Project scheduling in a lean environment to maximize value and minimize overruns

Motivated by the recent trend in delivering projects with value or benefit to stakeholders and seeking to reduce the significant fraction of projects plagued by schedule and budget overruns, researchers are looking at lean project management (LPM) as a possible solution. This paper outlines a new approach to project scheduling in an LPM framework. We develop and solve a math program for balancing project time, cost, value, and risk, seeking to maximize the project value subject to schedule and budget constraints in multimode stochastic projects. Each activity mode contains fixed and resource cost information and duration data, and may be associated with one or more value attributes, thereby integrating project and product scope. By selecting a mode for each activity, the value of the project is determined, and stability is achieved by complying with on-schedule and on-budget probability thresholds. We solve the problem by applying a reinforcement learning-based heuristic, a tool known for obtaining fast solutions in a variety of applications in uncertain environments. We validate the method by comparing the results to two benchmarks—those obtained by solving a mixed-integer program, and the values obtained by adapting a recently published genetic algorithm. Our method generates competitive values faster than the benchmarks, making this approach interesting for the planning stage of a project, when multiple project tradespace alternatives are explored and solved, and runtime is limited. Our approach can be applied by decision-makers to calculate an efficient frontier with the best project plans for given on-schedule and on-budget probabilities.