PROJECT SELECTION, SCHEDULING AND RESOURCE ALLOCATION FOR ENGINEERING DESIGN GROUPS

Abstract

This dissertation examines a profit-maximizing project selection and scheduling problem. Assume that a set of potentially profitable projects are available, yet limited available resources may not allow all of them to be pursued. Profit profiles for projects are assumed to be non-increasing functions of project completion times, i.e. profit returns are sensitive to time-to-market. Decision needs to be made on which sub-set of projects should be chosen and how resources should be allocated to these projects such that the total profit is maximized.Formal mathematical models are formulated for various versions of the problem, including such ones incorporating a third team formation aspect. Structure of the problem is examined and insights are gained regarding prioritization of project, specifically. Although prioritization is sub-optimal in general, heuristic solution methods based on prioritization are pursued, since the scheduling sub-problem itself is NP-hard.A decomposition heuristic framework is first proposed to obtain good solutions using minimum computational time. Sets of test instances are generated using project network data from well-known source in the literature. Computational runs reveal that three priority rules achieve significantly better profits than the benchmarking random priority rule.Improving upon the prioritization based decomposition heuristic, an implicit enumeration is proposed. This algorithm does not examine all priority sequences, yet guarantees an optimal priority sequence when the computation is completed. Several fathoming rules are proposed to cut back computational time effectively. Comparison to the profits achieved by the best priority rule and the benchmarking random priority rule shows a significant improvement on profits, yet at a cost of reasonable added computation time.Future research areas include identifying general conditions under which prioritization of projects would lead us to an optimal solution. Developing better upper bounds for the implicit enumeration scheme is also of interest. The team formation aspect has yet to be treated computationally. It would also be of interest to consider how synergy deviation information may be fed back to the earlier stages of project selection and scheduling decision. Trade-off between profit and team synergy may also be considered in the future.