A risk-averse distributionally robust project scheduling model to address payment delays

Delays in payments have become a common risk factor for industrial projects, especially in recent years, since the financial position of firms has been threatened by pandemics, wars, inflation, and major supply chain disruptions. These delays create a time lag between expenses and payments, potentially leading to cash shortages that can have significant negative effects on the project success. To address cash shortage issues, project contractors often explore alternative financing options. The amount of money the contractor needs to borrow and when the loan is taken out considerably affects the overall project cost. In this paper, we present a distributionally robust model for effective cash flow management that minimizes the financing cost by accurately estimating the amount and timing of the expenses and revenues throughout the project life cycle. For the proposed model, we develop a heuristic algorithm that solves the problem efficiently. The performance of the heuristic is compared to the best-known solutions generated within a time limit by an off-the-shelf exact solver. Our results show that our algorithm is very competitive and can generate better solutions in substantially less time. •We present a distributionally robust model for effective cash flow management.•The model considers the randomness in payment delays, a common risk for firms.•The model minimizes the financing cost, optimes the cash flows.•The problem is cast as two-stage stochastic mixed integer program.•Our heuristic algorithm solves the problem efficiently.