Optimization of Load-to-Pool Assignments in Shared Trucking Operations with Capacity and Cost Constraints

Abstract

The dynamic landscape of shared trucking necessitates optimization models that enhance load assignment to pools, balancing cost efficiency, capacity utilization, and strategic decision-making. This study introduces a mixed-integer linear programming (MILP) framework tailored for load assignment in shared trucking, with a focus on two key components: pool creation and load assignment. In pool creation, we structure pools to meet specific operational and cost-efficiency goals, incorporating unique constraints on capacity, load compatibility, and cost-effectiveness based on observed demand patterns. The MILP model then utilizes binary decision variables in the load assignment phase, ensuring each load is matched to a pool in a way that minimizes costs while satisfying operational and spatial constraints.To anticipate future impacts of present assignments, the MILP framework employs a forward-looking strategy with a rolling horizon approach, enabling iterative re-optimization as new data becomes available. This adaptability helps to balance immediate cost reduction with long-term operational resilience, accommodating potential variations in load and pool availability over time. Preliminary computational results, based on a case study with real-world demand data from an operating broker will demonstrate the model’s effectiveness in achieving cost-efficient and robust load-to-pool assignments. This approach offers a scalable, sustainable solution for shared trucking operations, supporting both operational and environmental objectives.