Optimizing cash flow in construction portfolios: A metaheuristic approach from the organization’s perspective

Abstract

The construction industry faces significant financial risks due to inflationary pressures and economic boom-and-bust cycles, which can result in negative cash flow and reduced profitability for project portfolios. Although various cash flow optimization models exist, many do not adequately address the combined effects of inflation, economic boom-and-bust cycles, and capital injection strategies. This gap limits their effectiveness in real-world conditions, particularly for organizations managing large construction portfolios.

This study aims to bridge this gap by developing a comprehensive model for optimizing cash flow in construction project portfolios from the organization’s perspective. The model integrates key factors such as construction cost inflation, house price inflation, and multi-stage capital injections, ensuring that cumulative negative cash flow does not exceed the available capital. Metaheuristic algorithms—including Differential Evolution (DE), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), Water Cycle Algorithm (WCA), and Teaching-Learning-Based Optimization (TLBO)—are employed to solve this NP-complete problem and maximize profitability by determining the optimal start and sale times for projects.

The model was tested on real-world construction portfolios, demonstrating a substantial improvement in cash flow management compared to traditional methods. The DE algorithm improved the objective function by up to 17.2% for larger portfolios. Sensitivity analyses revealed that portfolio performance is strongly influenced by the magnitude of capital injections and the timing of portfolio initiation within economic cycles.

Overall, this study provides a robust decision-support tool for managing financial risks, offering practical insights into optimizing cash flow and maximizing profitability in the face of inflation and market volatility.