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PurposeThere is a large amount of published literature on project management. However, there exists a gap between the existing literature and current practices in the industry for the development and execution of megaprojects. Existing literature generally focuses on individual elements applicable to project management in general. This article aims to provide an overview of the project management system components used in industrial megaprojects and identify the gaps between theory and practice, which can be used as input for further research on the topic.Design/methodology/approachThe topic of megaproject management is reviewed based on available literature sources on megaproject management systems to identify the main gaps in the literature between theory and practice. Based on the findings, an analysis is provided to discuss the improvements required in distinct project management areas and phases.FindingsThere are multiple gaps associated with issues, failures, successes and challenges in industrial megaprojects. Improvements are needed in distinct management areas and over the entire project lifetime. Further guidelines are required for achieving improved megaproject management systems. Such concepts could benefit researchers and practitioners in streamlining their research toward the most relevant and critical areas of improvement of megaproject management systems.Originality/valueThis study addresses the literature gaps between theory and practices on megaproject management systems with an overview that provides helpful guidance for industrial applications and future research. A holistic analysis identifies gaps and critical drives in the body of knowledge, revealing avenues for future research focused on quality as the central pillar that affects the entire megaproject management system.

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For high-performance operations in crude oil refinery processing, it is important to properly determine yields and properties of output streams from distillation units. To address such complex representation, we propose a cutpoint temperature-modeling framework using a coefficient setup MIQP (mixed-integer quadratic programming) technique to determine optimizable surrogate models to correlate independent X variables (crude oil compositions, temperatures, etc) to dependent Y variables (such as stream yields and properties of distillates). The X inputs are systematically generated by Latin hypercube sampling (LHS), and the experiments to obtain the synthetic Y outputs are simulated using the well-known conventional and improved swing-cut methods. By using these optimizable surrogate models (which are suitable to handle continuous data from the process) with measurement feedback (for adjustments and improvements), distillation outputs can be continuously updated when needed. The proposed approach successfully builds accurate surrogates for the distillation unit, which can be embedded into complex planning, scheduling, and control environments. Moreover, this MIQP surrogate identification technique may also be applied to other types of downstream process optimization problems such as reacting and blending unit operations, as well as other separating processes.