Strategic Project Management in Indian Construction Industry Based on Genetic Algorithms: A Comparative Study with Current Methods

Abstract

India has experienced familiar problems in the construction industry including delays in project completion, cost escalation and poor utilization of resources, which may be considered to be failures in managerial accounting control systems including budgeting, standard costing and variance analysis. The paper redefines these problems in the context of accounting and governance and proposes the implementation of the Genetic Algorithms (GAs) technology as a new method of optimizing projects and accounting practices. Based on the information of the large-scale infrastructure projects a GA-based model was created to balance the scheduling, costs, and allocation of resources and the results are used as a standard of costs and resources benchmarks to analyze the variance. When compared with traditional approaches to CPM, PERT, and LP, it can be revealed that the GA optimization lowers adverse cost and time variances significantly, increases efficiency ratio, and improves the overall control of the project. Other implications on financial accounting based on the findings include the acceleration of recognition of work-in-progress, timing of revenue, and decreasing overrun provisioning by using GA-based schedules. In managerial accounting, GA introduces a new standard-setting mechanism for budgeting and performance evaluation. From an assurance perspective, GA offers auditability and governance measures aligned with auditing standards for accounting estimates, while addressing ethical and governance concerns related to over-optimization and the management of algorithmic decision systems. Extensions into sustainability accounting demonstrate how incorporating ESG factors into GA fitness functions enables organizations to evaluate cost-carbon trade-offs, supporting integrated reporting. This study contributes to the existing literature by showing that GA optimization links project management with accounting theory and practice, helping to reform variance analysis, assurance frameworks, governance structures, and sustainability reporting in project-based industries. Further research is necessary to explore GA applications in ESG assurance and the behavioral aspects of managerial acceptance of algorithmic benchmarks.