Solving multiple criteria decision making problem in the legislative control of lifts using genetic algorithm

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Solving multiple criteria decision making problem in the legislative control of lifts using genetic algorithm


Author: Ching, Hon-kit
Title: Solving multiple criteria decision making problem in the legislative control of lifts using genetic algorithm
Degree: M.Sc.
Year: 1996
Subject: Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: iv, 83 leaves : ill. ; 30 cm
Language: English
InnoPac Record:
Abstract: This report describes the study of solving a multiple criteria decision making (MCDM) problem associated with the determination of lift inspection sequence for the purpose of monitoring the safety standard required under the Lifts and Escalators (Safety) Ordinance, Cap.327, of Laws of Hong Kong. To take into account of the time limitation, the MCDM problem is simplified into one with two conflicting lift selection criteria, which are the travel distance optimization and even selection amongst Registered Contractors (RCs). An Additive Fuzzy Programming Model is employed in this study to model the MCDM problem. In this model, the MCDM problem is handled in three subdivided stages, namely the optimization of total travel distance, the maximization of the even distribution of lift selection amongst the RCs, which is measured by a parameter Satisfaction Index, and the maximization of a fuzzy decision function. The technique of Genetic Algorithm is applied in the maximization processes for travel distance function and fuzzy decision function to handle the huge search space. The lift inspection sequence to be determined is represented in form of chromosome throughout the study. A prototype for the decision support system to handle the MCDM problem has been developed with C programming language in PC environment. Furthermore, fine tuning of the parameters of the Genetic Algorithms utilized in this prototype has been carried out so as to enhance the performance of the evolutionary process. The sets of parameter derived in the fine tuning process are used in the prototype for future lift selection exercises. The prototype was scrutinized with the use of two test cases. The results indicated that the system developed is capable of identifying the solution and is much better than random selection process in terms of effectiveness and efficiency. Hence, the prototype developed in this study forms a solid foundation for the future development of a decision support system to handle the complex optimization problem existing in the legislative control of lift industry in Hong Kong.

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