The engineering vice-president's problem [electronic resource]

The profitability of an engineering organization is the difference between the value of the projects it completes and the cost of the resources that it consumes. Real-world managers utilize four degrees of freedom to keep their employees fully occupied on high value projects. They add or cancel projects, adjust project completion dates, hire and reduce staff, and crash tasks. Each of these degrees of freedom may be subject to constraints. Managers attempt to maximize profitability by constructing portfolio, scheduling, and staffing plans that exploit these degrees of freedom while respecting the constraints. This is a difficult undertaking, as the solution space is large, the requisite decisions interact, and confirming the feasibility of a candidate solution is slow. I call this undertaking the Engineering Vice-President's Problem (EVPP). Despite the need for a solution to the EVPP, it appears that there is no formulation in the literature that provides a unified treatment of the EVPP's four degrees of freedom and their associated constraints. I have developed such a formulation and an accompanying algorithm that can solve modest test cases to (near) optimality in reasonable time. This research will allow managers to explore more portfolio, scheduling, and staffing alternatives than they can using manual methods, and it will increase managers' confidence in the quality and feasibility of their plans. I claim that past discrete variable approaches to solving portfolio selection and scheduling problems do not scale sufficiently to address the EVPP. I have developed a continuous variable approach that allows the richer EVPP to be solved without an attendant increase in algorithmic intricacy. This approach is a contribution to management science. My formulation and algorithm also contribute to management practice by constructing optimized schedules that respect given constraints. The formulation and algorithm provide executives in service organizations with a reliable answer to the question of "What would it take to squeeze this project in?" This research addresses engineering design work, which is knowledge intensive rather than capital intensive. Consequently, it does not model or optimize the use of capital equipment. Also, the model will be most useful to mid-size engineering organizations- those that are large enough to have employees organized into functional departments, but small enough such that each project considered has a material effect on profitability and resource consumption. Finally, the EVPP is NP-Hard, meaning that worst-case solution time rises exponentially with problem size. Future research on the EVPP could mitigate the scalability problem by exploiting the parallelism of the search algorithm. If a speedup can be achieved, then the algorithm could be embedded within a larger optimization loop. For example, users could optimize under different assumptions of task work volume or project revenue, which would give an indication of how sensitive the plan is to these uncertainties. The optimization of industrial-sized test cases could be attempted.

Advisor: Levitt Raymond E. primary advisor. Thesis advisor
Fischer Martin
Lepech Michael advisor. Thesis advisor