Integrated optimization of operations and capacity planning under uncertainty for drayage procurement in container logistics

2025-05-03

AI Summary

This paper addresses drayage cost optimization under dual uncertainties—container flow volumes and spot freight rates—in container logistics. Method: We propose a strategic-operational co-decision framework that jointly optimizes long-term capacity reservation and real-time dispatch allocation. Innovatively, we embed a dynamic programming–driven real-time dispatch policy into a unified stochastic optimization model for capacity reservation, thereby transcending conventional hierarchical decision-making paradigms. The approach integrates sample average approximation (SAA), quasi-Newton optimization, and Monte Carlo simulation. Results: Empirical evaluation across four planning horizons yields a 21.2% reduction in total cost. Cross-scenario Monte Carlo validation confirms strong generalizability and robustness, significantly enhancing decision quality and economic efficiency under uncertainty.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We present an integrated framework for truckload procurement in container logistics, bridging strategic and operational aspects that are often treated independently in existing research. Drayage, the short-haul trucking of containers, plays a critical role in intermodal container logistics. Using dynamic programming, we identify optimal operational policies for allocating drayage volumes among capacitated carriers under uncertain container flows and spot rates. The computational complexity of optimization under uncertainty is mitigated through sample average approximation. These optimal policies serve as the basis for evaluating specific capacity arrangements. To optimize capacity reservations with strategic and spot carriers, we employ an efficient quasi-Newton method. Numerical experiments demonstrate significant cost-efficiency improvements, including a 21.2% cost reduction in a four-period scenario. Monte Carlo simulations further highlight the strong generalization capabilities of the proposed joint optimization method across out-of-sample scenarios. These findings underscore the importance of integrating strategic and operational decisions to enhance cost efficiency in truckload procurement under uncertainty.

Problem

• Optimize drayage procurement under uncertain container flows and spot rates
• Integrate strategic and operational decisions for cost-efficient truckload procurement
• Develop optimal policies for carrier allocation and capacity reservations

Innovation

• Dynamic programming optimizes drayage volume allocation
• Sample average approximation reduces computational complexity
• Quasi-Newton method enhances capacity reservation efficiency