Multistage stochastic optimization for drayage procurement in container logistics using stochastic dual dynamic programming

TL;DR

This paper develops a multistage stochastic optimization model using SDDP for drayage procurement in container logistics, effectively capturing cargo flow uncertainties and correlations to improve operational decision-making.

Contribution

It introduces a novel multivariate count time series approach for modeling cargo flows and proposes an efficient stopping rule for SDDP, enhancing scalability and solution quality.

Findings

SDDP effectively approximates high-quality policies in realistic scenarios.

Inflow uncertainties significantly impact costs, while market variability has less effect.

The proposed stopping rule balances computational efficiency and solution accuracy.

Abstract

Truckload procurement plays a vital role in integrated container logistics, particularly under the uncertainties of container flow and market conditions. We formulate the operational volume allocation problem in drayage procurement as a multistage stochastic transportation problem and solve it using stochastic dual dynamic programming (SDDP). We employ a multivariate count time series approach from the literature to model cargo flow dynamics, relaxing independence assumptions and capturing complex correlations. Our numerical experiments demonstrate the scalability of SDDP and its effectiveness in approximating high-quality policies across realistic problem instances. Sensitivity analyses highlight the significant impact of inflow uncertainties on costs, while spot market variability has a comparatively minor effect. Additionally, we propose an alternative stopping rule for SDDP iterations, balancing computational efficiency and solution fidelity.