Proactive Project Scheduling with a Bi-Objective Genetic Algorithm in an R&D Department

During project execution, especially in a multi-project environment unforeseen events arise that disrupt project plans 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. Therefore, project schedules should also include solution robustness to cope with the uncertainties such that actually realized activity start times during project execution will not differ much from the baseline schedule. Constructing solution robust schedules requires proactive scheduling techniques. The literature on proactive project scheduling is relatively scarce. Leus (2003) consider the objective of minimizing the total weighted instability of the schedules from a given deadline. Herroelen and Leus (2004) develop mathematical models for the generation of stable baseline schedules. Van de Vonder et. al. (2006) propose resource flow dependent float factor heuristic as a time buffering technique relying completely on the activity weights. Lambrechts et. al. (2008) focus on disruptions caused by stochastic resource availabilities to generate stable baseline schedules. Van de Vonder et. al. (2008) introduce multiple algorithms to include time buffers in a given schedule while a predefined project due date remains respected. In a recent study, Lambrechts et. al. (2011) analytically determine the impact of unexpected resource breakdowns on activity durations and develop an approach for inserting explicit idle time into project schedules in order to protect them from possible resource unavailability. In addition to these proactive strategies, there are some risk integrated procedures. Shatteman et. al. (2008) develop a computer supported risk management system that allows to identify, analyze and quantify the major risk factors and derive the probability of their occurrence and their impact on the duration of the project activities. Creemers et. al. (2011) propose a quantitative approach that allows to address the risk response process in a scientifically-sound manner and shows that a risk-driven approach is more efficient than an activity-based approach when it comes to analyzing risks. Herroelen (2014) propose a methodology that integrates quantitative risk analysis with reliable proactive/reactive project scheduling procedures. We consider the preemptive resource constrained multi-project scheduling problem (RCMPSP) 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 projects in the R&D Department of a leading home appliances company in Turkey. Phase I of the model, uncertainty assessment phase, provides a systematic approach to assess uncertainty by identifying the most important sources of uncertainty, measuring the impacts of these factors to resource usage deviation levels of projects and their activities and generating activity deviation distributions by using the most important data mining techniques: feature subset selection, clustering and classification. Phase II, proactive project scheduling phase, proposes two scheduling approaches using a bi-objective genetic algorithm (GA). Phase III, reactive project scheduling phase, aims at rescheduling the disrupted project activities. In this paper, our focus is limited to Phase II of the three-phase approach. In Section 2, the problem and the problem environment are explained. In Section 3, we present the solution methodology and in Section 4 we present the main results obtained by the implementation of the proposed proactive project scheduling approach with real data. Finally, in Section 5 we conclude and provide suggestions for future work.