The electricity generation policy is a strategic policy that drives development in a community. Energy policies are often analyzed with the aim of generating a reliable and affordable electricity for a community. There is a high probability of achieving this aim when energy policy is combined with a community social, technical, economic and environmental needs. This paper determines a hybrid renewable energy source (HRESs) for a rural community using technical, economic, and techno-economic criteria. The selection process combines Criteria Importance Through Inter-criteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a solution method. This approach applicability was tested using six HRESs under economic and technical criteria. Ten technical and nine economic criteria were simulated for the HRESs using HOMER. The results from the HOMER software show that A5(PV/wind/battery) and A6 (PV/battery) had a renewable fraction of 1. The results obtained from the CRITIC method showed that the most important technical and economic criteria were diesel generator and total fuel cost, respectively. From an economic perspective, the best HRES for the case study was A4 (diesel/batteries), while A3 (wind/diesel generator/batteries) was the best HRES from a technical and techno-economic perspectives.

Retrofitting technologies have helped to manage energy consumptions in residential, public and industrial buildings. However, understanding of the technical and economic considerations for selection of appropriate retrofitting technology is still evolving and divergent. Thus, this study presents a framework that combines techno-economic requirements as a means for evaluating the important retrofitting criteria and suitable lighting retrofit technologies for building projects. The framework is hinged on the unique features of entropy fuzzy and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods. The analysis of the lighting technology selection was performed from technical, economic and techno-economic perspectives. During the application of the proposed framework, four lighting technologies (CFL, T5, E-ballast and T8-electronic) and nine techno-economic criteria were considered. The most and least important techno-economic criteria for the case study were net present value and electricity saved, respectively. The least and most suitable retrofitting technologies were T8-electronic and CFL, respectively, from techno-economic perspective. T5 and T8-electronic were identified as the most suitable lighting technologies from an economic and technical perspectives, respectively. This discrepancy in the results justified the need for the techno-economic approach for the retrofitting technologies evaluation.

This paper discusses Islamic financial reporting system as practiced in many Islamic states for economic development. The issue of Islamic accounting among scholars provided evidence of economic benefits among the countries that have Islamic financial institutions (IFIs). The IFIs have been reported to be the fastest-growing sector in the world with a greater contribution of the global total bank assets. Although the Muslim population in Nigeria is large, the contribution or participation of Muslim towards IFIs is low compared to other countries like Bahrain, Saudi Arabia, Pakistan, and Syria. Furthermore, the accounting reporting in Nigeria is based on the International Financial Reporting Standards (IFRS) for all listed firms in Nigeria, which includes IFIs, even though, IFIs apply dual reporting. Therefore, the need for the mandatory adoption of Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) standards is significant in Nigerian for IFIs to function better. Several countries have made it mandatory for IFIs to adopt AAOIFI instead of IFRS. The need for IFIs firms to report on their accounting system as an alternative to the conventional, will not only enhance transparency, improve reporting disclosures, greater Muslim investors, but will also improve Nigerian stock market. Policy makers, governments, and the regulator should make it mandatory for all IFIs to adopt AAOIFI for financial reporting.

Electrical Energy is an essential commodity which significantly contributes to the economic development of any country. Many non-linear factors contribute to the final output of electrical energy demand. In order to efficiently predict electrical energy demand, many time-series analysis and multivariate techniques have been suggested. In order for these methods to accurately work, an enormous quantity of historical dataset is essential which sometimes are not available, inadequate and inaccurate. To overcome some of these challenges, this paper presents an Artificial Neural Network based method for Electrical Energy Demand Forecasting using a case study of Lagos state, Nigeria. The predicted values are compared with actual values to estimate the performance of the proposed technique.

The increased role of wind turbine systems makes it important for its operational states to be con-tinuously monitored and optimized. This goal can be achieved using existing methods, which re-lies on closed-form expressions. The use of these methods, however, becomes challenging when interacting parameters cannot be fully presented with closed form expressions. In this paper, an artificial neural network (ANN) based algorithm is proposed as a solution to this problem. This algorithm is used to estimate wind turbine systems operational state and reliability. The proposed method is able to provide a more holistic approach to manage a wind turbine system with respect to the problem mentioned above. Simulation results show that the developed ANN can predict the average number of failures per year, distribution of failure and average downtime per failure with good accuracy. This was achieved using an ANN model with 5-15-3 architecture. The model generates mean square errors of 4.6 × 10^-3, 4.2 ×10-3, and 4.0 × 10^-3 at the training, validation, and testing stages, respectively. The study is beneficial to wind turbine practitioners and manufacturers as its findings can provide in-depth understandings of reliability issues of the system.

Manufacturing facilities are systems that require adequate designing, maintenance and reservations for improvement in the future. Layouts need to be effectively designed to reduce operating to the minimum. Computer simulation is a process of investigating and analyzing the behavior of production processes for effective decision-making using computers to generate solutions that will positively impact short, and long-term planning of the Plants and save costs of real-life implementation. This study investigated a 500Kg capacity shea nut processing plant using FlexSim©. The findings from the initial model were not effective and experienced bottlenecks in workstations (Roaster and Milling) sections, poor cycle and lead times coupled with manual labor, Plant efficiency was 35.7%. However, the Improvement Layout Model was able to address these bottlenecks, the results showed the Plant efficiency increased to 83.3%, shorter lead and cycle times, improved machine utilization, and throughput capacity of the Plant. The results were an indication of conformance to the layout design developed to aid in enhancing the traditional shea nut processing that is largely dominated by traditional processing practices.

A new flexible and adjustable model which is based on an Intuitionistic Fuzzy TOPSIS model with an exponential-related function and intuitionistic fuzzy entropy method has been presented and apply in this study. For supporting designers in determining design stakeholders’ preferences when finding the best compromise between new concepts design during early product develop-ment phase. The main advantage of the new model is that it is flexible and adjustable and affords the design stakeholders the option to a change of action in the event they receive new information or reason not to continue with a particular design or product. Finally, it uses both subjective and objective weights methods for the evaluation of the design concepts criteria, which makes the proposed model more realistic and more practical. The model has been applied for the design concept evaluation of seven forklift trucks with the view to improving the reliability of the machine.

Decision-making process is an integral part of every sporting event across the globe. This process uses inputs from single to multi-decision makers. To make a sporting event a success, appropriate decision making needs to be made starting from hosting-right to the end of the event. Thus, this study presents a framework that scientifically determines the hosting-right for a sporting event. The framework is based on techno-economic analysis of potential hosting locations for a sporting event. Techno-economic criteria are analysed using fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method. The analysis of the hosting-right is also performed through technical, economic, techno-economic and cost-benefit ratio perspectives. A case study of national sports festival in Nigeria is used to demonstrate the applicability of the proposed framework. Twelve technical and eight economic criteria are considered in the proposed framework. Six loca-tions are considered during the implementation of the proposed framework. The results obtained show that the issue of hosting-right award depends on the evaluation criteria that are considered by decision-makers.

Business process evaluation is a common norm in small-medium-large industries globally and information obtained during such evaluation have been used in simulating future performance of most industries using mathematical models such as Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN). This study explored the possibility of predicting port productivity and berth effectiveness of seaport using ANN and ARIMA. A comparative analysis of multi-layer perceptron (MLP) back propagation algorithm and ARIMA performance was carried out based on ships days at port, days at berth and tonnage which serves as model inputs, while port productivity and berth effectiveness were the model outputs. The MLP-ANN and ARIMA (1, 0, 4- port productivity) and (1, 0, 4-berth effectiveness) results were compared based on their coefficient of correlation and mean square error. The coefficient of correlation for port productivity prediction using MLP-ANN was 0.998. This value outperformed that of ARIMA (0.9862) for port productivity. The coefficient of correlation of 0.9956 and 0.9928 were obtained for berth effectiveness using MLR and ARIMA, respectively.

This paper considers the dynamic variant of single processor scheduling problem of minimizing total tardiness. In practice, it occurs when minimizing tardiness penalty. The problem is NP-hard; thus two heuristics were proposed. The utility of the proposed models was demonstrated through computational experiments and comparative analyses against existing solution methods and the Branch and Bound (BB) method. The results show that the proposed models yield effi-cient solutions and in most cases perform effectively better than the existing heuristics in the lit-erature.