Robust optimization of port logistics service supply chain under demand uncertainties

Demand uncertainty in market environments presents substantial challenges in managing port logistics service supply chains. This paper investigates the impact of demand uncertainty on the interactions between entities within a port logistics supply chain, addressing three main issues: customer decision-making uncertainty, time constraints, and demand variability. To minimize the total cost of providing logistics solutions to customers, we propose a two-stage robust optimization model centered around a logistics integrator. In the first stage, the model selects appropriate logistics providers and shipping schedules based on fixed time constraints. The second stage adjusts logistics service arrangements according to fluctuating customer demand, utilizing a column-and-constraint generation algorithm to solve the model. The objective is to deliver optimal logistics solutions that minimize both time and cost, while meeting customer requirements. In the numerical experiments, customer decision-making uncertainty is represented by uncertain budget levels, while demand fluctuations are modeled through changes in coefficients that reflect the degree of variability in demand. The results indicate that by using uncertain budget levels to capture the impact of uncertainty on decision-making and employing polyhedral sets to represent demand uncertainty, the two-stage robust optimization method effectively addresses demand uncertainty in port logistics service supply chains. This study offers valuable insights for optimizing port logistics service supply chains.