Multi-criteria optimization and automated network restructuring to mitigate construction projects delays on-the-run

TL;DR

This paper introduces MitC, a novel multi-criteria optimization tool that dynamically restructures project networks to effectively mitigate delays, considering stakeholder goals, automated adjustments, and multiple impact factors.

Contribution

The paper develops a new mitigation controller integrating non-linear optimization, Monte Carlo simulation, and preference modeling to improve delay mitigation strategies in construction projects.

Findings

MitC significantly increases the probability of completing projects on time.

The model effectively balances delay mitigation with cost and environmental considerations.

Application to a case study demonstrates improved project completion likelihood.

Abstract

Construction project management requires dynamic mitigation control ensuring the project's timely completion by a best fit for common purpose strategy for all stakeholders. Current mitigation approaches are usually performed by an iterative Monte Carlo (MC) analysis focussing on lowest-cost strategies which do not include (1) the project manager's goal-oriented behaviour, (2) automated network restructuring potential, and (3) multi-dimensional optimization criteria for best fitting mitigation strategies-criteria. Therefore, the development statement within this paper is to design a method and implementation tool that properly dissolves all the aforementioned shortcomings ensuring the project's completion date by finding the most effective and efficient mitigation strategy. To fulfill the purpose of this paper, the Mitigation Controller (MitC) has been developed using an integrative approach of non-linear optimization techniques, probabilistic Monte Carlo simulation, and preference function modeling. Compared to the conventional way of mitigating project delays. The developed MitC allows mitigating potential delays with the least negative consequences on several project criteria, such as cost, environmental impact, etc. The application of the model to the demonstrative case study shows the ability of the model to significantly increase the probability of completing the project in the given target duration. Embedding the multi-criteria evaluation in the optimization model ensures that other interests are also represented in finding the optimal strategy for project delays.