DEPARTMENT OF INDUSTRIAL AND PRODUCTION ENGINEERING
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Item A biobjective production planning model and application of three LP procedures(Nigerian Metallurgical Society, 1999) Adeyeye, A. D.; Charles-Owaba, O. E.A biobjective model is proposed for production planning in a multi-stage, multi-facility production system. The decision situation considered was a case where the Decision Maker (DM) wants to determine the quantities of materials to be fed into each production facility at each stage of production that gives maximal realization of his objectives. A numerical example is solved using three Linear Programming procedures. The methods used are: Compromise Constraint Biobjective LP (CCBLP), Linear Combination of the Objective Functions (LCOF) and Goal Programming (GP). The behaviour of the CCBLP model shows that it is superior to LCOF and non-preemptive GP in terms of its sensitivity to relaxations in the objectives. It also supports the result of an earlier research that the CCBLP gives the real compromise solution.Item Goal programming model for production planning in a toothpaste factory(South African Institute of Industrial Engineers (SAIIE), 2008) Adeyeye, A. D.; Charles-Owaba, O. E.The GP model was developed for production planning in a toothpaste factory. Two objectives were distinguished: minimization of processing cost, and maximization of the capacity utilization of production facilities. Two priority structures were used to explore the trade-off options. When processing cost minimization was assigned the first priority, the utilizations of Processing Plant 1 and Filling Machine 2 were 20.32% and 0.18% respectively. When capacity utilization was assigned first priority, the processing cost increased by 7.55% but capacity utilization improved. The least utilized facility was Filling Machine 1 with a utilization of 43.85%.Item A neuro-fuzzy linguistic approach in optimizing the flow rate of a plastic extruder process(Centre of Professional Research Publications, 2006) Oke, S.A.; Johnson, A. O.; Charles-Owaba, O. E.; Oyawale, F. A.; Popoola, I. O.The plastic extruder system is an important process in the solid waste recycling system. This paper optimizes the flow rate of this process with the application of a neuro-fuzzy model. The model identifies a specified desired output from a large number of input parameter. The methodology adopted is neuro-fuzzy. The concept of neuro-fuzzy is not new as a research methodology but new in its applied form to plastic recycling extruder process. The result obtained indicates the feasibility of applying the methodology in this instance. Thus, the study may be extended to other recycling processes apart from plastic base. The study is predicated on the need to attain more precision in the derivation of optimal values for the plastic extruder system in recycling plant. The research has strong economic implications since it has theory with an applied bias on a problem experienced by the industry. It can be used by managers in the plastic industry, or practitioner i.e. those supporting practice such as consultants or software developers, to mention a few. The work particularly has an economic justification. Intrinsically, the result obtained being an improvement over what was reported by earlier researchers is noteworthy. The paper is new in that it appears to be the first application of neuro-fuzzy in the system being researched.Item Application of fuzzy logic concept to profitability quantification in plastic recycling(2006) Oke, S. A.; Johnson, A. O.; Popoola, I. O.; Charles-Owaba, O. E.; Oyawale, F. A.This paper aims at applying a fuzzy logic control model to profitability in a case study of the plastic recycling industry in Nigeria. The studies of profitability components of the plastic recycling industry as used to develop a model framework and the application of fuzzy logic control model to the framework are given in this paper. A brief introduction to profitability concepts as well as useful suggestions and conclusions are all integral part of this paper which is based on the application of a fuzzy logic model of control to profitability concept in plastic recycling industry.Item Predicting students academic performance using artificial neural network: a case study of an engineering course(Akamai University, Hilo, HI, USA, 2008) Oladokun, V. O.; Adebanjo, A. T.; Charles-Owaba, O. E."The observed poor quality of graduates of some Nigerian Universities in recent times has been partly traced to inadequacies of the National University Admission Examination System. In this study an Artificial Neural Network (ANN) model for predicting the likely performance of a candidate being considered for admission into the university was developed and tested. Various factors that may likely influence the performance of a student were identified. Such factors as ordinary level subjects' scores and subjects' combination, matriculation examination scores, age on admission, parental background, types and location of secondary school attended and gender, among others, were then used as input variables for the ANN model. A model based on the Multilayer Perception Topology was developed and trained using data spanning five generations of graduates from an Engineering Department of University of Ibadan, Nigeria's first University. Test data evaluation shows that the ANN model is able to correctly predict the performance of more than 70% of prospective students. "Item Application of a neurofuzzy mathematical model in the development of a local oven design(Tapas Institute of Scientific Research and Development, 2009-12) Odior, A. O.; Oyawale, F. A.; Charles-Owaba, O. E.This paper presents a contribution on the development of a neurofuzzy mathematical model that aids in capturing and analyzing the various parameters in oven design. The oven was designed specifically for domestic and commercial baking operations. The neurofuzzy methodology was used to regulate the oven baking temperatures to acceptable standards. Particular use was made of neurofuzzy model since it is an improvement on the process design. The work was motivated by the need for a more reliable and easily understandable methodology that guides decision makers in making correct decisions in a timely manner. The results obtained demonstrated that it is feasible to apply the model in practice.Item A facility maintenance scheduling model incorporating opportunity and inflationary costs(Inderscience Enterprises Limited, 2008) Charles-Owaba, O. E.; Oluleye, A. E.; Oyawale, F. A.; Oke, S. A.This paper deals with facility maintenance scheduling model which incorporates opportunity and inflationary cost. A case study pertaining to a shipping firm has been defined as transportation model of minimizing Maintenance Cost (MC), Maintenance –Inflation Cost (MIC), Maintenance-Opportunity Cost (MOC) and combined Maintenance-Opportunity-Inflation Cost (MOIC), The optimal schedules indicating the ship maintenance, idle and operation periods were deduced for each approach. For all the samples, the costs of the first model were significantly (p≤0.05) different from that of the other three models. To reduce cost and delays, decisions for scheduling maintenance of a fleet of ships would be better informed if based on maintenance and opportunity cost indices in both inflationary and non-inflationary conditions.Item An application of artificial neural network to maintenance management(2006) Oladokun, V. O.; Charles-Owaba, O. E.; Nwaozuru, C. S.This study shows the usefulness of Artificial Neural Network (ANN) in maintenance planning and management. An ANN model based on the multi-layer perception having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. The model achieved an accuracy of over 70% in predicting the expected downtime.Item A set of sequencing heuristic and the computer code for the TSP.(1999) Charles-Owaba, O. E.; Oladokun, V. O.This paper proposes a set sequencing heuristic solution for the travelling salesman problem (TSP). It attempts to first select, preferably a set of M smallest elements of the TSP matrix and then form a sequence. A computer code of the procedure was developed in Fortran 77 and used to examine its efficiency and relative effectiveness. It was found to be as effective as, but more efficient than the best of the nearest neighbour heuristics.