A three-phase approach for robust project scheduling: an application for R&D project scheduling

Official URL: http://risc01.sabanciuniv.edu/record=b1534442 (Table of Contents)

During project execution, especially in a multi-project environment unforeseen events arise that disrupt the project process resulting in deviations of project plans and budgets due to missed due dates and deadlines, resource idleness, higher work-in-process inventory and increased system nervousness. In this thesis, we consider the preemptive resource constrained multi-project scheduling problem with generalized precedence relations in a stochastic and dynamic environment and develop a three-phase model incorporating data mining and project scheduling techniques to schedule the R&D projects of a leading home appliances company in Turkey. In Phase I, models classifying the projects with respect to their resource usage deviation levels and an activity deviation assignment procedure are developed using data mining techniques. Phase II, proactive project scheduling phase, proposes two scheduling approaches using a bi-objective genetic algorithm (GA). The objectives of the bi-objective GA are the minimization of the overall completion time of projects and the minimization of the total sum of absolute deviations for starting times for possible realizations leading to solution robust baseline schedules. Phase II uses the output of the first phase to generate a set of non-dominated solutions. Phase III, called the reactive phase, revises the baseline schedule when a disruptive event occurs and enables the project managers to make “what-if analysis” and thus to generate a set of contingency plans for better preparation.