In late 2015 three of the co-authors of this paper published the first review on time-dependent routing problems. Since then, there have been several important algorithmic developments in the field. These include travel time prediction methods, real-time re-optimization by operating directly on the road graph, efficient exploration of solution neighborhoods, dynamic discretization discovery and Machine Learning-inspired methods. The aim of this survey is to present such research lines, together with indications on their further developments.
Integrating incentive-driven delivery options and time-dependent routing decisions with availability profiles
As e-commerce continues to evolve, the issue of first delivery failures due to customers not being at home has become increasingly prominent, significantly increasing the operational costs of logistics companies. This paper considers an incentive-driven (ID) strategy, whereby logistics companies …
Dynamic demand-responsive transit scheduling with time-dependent travel times: A joint supply and demand management approach
Demand-responsive transit (DRT) is a flexible public transportation mode offering affordable door-to-door services. However, its widespread adoption still faces large hurdles such as demand variability, immediacy, and financial sustainability. Most DRT studies focus on fleet management, often leading to underutilization …
Sustainable risk mitigation in hazardous material transportation
The transportation of hazardous material involves the movement of freight representing a high risk to health, safety, and the environment. Due to its nature, hazardous material transportation is regulated by strict laws and must be treated separately from classical transportation. …
A branch-price-and-cut algorithm for the time-dependent multiple truck–drone routing problem
Time-varying traffic conditions are crucial features of urban logistics. Overlooking these conditions will pose a high coordination risk for drone-assisted routing problems. In this paper, a time-dependent multiple truck–drone routing problem (TD-MTDRP), which captures the time-varying traffic conditions as time-dependent …
Time-Dependent Routing and Road Network Precision
In many industries, data changed the way decisions are made, driving performance and competitiveness. Transportation and logistics are no exception. Companies are analyzing data from their vehicles to improve driving behavior, optimize transportation routes, and improve their operations. Over the …
A three-phase algorithm for the three-dimensional loading vehicle routing problem with split pickups and time windows
In a survey of Belgian logistics service providers, the efficiency of first-mile pickup operations was identified as a key area for improvement, given the increasing number of returns in e-commerce, which has a significant impact on traffic congestion, carbon emissions, …
A new branch-and-Benders-cut algorithm for the time-dependent vehicle routing problem
Daily traffic congestion poses significant challenges for companies operating in urban areas. By considering predicted travel times throughout the day, route planning systems can improve delivery schedules, thereby reducing costs associated with delays and congestion. Although the time-dependent vehicle routing …
Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case Study
Semi-structured decisions, which fall between highly structured and unstructured decision types, rely on human intuition and experience for the final choice, while using data and analytical models to generate tentative solutions. These processes are traditionally iterative and time-consuming, requiring cycles …
Vehicle routing with time-dependent travel times: Theory, practice, and benchmarks
We develop theoretical foundations and practical algorithms for vehicle routing with time-dependent travel times. We also provide new benchmark instances and experimental results. First, we study basic operations on piecewise linear arrival time functions. In particular, we devise a faster …