Date of Award

2018-01-01

Degree Name

Master of Science

Department

Industrial Engineering

Advisor(s)

Jose Espiritu

Abstract

This work presents a new multi-objective evolutionary algorithm (MOEA), capable of obtaining a system's reliability while considering other objectives that concurrently need to be optimized. The algorithm is applied in two different case studies. One of the problems considers a multi-state, multi-objective renewable energy system which tries to find a configuration between solar panels and wind turbines that maximizes the system's reliability of achieving specific energy demand, while also minimizes the purchasing cost of the system. The second case study analyzes a flexible manufacturing system that contains different machines that can perform different functions. The primary objective is to obtain an optimal arrangement of a multi-state, flexible manufacturing system, considering the maximization of the overall system's availability and minimization of the CO2 emissions. These case studies use the Universal Generating Function to determine the system's reliability and the other objectives deliberated by each problem. Since the problems studied are multi-objective optimization problems, a single solution is not expected to be obtained, rather, a strong set of solutions called Pareto optimal solutions will be retrieved.

Language

en

Provenance

Received from ProQuest

File Size

104 pages

File Format

application/pdf

Rights Holder

Luis Ramirez

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