GA-based multi-objective optimization of finance-based construction project scheduling

Abstract

From a financial management perspective, the profitability of a construction project is connected to the cash requirements of the project and the ability of a company to procure cash at the right time. Line of credit as a bank credit agreement provides an alternative way of managing the necessary capital and cash flow for the project. Today’s highly competitive business environment necessitates comprehensive scheduling with respect to cash providing provisions and restrictions. This paper presents a multi-objective elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) based optimization model for finance-based scheduling which facilitates the decision making process of the most appropriate line of credit option for cash procurement. Finance-based scheduling modifies the initial schedule of the project so that its maximum negative cash flow is limited to a specific credit limit. Furthermore, this paper suggests several improvements to basic NSGA-II and demonstrates how they significantly enhance the efficiency of the model in searching for non-dominated solutions. The proposed model is validated by a designed benchmark problem, and its performance and merits are illustrated through its application to a case example. It is shown that the model can effectively approach to the optimal Pareto set and maintain diversity in solutions.