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STOCHASTIC RESOURCE CONSTRAINED PROJECT SCHEDULING WITH STOCHASTIC TASK INSERTION PROBLEMS
Title
STOCHASTIC RESOURCE CONSTRAINED PROJECT SCHEDULING WITH STOCHASTIC TASK INSERTION PROBLEMS.
Creator
Archer, Sandra, Armacost, Robert, University of Central Florida
Abstract / Description
The area of focus for this research is the Stochastic Resource Constrained Project Scheduling Problem (SRCPSP) with Stochastic Task Insertion (STI). The STI problem is a specific form of the SRCPSP, which may be considered to be a cross between two types of problems in the general form: the Stochastic Project Scheduling Problem, and the Resource Constrained Project Scheduling Problem. The stochastic nature of this problem is in the occurrence/non-occurrence of tasks with deterministic...
Show moreThe area of focus for this research is the Stochastic Resource Constrained Project Scheduling Problem (SRCPSP) with Stochastic Task Insertion (STI). The STI problem is a specific form of the SRCPSP, which may be considered to be a cross between two types of problems in the general form: the Stochastic Project Scheduling Problem, and the Resource Constrained Project Scheduling Problem. The stochastic nature of this problem is in the occurrence/non-occurrence of tasks with deterministic duration. Researchers Selim (2002) and Grey (2007) laid the groundwork for the research on this problem. Selim (2002) developed a set of robustness metrics and used these to evaluate two initial baseline (predictive) scheduling techniques, optimistic (0% buffer) and pessimistic (100% buffer), where none or all of the stochastic tasks were scheduled, respectively. Grey (2007) expanded the research by developing a new partial buffering strategy for the initial baseline predictive schedule for this problem and found the partial buffering strategy to be superior to Selim's "extreme" buffering approach. The current research continues this work by focusing on resource aspects of the problem, new buffering approaches, and a new rescheduling method. If resource usage is important to project managers, then a set of metrics that describes changes to the resource flow would be important to measure between the initial baseline predictive schedule and the final "as-run" schedule. Two new sets of resource metrics were constructed regarding resource utilization and resource flow. Using these new metrics, as well as the Selim/Grey metrics, a new buffering approach was developed that used resource information to size the buffers. The resource-sized buffers did not show to have significant improvement over Grey's 50% buffer used as a benchmark. The new resource metrics were used to validate that the 50% buffering strategy is superior to the 0% or 100% buffering by Selim. Recognizing that partial buffers appear to be the most promising initial baseline development approach for STI problems, and understanding that experienced project managers may be able to predict stochastic probabilities based on prior projects, the next phase of the research developed a new set of buffering strategies where buffers are inserted that are proportional to the probability of occurrence. The results of this proportional buffering strategy were very positive, with the majority of the metrics (both robustness and resource), except for stability metrics, improved by using the proportional buffer. Finally, it was recognized that all research thus far for the SRCPSP with STI focused solely on the development of predictive schedules. Therefore, the final phase of this research developed a new reactive strategy that tested three different rescheduling points during schedule eventuation when a complete rescheduling of the latter portion of the schedule would occur. The results of this new reactive technique indicate that rescheduling improves the schedule performance in only a few metrics under very specific network characteristics (those networks with the least restrictive parameters). This research was conducted with extensive use of Base SAS v9.2 combined with SAS/OR procedures to solve project networks, solve resource flow problems, and implement reactive scheduling heuristics. Additionally, Base SAS code was paired with Visual Basic for Applications in Excel 2003 to implement an automated Gantt chart generator that provided visual inspection for validation of the repair heuristics. The results of this research when combined with the results of Selim and Grey provide strong guidance for project managers regarding how to develop baseline predictive schedules and how to reschedule the project as stochastic tasks (e.g. unplanned work) do or do not occur. Specifically, the results and recommendations are provided in a summary tabular format that describes the recommended initial baseline development approach if a project manager has a good idea of the level and location of the stochasticity for the network, highlights two cases where rescheduling during schedule eventuation may be beneficial, and shows when buffering proportional to the probability of occurrence is recommended, or not recommended, or the cases where the evidence is inconclusive.
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Date Issued
2008
Identifier
CFE0002491, ucf:47673
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002491
A Dynamic Enrollment Simulation Model for Planning and Decision-Making in a University
Title
A Dynamic Enrollment Simulation Model for Planning and Decision-Making in a University.
Creator
Robledo, Luis, Sepulveda, Jose, Kincaid, John, Armacost, Robert, Archer, Sandra, University of Central Florida
Abstract / Description
Decision support systems for university management have had limited improvement in the incorporation of new cutting-edge techniques. Most decision-makers use traditional forecasting methods to base their decisions in order to maintain financially affordable programs and keep universities competitive for the last few decades. Strategic planning for universities has always been related to enrollment revenues, and operational expenses. Enrollment models in use today are able to represent...
Show moreDecision support systems for university management have had limited improvement in the incorporation of new cutting-edge techniques. Most decision-makers use traditional forecasting methods to base their decisions in order to maintain financially affordable programs and keep universities competitive for the last few decades. Strategic planning for universities has always been related to enrollment revenues, and operational expenses. Enrollment models in use today are able to represent forecasting based on historical data, considering usual variables like student headcount, student credit, among others. No consideration is given to students' preferences. Retention models, associated to enrollment, deal with average retention times leaving off preferences as well.Preferences play a major role at institutions where students are not required to declare their intentions (major) immediately. Even if they do, they may change it if they find another, more attractive major, or they may even decide to leave college for external reasons.Enrollment models have been identified to deal with three main purposes: prediction of income from tuition (in-state, out-of-state), planning of future courses and curriculum, and allocation of resources to academic departments, This general perspective does not provide useful information to faculty and Departments for detailed planning and allocation of resources for the next term or year. There is a need of new metrics to help faculty and Departments to reach a detailed and useful level in order to effectively plan this allocation of resources. The dynamics in the rate-of-growth, the preferences students have for certain majors at a specific point of time, or economic hardship make a difference when decisions have to be made for budgets requests, hiring of faculty, classroom assignment, parking, transportation, or even building new facilities. Existing models do not make difference between these variables.This simulation model is a hybrid model that considers the use of System Dynamics, Discrete-event and Agent-based simulation, which allows the representation of the general enrollment process at the University level (strategic decisions), and enrollment, retention and major selection at the College (tactical decisions) and Department level (operational decisions). This approach allows lower level to more accurately predict the number of students retained for next term or year, while allowing upper levels to decide on new students to admit (first time in college and transfers) and results in recommendations on faculty hiring, class or labs assignment, and resource allocation.This model merges both high and low levels of student's enrollment models into one application, allowing not only representation of the current overall enrollment, but also prediction at the College and Department level. This provides information on optimal classroom assignments, faculty and student resource allocation.
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Date Issued
2013
Identifier
CFE0005055, ucf:49970
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005055