Multi-Mode Resource-Constrained Project Scheduling Problem with Technological Performance Measures
The first part of this research suggests a model-based approach for linking strategic project decisions, such as the selection of design modes and technologies, to tactical and operational decisions, as well as setting specific schedules and allocating resources to project activities. We develop a model that combines quantitative project management models that focus on time and cost with value-focused qualitative methodologies by extending the multi-mode resource-constrained project scheduling problem (MRCPSP) to include value aspects (e.g., technical performance). A genetic algorithm solves the problem to near optimality in reasonable computational times. An efficient-frontier of project plans, where each plan on the frontier achieves the best value for its cost, enables decision-makers to select their preferred plan.
The second part of this research deals with a robust optimization approach for project decisions in uncertain environments. Stochasticity is manifested by uncertain activity durations, which lead to stochastic resource demands. The objective is to define a project plan that includes selected modes, resource allocations and a project schedule that minimize the worst-case project duration, under polyhedral uncertainty sets. A Benders decomposition approach is proposed to solve the robust counterpart of the suggested model.We conduct computational experiments for analyzing the price of robustness under varying levels of uncertainty. The results provide managerial insights regarding to the performance of robust policies under various conditions, compared to their respective utopian and deterministic policies. We demonstrate the conditions under which the robust approach may be favorable with respect to deterministic policies and the general conditions under which the price of robustness is relatively low.
The last part of our research develops heuristics for solving large instances of stochastic MRCPSP using the robust optimization approach. We report on the performance of the heuristics compared to an optimal procedure.