Combining Machine Learning and Operations Research Methods to Advance the Project Management Practice

Abstract

Project Management is a complex practice that is associated with a series of challenges such as handling of conflicts and dependencies in resource allocation, fine tuning of projects to avoid fragmented planning, handling of potential opportunities or threats during the execution of a project, and alignment between projects and business objectives. Traditionally, methods and tools to address these issues are based on analytical approaches developed in the realm of the Operations Research discipline. Aiming to facilitate and augment the quality of the Project Management practice, this paper proposes a hybrid approach that builds on the synergy between contemporary Machine Learning and Operations Research techniques. Based on past data, Machine Learning techniques can predict undesired situations, provide timely warnings and recommend preventive actions regarding problematic resource loads or deviations from business priority lists. The applicability of our approach is demonstrated through two real examples elaborating two different datasets. In these examples, we comment on the proper orchestration of the associated Operations Research and Machine Learning algorithms, paying equal attention to both optimization and big data manipulation issues.