(2022-11-01) Frimpong, Samuel Ofori; Millham, Richard; Agbehadji, Israel Edem
A stable and cost-effective power supply in an autonomous hybrid energy system requires an efficient design process for renewable energy technologies. Accordingly, the best design of a standalone hybrid renewable energy system (HRES) should consider several factors such as renewable energy data, load profile, technical and economic analysis of the renewable technologies, ideal location for the power system, etc. Different data from renewable energy sources are modelled into an optimization problem which incorporates the crucial point, in HRES, of the correct sizing of the various power components, which directly affect the cost and power security/reliability of the system. This thesis proposes an innovative meta-heuristic optimization algorithm called Social Spider-Prey (SSP) that mimics the foraging behaviour of social spiders and prey(s) on the social web. By examining the foraging behavioural traits of social spiders and prey(s), a global optimization algorithm was developed to solve a hybrid renewable energy optimization problem of correct sizing, minimal cost, and highest reliability. In SSP, artificial spiders are considered search agents. On the one hand, every spider can freely roam the social web, a hyperdimensional search space, to implement an exploratory search scheme. On the other hand, nearby spiders relative to a captured prey search the neighbourhood, which is implemented as an exploitative search mechanism. These two search strategies are harmonized in SSP to solve the multi-source renewable power generation optimization problem effectively. Four different power generation scenarios were analysed to determine optimal power generation using experimental real-time environment data collected with sensors and secondary data retrieved from a benchmark dataset, National Renewable Energy Laboratory (NREL). The optimization algorithms inspired by nature, namely Social Spider-Prey (SSP), Particle Swarm Optimization (PSO), Teaching-Learning Based Optimization (TLBO) algorithm and Social Spider Algorithm (SSA), were used in a comparative study to search for a near-optimal result for the hybrid system configuration that satisfies the optimization problem. The results show the economic and reliable implications of different system configurations that meet the specified combined criteria, as indicated in the HRES optimization problem, to make the best investment decision. The SSP guaranteed optimal annualized system costs and met the reliability constraints for all the case scenarios: wind/biomass/battery (ZAR 3,431,512.26 and LPSP of 0.011), PV/wind/ biomass (ZAR 2,549,792.71 and LPSP of 0, 0011), PV/biomass/battery (ZAR1, 638,628.82 and LPSP of 0.00021) and PV/wind/biomass/battery (ZAR1, 412,142.80 and LPSP of 0.0141). Based on this result, the study proposes the SSP as an optimization approach for the solar PV/wind/biomass/battery hybrid system, as it ensures 99.98% power reliability. In addition, a Kruskal-Wallis test was performed to determine the significant differences among the comparison algorithms.