Industrial Wastes Risk Ranking with TOPSIS, Multi Criteria Decision Making Method

Today, various types of industrial waste are produced in different industries to meet human demands. Growth in quantity as well as complication in quality of these wastes are followed by the advance of technology. Management of such wastes need a proper identification and comprehensive understanding of the risk, emerging after the harmful characteristics of the wastes and negatively affect the human and environment health. Wastes risk ranking systems, in this regard, links between the industrial wastes indices and mathematical method/algorithm, being able at estimation of the risk level as well as comparison between the wastes of an industrial unit based on the risk level. Complexity of the method, high computational costs and lack of proper description of waste using selected indices in former studies has led to the proposal of an applicable and flexible method. In this study, the “TOPSIS Multi-Criteria Decision-Making (MCDM) method” was developed in order for ranking the risk of various industrial wastes. Totally, a number of 9 subsidiary indices on the human health and 11 subsidiary indices on the environment health was identified and employed. Finally, the proposed waste risk ranking system was used for ranking 9 types of identified industrial waste in three industrial section. Results show that the “TOPSIS MCDM”, due to the lack of complexities in method and limited computational costs, is an efficient and appropriate method for ranking industrial wastes

Selection Of Project Managers In Construction Firms Using Analytic Hierarchy Process (AHP) And Fuzzy Topsis: A Case Study

Selecting a project manager is a major decision for every construction company. Traditionally, a project manager is selected by interviewing applicants and evaluating their capabilities by considering the special requirements of the project. The interviews are usually conducted by senior managers, and the selection of the best candidate depends on their opinions. Thus, the results may not be completely reliable. Moreover, conducting interviews for a large group of candidates is time-consuming. Thus, there is a need for computational models that can be used to select the most suitable applicant, given the project specifications and the applicants’ details. In this paper, a case study is performed in which a Fuzzy Multiple Criteria Decision Making (FMCDM) model is used to select the best candidate for the post of project manager in a large construction firm. First, with the opinions of the senior managers, all the criteria and sub-criteria required for the selection are gathered, and the criteria priorities are qualitatively specified. Then, the applicants are ranked using the Analytic Hierarchy Process (AHP), approximate weights of the criteria, and fuzzy technique for order performance by similarity to ideal solution (TOPSIS). The results of the case study are shown to be satisfactory

Decision-maker Trade-offs In Multiple Response Surface Optimization

The focus of this dissertation is on improving decision-maker trade-offs and the development of a new constrained methodology for multiple response surface optimization. There are three key components of the research: development of the necessary conditions and assumptions associated with constrained multiple response surface optimization methodologies; development of a new constrained multiple response surface methodology; and demonstration of the new method. The necessary conditions for and assumptions associated with constrained multiple response surface optimization methods were identified and found to be less restrictive than requirements previously described in the literature. The conditions and assumptions required for a constrained method to find the most preferred non-dominated solution are to generate non-dominated solutions and to generate solutions consistent with decision-maker preferences among the response objectives. Additionally, if a Lagrangian constrained method is used, the preservation of convexity is required in order to be able to generate all non-dominated solutions. The conditions required for constrained methods are significantly fewer than those required for combined methods. Most of the existing constrained methodologies do not incorporate any provision for a decision-maker to explicitly determine the relative importance of the multiple objectives. Research into the larger area of multi-criteria decision-making identified the interactive surrogate worth trade-off algorithm as a potential methodology that would provide that capability in multiple response surface optimization problems. The ISWT algorithm uses an ε-constraint formulation to guarantee a non-dominated solution, and then interacts with the decision-maker after each iteration to determine the preference of the decision-maker in trading-off the value of the primary response for an increase in value of a secondary response. The current research modified the ISWT algorithm to develop a new constrained multiple response surface methodology that explicitly accounts for decision-maker preferences. The new Modified ISWT (MISWT) method maintains the essence of the original method while taking advantage of the specific properties of multiple response surface problems to simplify the application of the method. The MISWT is an accessible computer-based implementation of the ISWT. Five test problems from the multiple response surface optimization literature were used to demonstrate the new methodology. It was shown that this methodology can handle a variety of types and numbers of responses and independent variables. Furthermore, it was demonstrated that the methodology can be successful using a priori information from the decision-maker about bounds or targets or can use the extreme values obtained from the region of operability. In all cases, the methodology explicitly considered decision-maker preferences and provided non-dominated solutions. The contribution of this method is the removal of implicit assumptions and includes the decision-maker in explicit trade-offs among multiple objectives or responses

Optimisation and modification of TiO2 nanorods towards a highly efficient interfacial layer for uniformly CU2O deposition in heterojunction solar cell applications

The deficiency of successful heterojunction solar cell development detains the future implementation of oxide-based photovoltaic devices. In the group of semiconducting compounds, titanium dioxide (TiO2) which has superior properties in solar cell development, and copper (I) oxide (Cu2O) is the best candidate that serves as an absorber layer, owing to its excellent optical properties. In this work, TiO2 with a well-aligned nanorods structure and high adhesion was synthesized on fluorinedoped tin oxide (FTO) substrate by hydrothermal method. The incorporation of Cu2O on TiO2 nanorods by electrodeposition method resulted in an inhomogeneous growth of Cu2O film attributed to a high resistivity of TiO2 film. Hydrothermal etching treatment in a highly acidic medium of hydrochloric acid (HCl) was employed to create a high efficiently interfacial layer of TiO2 with low resistivity for uniformity of Cu2O film deposition. The morphology changed, and the transmittance increased while the electrical resistivity declined, showing that the etching treatment had a substantial effect on the TiO2 thin film properties. In this study, the first fabrication of Cu2O on the etched TiO2 layer was demonstrated using the facile and easily electrodeposition method. Cu2O film was successfully deposited with high uniformity on the etched-TiO2 layer as the pyramid structure could stack perfectly on the rods layer, which is believed to improve the properties of the interfacial layer. When applied in photovoltaic solar cell, the heterojunction thin film exhibited an efficiency of 0.0138% under an irradiance of 100 mW/cm of the TiO2 film etching for 5 hours deposited with Cu2O for 90 minutes. Even though the application of Cu2O/etched-TiO2-nanorods in thin-film solar cells presents a meagre photovoltaic performance, far from the theoretical limit reports, this work could motivate other researchers to do the enhancement through intensive research for both TiO2 and Cu2O materials

An optimal load shedding scheme based on the analytical heirarchy approach: a case study of the Selangor Electrical System

Most people depend on electrical energy in every aspect of their life. All sectors in Malaysia really need electrical energy to remain stable and consistent. As consumers, the public are want to have constant distributed of electricity energy without any disturbances. For example, food industries will be experiencing large financial lost if there are disturbances in electrical supplies even for only one day. If the total electrical load power demand greatly exceeds than the power supplied and no decision-making in removing a certain load, it will affect to the power system. Certain loads will be have to remove and needs some decision-making process in order to choose the best load(s) to be cut off. The load shedding process automatically detects overload conditions, then shed enough load to relieve the overloaded equipment before there is loss of generation, line tripping, equipment damage, or a chaotic random shutdown of the system. In this paper, an analysis is made to find the best method to be applied in load shedding. Analytical Hierarchy Process (AHP) and Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) is two methods most widely applied techniques MADM/MCDM problem. By using the AHP and TOPSIS methods, the priority of the load can be determined. This paper is focusing on the analysis of alternative methods in choosing the load priority of load shedding scheme in Selangor Electrical system. By using the AHP and TOPSIS methods, both have its own advantages in approach to determine the sequences of load to be shed