In this dissertation we address large, multiple-mode, resource-constrained project scheduling problems with the objective of minimizing makespan. After noting that projects often fail and new research is needed, we provide the formal definition of the resource-constrained project scheduling problem and review the existing literature. We then introduce a new model based on digital electronics. We conceptualize our model using agent technology and discuss it as extension of existing models with more representational power. We also describe how our model supports distributed planning. After implementing our model, we conduct two computational studies. In the first, we develop two agent types: basic and enhanced where the enhanced agent is more sophisticated in selecting an activity execution mode. We apply these agents to the scheduling of 500 instances of a small project originally published by Maroto and Tormos (1994). We evaluate the performance of the agents in conjunction with their use of eight heuristic prioritization rules: shortest and longest processing time, fewest and most immediate successors, smallest and greatest resource demand, earliest start time, and earliest due date. Our results show that enhanced agents consistently outperform basic agents while the results regarding priority rules were mixed. In the second computational study, we further develop our enhanced agents by providing still more sophisticated mode selection. We also evaluate static versus dynamic prioritization and two more priority rules: shortest and longest duration critical path. For this study we generated 2500, 5000, 7500, and 10000 activity projects. For each of these, we generated networks with complexities of 1.5, 1.8, and 2.1. For these twelve networks, we generated 20 problem instances for every possible combination of resource factor = 0.25, 0.50, 0.75, 1.0 and resource strength = 0.2, 0.5, 0.8. We graphically evaluated scheduling performance, computation times, and failure rates and conducted an extensive statistical analysis. We found that enhanced agents using shortest processing time priority consistently produced the shortest schedules. However, these agents fail more often than basic agents. We found that dynamic prioritization requires more computation time, but provides little improvement in scheduling performance. We conclude this work with suggestions for future research.
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