Abstract:Modeling of composite curing process is required prior to composite production as this would help in establishing correct production parameters thereby eliminating costly trial and error runs. Determining curing profile temperatures from experiment is a huge challenge which in itself is like re-inventing the wheel of trial and error, when mathematical models of physical, chemical and kinetic properties of the constituent materials could be used in modeling the cure situation to some degree of trust. This work has modeled two types of polymer based composite materials (Aluminum filled polyester and carbon-black filled polyester) representing polymer-metal and polymer-organic composites in order to predict the possible trends during conventional autoclave heating with regards to effect of heating rate on degree of cure of the composites. The numerical models were constructed by taking into account the heat transferred by conduction through the resin/filler mixture, as well as kinetic heat generated by cure reaction. The numerical solution of the mathematical models presented were discretized using forward finite differences of the Runge Kuta Method and finally solved using MATLAB® C programming language. It was observed that Aluminum filled polyester composite responded faster to heat input-induced curing and as such was able to cure faster than polyester -carbon black composite which had much slower cure -heat input response. This implies that in the production process of polymer-organic composites, faster heating rate was necessary to input heat into the process as there was no heat of reaction released during the cure process whereas, polymer-metal composites release heat of reaction contributing to the quick transfer of heat into the metal components causing the metal components to behave as points of adhesion to the polymer matrix thereby necessitating a slower heating rate.
Portfolio selection is a business process which has helped organisations identify an area of competitive 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 Order 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 information from three decision-makers. An illustrative example of real bank information is used to demonstrate the proposed framework applicability. The complex proportional assessment of alternatives (COPRAS) method is also used as an evaluation technique and the results are compared, which yields that the results from the ranking order of fuzzy-TOPSIS and COP-RAS 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.
Project portfolio selection involves decision making and it plays a crucial role in any organization. Therefore selecting not just the right projects but also the right mix of projects for the portfolio is considered as one of the most important tasks for organisations to ensure the achievement of the corporate strategy within limited resources and capabilities of the organization. Prioritizing and selecting optimal project portfolio can be very challenging especially with a large number of projects with multiple constraints and interdependences. In an ideal world with unlimited budget the project selection process would be very straightforward. However, this is not the case in life situations. In this work, an attempt is made to address this challenge. An integer linear programming model for project selection was developed and applied in a selected organization in Nigeria. The model seeks to optimize the mix of the projects to be undertaken while keeping the total cost and project interdependency as constraints. The analysis of the results showed that a total of 11 projects out of 16 were eligible for selection in the period under review. The total cost of the selected project was 92,840,000 Naira, which was about 90% of the total budget. Ordinarily, apart from not prioritizing and obtaining an optimal project mix, the community would have spread its entire resources on the 16 projects with some of them being abandoned later. The model can also be used to plan an optimal mix of project portfolio for a future date within the limitations of a given set of constraints and interdependence.
Effective project planning in Magnetic Resonance Imaging (MRI) machine installation takes into consideration several factors including Time, Cost, Quality and Risk which are essential but conflicting factors that affect projects. These critical factors should be optimized in all projects especially those in Low and Medium Income Countries (LMIC) with limited resources and inadequate investment in medical facilities and equipment. The main objective of this study was to develop an optimization model for fuzzy Time-Cost-Quality-Risk Trade-off (TCQRT) problem for MRI machine installation project. The model was solved by Multiobjective Genetic Algorithm (MOGA) and the solutions ranked using the Technique for the Order of Preferences by Similarity to Ideal Solution (TOPSIS). The results indicate a tradeoff relationship exists among time, cost, quality and risks.
The demand for renal replacement therapy (RRT) from the growing number of patients suffering from chronic kidney disease (CKD) and end stage renal disease (ESRD) in Nigeria is reported to be on the rise. However, dialysis clinics are few with limited facilities to meet the increasing demand leading to congestion, long waiting time and increased length of stay (LOS) in dialysis clinics. This paper presents an optimisation model for scheduling patient flow in an outpatient haemodialysis clinic. The objective is to minimize patient LOS using Genetic Algorithm (GA), implemented in Python programming language with Spyder Integrated Development Environment (IDE). The model was tested using data obtained from a haemodialysis clinic, in Lagos, Nigeria. The model generated optimum LOS values (193.01, 275.02 and 390.01) minutes compared to the mean LOS values at the haemodialysis clinic (235.50, 296.62 and 424.50) minutes for the 3-hour, 4-hour and 6-hour dialysis sessions. Furthermore, a simulation experiment of patient flow in a typical haemodialysis clinic was performed by gradual variations in patient arrival rates, λ. Simulation results at (λ=0.1,0.2,0.3,0.4) revealed mean LOS (minutes) as (312.85 ± 73.45, 348.18 ± 84.89, 342.18 ± 81.30, 305, 28 ± 63.67) respectively. The optimisation model was effective in reducing patient LOS.
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