Developing an Ant Colony Optimization System For Scheduling Repetitive Projects With Multiple-objectives

Abstract

Repetitive Scheduling Projects (RSP) are common in a number of different situations such as highway construction, pipeline networks, bridges, railways, and airport runways. Several solution approaches have been presented to solve different models of RSP problem, but they all have their own disadvantage especially if the problem has multi-objective functions. This thesis presents a new approach called Hybrid Intelligent Approach (HIA) to solve Multi­ objective RSP (MORSP) problem. This approach incorporates both a genetic algorithm (GA) and an ant colony (AC) in a hybrid approach to get a powerful search methodology. Comparisons between HIA and current available solution approaches are made and numerical examples are given to illustrate the implementation of the approach.