Route Optimization Over Scheduled Services For Large-Scale Package Delivery Networks

TL;DR

This paper presents a novel column-generation heuristic for large-scale trailer routing in scheduled package delivery networks, significantly improving solution quality and computational efficiency for tactical and real-time planning.

Contribution

It introduces the TPOSSP model, a network reduction technique, and a stabilized column-generation approach to efficiently solve large-scale, time-dependent routing problems with practical industrial applications.

Findings

Solutions achieve 3.7%-5.7% optimality gap in hours

Real-time solutions within 3% of optimality in under 1 minute

Network reduction decreases run times by 85%

Abstract

This paper introduces the Trailer Path Optimization with Schedule Services Problem (TPOSSP) and proposes a column-generation heuristic (CG-heuristic) to find high-quality solutions to large-scale instances. The TPOSSP aims at determining trailer routes over a time-dependent network using existing scheduled services, while considering tractor capacity constraints and time windows for trailer pickups and deliveries. The objective is to minimize both the number of schedules used and the total miles traveled. To address the large scale of industrial instances, the paper proposes a network reduction technique that identifies the set of feasible schedule-legs for each requests. Moreover, to address the resulting MIP models, that still contains hundred of millions variables, the paper proposes a stabilized column-generation, whose pricing problem is a time-dependent shortest path. The approach is evaluated on industrial instances both for tactical planning where requests for the entire network are re-optimized and for real-time operations where new requests are inserted. In the tactical planning setting, the column-generation heuristic returns solutions with a 3.7%-5.7% optimality gap (based on a MIP relaxation) in under 1.7-10.3 hours, and improves the current practice by 2.3-3.2%, with translates into savings of tens of millions of dollars a year. In the real-time setting, the column-generation heuristic returns solution within 3% of optimality in under 1 minute, which makes it adequate for real-time deployment. The results also show that the network reduction decreases run times by 85% for the column-generation heuristic.