Search

In recent times, there have been developments in the maritime industry that underscore the need to optimise operations to yield maximum productivity. Apart from this, stakeholders in this industry have also advocated improvements in seaport operations' critical areas. However, there is no known study in which the relationship between performance criteria and seaport operation measures is investigated. This study proposes a framework for selecting operation measures for the maritime industry. It uses stakeholders' expectations for operational criteria and fuzzy logic to design the framework. Nine criteria were considered in the framework, while Fuzzy VIKOR (VIsekriterijumska optimizacija I KOmpromisno Resenje) and fuzzy Shannon entropy were incorporated into it. The framework's applicability was tested using information that was obtained from Tin can port, Lagos, Nigeria. During this process, hinterland traffic diversion (A1), congestion pricing (A2), off-dock container yards (A3), Fast rail shuttles (A4), expanded rail connections (A5) were considered as alternatives for seaport operational measures. When the developed framework was used to analyse the collected information from Tin Can port, Lagos, Nigeria, the fuzzy VIKOR index ranked the alternatives as A1 » A2 » A3 » A5 » A4. Therefore, this study's insights show that mathematical models can be used to make informed seaports decisions.

The calls for actions to combat climate change have resulted in renewable energy adoption and, consequently, an emerging renewable energy industry. Of course, this has also precipitated the need for more renewable energy expert grounded in hybrid renewable energy system (HRES) software to support the growing industry. Hence, selecting particular software for teaching renewable energy modules for the training of students at a university level is essential. The appraisal and selection of HRES sizing and optimisation software have become crucial for instructors and learners being prepared for the industry. This paper's central contribution is to provide academics, tutors, and researchers in the field of renewable energy system sizing with an idea on how to proficiently and effectively deploy multi-criteria decision-making (MCDM) approaches in HRES sizing and optimisation software selection problems. A framework that combines fuzzy entropy method and fuzzy-VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) was used to rank HRES simulation software. The performance of the framework was compared with that of the complex proportional assessment (COPRAS) method. The article observed a difference between the fuzzy VIKOR and COPRAS solution for ranking the most preferred software; the most preferred option using the Fuzzy VIKOR is the BCHP screening tool, and HOMER for the COPRAS method. They ranked the least preferred software as RETScreen (VIKOR method) and ORCED (COPRAS method).

The management of waste with the limited workforce and trucks is a complicated problem. Currently, there is insufficient evidence in literature on how this process could be optimised. In this article, two new models on genetic algorithm and differential evolution were developed to jointly optimise the cost and human reliability of a municipal solid waste (MSW). It optimised this system’s benefit-cost and established the relationship between a MSW’s workforce and truck allocation. Although prior research has revealed relationships among cost, workforce strength, and truck allocation activities, however, the nature of this relationship and the unique attribute of workers’ reliability to influence the total operating cost and the benefit-cost ratio have not been thoroughly understood. A case study of a MSW agency in Nigeria was used to demonstrate the applicability of the proposed model. The results obtained showed preference to the differential evolution algorithm’s results. This article contributes to MSW in the following ways: it presents a model to assign reliability to workforce in a MSW system based on evolutionary algorithms performance, and it optimises a MSW system’s total operating cost and the benefit-cost ratio concurrently.

Sulphate-rich electrolytic bath containing ZnSO4.7H2O in NiP solution was used to develop coating with Ni-P-Zn matrix under optimised process parameter. The major considerations are to examine the ...

At present, labour unions of waste disposal agencies and company management are at loggerheads, frequently turning out contradictory sanitation assessments. This reveals a shifting outlook of sanitation accomplishment that should be resolved. Unfortunately, there is scanty research on road sanitation and no study exists on how to determine the important workforce variables of these workers. To solve this research problem, a multi-objective optimisation model is developed and solved using the differential evolution model. The proposed model considered different constraints including workforce size, budgets, and service time. Three conflicting goals of maximization of cleanliness, maximization of workers' effectiveness and minimization of traffic obstruction were incorporated into the model and solved using practical data from a waste disposal agency in a developing country. A key result shows that the system's average workers' turnover rate is 0.2472 while the system's average service failure rate is 0.2518. For each location, the system requires an average of eight workers per period. The worker's average quality of work done is 0.8552. The outcome of the work revealed the feasibility of the model application. It was concluded that the model serves as a basis to evaluate road sanitation workers and may be used for budgetary purposes.

This paper presents a brief overview of solar thermal power generation technologies (parabolic trough, central tower receiver, linear Fresnel reflecting and parabolic dish concentrators) and their possible application in Nigeria. It further discusses an array of barriers to the development of the technologies, such as the lack of understanding of solar thermal systems, lack of incentives for renewable technologies and previous experience of solar photovoltaic systems failure that is making people doubt the viability of renewable electricity. Other barriers that were considered are lack of technical expertise, high technology cost and lack of project funding, including a lack of enabling policies to drive the technologies. This study then develops a policy framework that will help to understand and address some of the identified challenges to achieve widespread adoption, application, and diffusion in Nigeria. In addition, the framework will provide useful insights into the major issues that affect community-based or regional solar thermal power systems in developing countries. Widespread application of these clean energy technologies can help mitigate climate change.

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.

Flood management is a global problem that has created immense contributions from researchers and practitioners, especially those in developing countries. These people often seek ways to minimise the aftermath of a flood. Recently, they are making a case for sustainable solutions to flood management. This study, therefore, contributes a sustainability model that addresses the problem of blue-green technology selection to the current discussion on flood management. It coupled the techno-economic, social, and environmental impact of a blue-green technology using the unique attributions of three multi-criteria decision-making tools: best-worst method, fuzzy axiomatic method and VIKOR; its performance was investigated with qualitative data sets that were obtained from experts. The outcomes of the investigation showed that techno-economic criteria contributed about 88.18% to the ranking of blue-green technology. The most and least suitable blue-green technologies for a community in Nigeria are Rainwater and floodwater harvesting and Retention lake, respectively. With these results, the proposed model will aid decision-makers strategic and tactical criteria that can be used to evaluate blue-green technology selection.

This paper studies the use of fuzzy logic theory embedded in fuzzy axiomatic design to develop an integrated model with the analytical hierarchy process, and a selection of maintenance strategies in manufacturing systems through weighted sum-product evaluation. In this paper, an analysis was conducted to understand the model characteristics of combined fuzzy axiomatic design, analytic hierarchy process and weighted aggregated sum product assessment. The results from the analysis served as information to establish the best maintenance strategy. Based on the data obtained from a factory, which was tested on the proposed framework, it is concluded that the most preferred proactive maintenance strategy for the case study was preventive maintenance, followed by reliability-based maintenance while predictive maintenance is the least preferred maintenance strategy in the rolling mill. Accordingly, the two-stage fuzzy multi-criteria maintenance strategy selection approach is appropriate to select the best maintenance strategy for the factory. This paper offers a new method to establish the best maintenance strategy for a rolling mill. As such, the manager could install a method for significant improvement in engineering practices with promising business excellence and competitiveness results.

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 draws on the “human reliability” concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of deviation to encompass three elegant adjustments to literature model in terms of maintenance time, workforce performance and return-on-workforce investments. These fully explain the results of our influence. The presented structure breaks new grounds in maintenance workforce theory and practice from a number of perspectives. First, we have successfully implemented fuzzy goal programming (FGP) and differential evolution (DE) techniques for the solution of optimisation problem in maintenance of a process plant for the first time. The results obtained in this work showed better quality of solution from the DE algorithm compared with those of genetic algorithm and particle swarm optimisation algorithm, thus expressing superiority of the proposed procedure over them. Second, the analytical discourse, which was framed on stochastic theory, focusing on specific application to a process plant in Nigeria is a novelty. The work provides more insights into maintenance workforce planning during overhaul rework and overtime maintenance activities in manufacturing systems and demonstrated capacity in generating substantially helpful information for practice.

This article proposes a conceptual framework that used mini-gird business models (MBMs) to rank electricity tariff plans. The framework combines intuitionistic fuzzy set (IFS), Criteria Importance Through Inter-criteria Correlation (CRITIC), fuzzy axiomatic design (FAD) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It considered when decision-makers specify or did not specify for MBM’s requirements. Also, this article considered uncertainty in decision-makers information. The framework applicability was showed using information obtained from Sub-Sahara Africa. Five decision-makers provided information for the model implementation based on a well-structured questionnaire. The CRITIC method results showed that least and most important MBM are community-based and private MBMs, respectively. The IFS-FAD method identified time-based electricity tariff plan as the best suitable for the case study, while the IFS-TOPSIS method identified flexible best electricity tariff plans for the case study, respectively. However, both methods identified flat-rate as the least suitable plan. The proposed framework will provide mini-grid business owners with relevant information for energy tariff selection.

Socio-technical and economic attributes consideration are very important during a renewable energy technology selection for a community. When decision-makers considered these attributes under a dynamic nature, they arrive at a robust decision. Hence, this study proposes an integrated model for renewable energy technologies evaluation under a dynamic condition. We developed the model using dynamic intuitionistic fuzzy Einstein geometric averaging operator, intuitionistic fuzzy entropy, and the intuitionistic fuzzy technique for order of preference by similarity to ideal solution method (TOPSIS). This model’s applicability was tested using five renewable energy technologies - solar (PT1), wind (PT2), hydroelectricity (PT3), geothermal (PT4) and biomass (PT5) and five attributes (risk factor, payback reliability, social benefit, change in demand and cost). Based on five energy experts, from academia and industry, opinions, the proposed model identified biomass energy technology as the most suitable energy technology. Three existing multi-criteria models were used to verify the proposed model; the proposed model performance was consistent with the existing models’ results. From most suitable the least suitable, the model ranked these technologies PT5 > PT2 > PT3 > PT1 > PT4.

The current huge investments in engineering systems have created a renewed concern about their reliability and safety among scholars and practitioners. This article bridges the knowledge gap in the evaluation of reliability and safety concerns in engineering systems. It uses a literature review and qualitative content analysis method to identify the underlying factors that cause reliability and safety issues in engineering systems. The identified factors are organizational, environmental, and physical and mechanical factors. An extended Fuzzy Analytical Hierarchy Process (FAHP) method was used to determine rank these factors criticality to accidents occurrence in engineering systems. FAHP’s applicability was demonstrated using cranes as a case study. Based on the analysis of three experts’ evaluations, the environmental factor is the most critical accidents in engineering systems. On the other hand, the organizational factor is the least critical factor to accidents in engineering systems. The study will be beneficial to product development/manufacturing companies. Its findings could provide practitioners and scholars with sound knowledge of the implications of reliability and safety issues on engineering systems accidents.

Product development is an integral part of an organization that plans to stay relevant and competitive in today’s world. Several companies have research and development sections, which are most times responsible for developing new products. Still, there are other processes such as technology development, technology acquisition, capacity acquisition, and project management in which product development can be achieved. Improved product development processes such as lean manufacturing, six sigma, and a hybrid of the two known as lean six sigma (LSS) are platforms that enhance product development, which helps organizations remain relevant.

Across the world, machining learning (ML) algorithms, such as support vector machines (SVR) and artificial neural networks (ANN), are among scientific tools for the fourth industrial revolution, or Industry 4.0, campaign. These algorithms have wide engineering applications, but their potentials in energy management are still evolving. Hence, this study investigates the performance of SVR and ANN algorithms as predictive models for wind turbines capacity factor (CF) estimation. Five independent parameters-wind speed, power density, turbulence intensity, installed capacity, and wind shear - were used as input parameters for this estimation problem. Polynomial, radial basis function (RBF), and linear kernels were used to train an SVR model that estimates CF, while Adams was used to optimize the performance of a backpropagation ANN model. These models’ applicability was evaluated using data sets from eight locations. This study used correlation coefficient used to compared the model performance. The RBF trained SVR model performed better than the other kernels during model training, but the linear kernel trained SVR model performed better than the other kernels during testing model. When these kernels performances were compared with a single hidden layer ANN model, the ANN model results were better than these kernels results.

As the world embraces the fourth industrial revolution, the relationship between customers and their clients will change. The rate of this change might vary from one organization to another; revenues from existing or new businesses that refuse to embrace this revolution will experience a steady decrease. Utility firms, whose operations are affected by this revolution, must devise a means of estimating the number of customers that will deflect from a grid. This article, therefore, presents a system dynamics (SD) model for utility firms’ revenue estimation. The model was used to study the relationship among the parameters that influence households’ connection and disconnection from a grid. A case study of households in South Africa was used to test the proposed model applicability. We observed that the model responses to change in internal and external parameters that affect utility firms’ revenue. When the administrative factor is 0.4, less number of households disconnect from the grid when it was compared with an administrative factor of 0.6. In addition, there was an increase of 12.04% in revenue when the administrative factor was changed from 0.4 to 0.6. Also, there was a 15.16% increase in revenue when the technological advancement factor was changed from 0.4 to 0.6. The model’s results show that it can be used to support a decision-making process in a utility firm.

Portfolio selection is a business process which has helped organisations identify an area of com-petitive advantage and it is a major concern to industrial players in the banking sectors. In order to enhance bank portfolio selection, cost, profitability, time and location are important parameters that decision-makers often consider. This study implements a fuzzy-TOPSIS (Technique for Or-der Preference by Similarity to Ideal Solution) framework to evaluate three potential portfolios (automated teller machine gallery, quick service point and branch) for a bank using the infor-mation from three decision-makers. An illustrative example of real bank information is used to demonstrate the proposed framework applicability. The complex proportional assessment of al-ternatives (COPRAS) method is also used as an evaluation technique and the results are com-pared, which yields that the results from the ranking order of fuzzy-TOPSIS and COPRAS were different. However, there is a consistency between the aggregation of intuition-based, fuzzy-TOPSIS and COPRAS ranks and fuzzy-TOPSIS ranking results. The presented framework is an easy-to-apply tool that improves portfolio selection decision in the banking system.

The growing interest in technicians' workloads research is probably associated with the recent surge in competition. This was prompted by unprecedented technological development that triggers changes in customer tastes and preferences for industrial goods. In a quest for business improvement, this worldwide intense competition in industries has stimulated theories and practical frameworks that seek to optimise performance in workplaces. In line with this drive, the present paper proposes an optimisation model which considers technicians' reliability that complements factory information obtained. The information used emerged from technicians' productivity and earned-values using the concept of multi-objective modelling approach. Since technicians are expected to carry out routine and stochastic maintenance work, we consider these workloads as constraints. The influence of training, fatigue and experiential knowledge of technicians on workload management was considered. These workloads were combined with maintenance policy in optimising reliability, productivity and earned-values using the goal programming approach. Practical datasets were utilised in studying the applicability of the proposed model in practice. It was observed that our model was able to generate information that practicing maintenance engineers can apply in making more informed decisions on technicians' management.

End milling machining performance indicators such as surface roughness, tool wear and machining time are the principally indicators of machine tool industrial productivity, cost and competitiveness. Since accurate predictions and optimisations are necessary for control purposes, new merit-driven approaches are for good results. The aim of this work is two folds: prediction of machining performance for surface roughness, tool wear and machining time with ANN and the optimisation of these performance indicators using the combined models of ANN-BB-BC and ANN-PSO. However, the optimisation platform is hinged on the fuzzy goal programming model, which facilitates comparisons between the performance of the BB-BC and the PSO algorithms. To demonstrate the approach, optimal tool wear and surface roughness were obtained from a fuzzy goal programme, then converted to a bi-objective non-linear programming model, and solved with the BB-BC and the PSO algorithms. The outputs of the artificial neural network (ANN) were integrated with the optimisation models. The effectiveness of the method was ascertained using extensive literature data. Thus, prediction and optimisation of complex end milling parameters was attained using appropriate selection of parameters with high quality outputs, enhanced by precise prediction and optimisation tools in this proposed approach.

Today, maintenance plays a significant responsibility in all stages of equipment life due to strong government interests in environmentally conscious manufacturing. The need of the hour is to ignore the sparse attention to sustainable maintenance research and pursue valuable links between maintenance strategy and sustainable maintenance. In maintenance strategy choice, available reports have not sufficiently addressed the imbalance caused by uncertainties in maintenance practices. In addition, current reports on maintenance strategy sustainability focus on technical and economic aspects of maintenance and scantily treat environmental, social and safety criteria. This affects the quality of decisions in maintenance systems. To remedy this situation, this study applies fuzzy entropy weight and PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluation) in ranking maintenance sustainability strategies. The proposed approach is tested in a cement plant. Based on the choice criteria, the PROMETHEE methods results identified the best maintenance strategy as maintenance optimisation strategy. Workforce training strategy was identified as the worst maintenance sustainability strategy. These obtained results were compared with fuzzy TOPSIS (Technique of Preference Order by Similarity to Ideal Solution) approach and the practical application of the approach was verified. The results serve as a basis and a platform for further application of the approach in other manufacturing companies.

In today’s global economy, the introduction of new methods, designs, products and processes have been accelerating in geometric progression due to changing consumers’ needs in the face of global competitions and technological advancement in product development. Product development managers and design stakeholders are facing real pressure to continuously adopt new system architecture and operational parameters to remain competitive. The viability of the product development and manufacturing firm now depends largely on how well they are able to respond to these changes in order to meet the customer requirements while becoming lean. The study will also provide a tool for monitoring the relationship between product performance and the Lean Product Development Tool (LPDT). It evaluates the relationship between Maggi spice production, of a Consolidated Foods Limited, in Nigeria and its relationship with DMAIC (Define, Measure, Analyse, Improve and Control) model, a lean six sigma tool, for evaluating root causes of product defects. At the end of the lean implementation, product performance of the pilot line, Omori Line 1 increases from 60% to 76% and a cost savings of NGR 12.03 million were realized at the end of the project.

This paper formulates a non-linear integer programming model to solve a maintenance workforce sizing problem with a productivity improvement goal. This problem is modelled in a bi-objective framework that minimises the number of maintenance personnel while maximising their productivity levels. Inputs into the optimisation model include monthly and routine maintenance periods, volume of production, contingency maintenance time, use factor and priority factor among others. The model has been validated with real-life detergent factory data, demonstrating its potential usefulness. A principal novelty of the model is the inclusion of use factor, which captures how often maintenance technicians are busy on the job with respect to assigned tasks, including unanticipated high maintenance workload. The model has been solved using a branch and bound algorithm. The impact of workforce structure and workers’ salaries on model’s performance has been studied and sensitivity analysis carried out to investigate the changes to the optimal solution as a result of changes in the input data. The results show a reduction in the number of maintenance workforce personnel in comparing values with and without use factor in the model. The model’s ability to obtain global optimal result depends on the value of the minimum routine maintenance time within each maintenance section. Also, the model is shown to be sensitive to priority factor, which captures the appropriate ratio of full- and part-time workers under the same category. The model provides both an easy-to-use practical tool for maintenance managers and supervisors as well as a scientific tool for determining optimal maintenance workforce size for a manufacturing plant.

Prompt leak location and leak area determination in oil and gas pipeline installations is an indispensable approach to controlling petroleum products wastages in pipes. However, there is an evident lack of literature information on this subject. In this paper, we modelled leak location detection and leak area determination in pipes by applying two methodologies and gave an illustrative example using simulated data with the aid of Matlab. A comparison of these two approaches resulted in an error of 6.24%, suggesting that the closer the leak is to the measurement station, the lower will be the time interval between two successive waves that will pass through the leak and get to the measurement station. The relationship between the pipe area and coefficient of reflection is parabolic. This contribution is valuable to pipeline engineers in the economic control of leaks.

Small scale enterprises constitute an important subset of manufacturing economy and the contribution of facility redesign in bridging the performance gaps in small-scale enterprises is necessary for enterprise growth and stability. In this paper, a bi-objective programming-based facility layout design problem is formulated. We minimise workforce costs and maximise efficiency improvement in a layout. We utilised fuzzy goal programming and big-bang big-crunch algorithm in generating a Pareto solution. The model was tested using a small-scale sachet water production enterprise data. Increase in finished goods area was 66.55 % while reduction in total annual distance travelled in the facility was 48.22 % when the proposed layout was compared with existing one. Reduction in annual cost of labour was 16.2 %. The possibility of using work-centres with high number of interrelationship was confirmed using quality function deployment and Hurwicz criterion. The optimal workforce size was found to be 14 workers against the existing workforce strength of 18 workers. The study provides a framework upon which small-scale sachet water production factories can be designed for optimum performance.