Large-scale Routing Problems Research Articles

GLOP: Learning Global Partition and Local Construction for Solving Large-Scale Routing Problems in Real-Time

The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP hierarchically partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance on large-scale routing problems, including TSP, ATSP, CVRP, and PCTSP. Our code is available at: https://github.com/henry-yeh/GLOP.

Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework

The overall quality of last-mile delivery in terms of operational costs and customer satisfaction is primarily affected by traditional logistics planning and the consideration and integration of driver knowledge and behavior. However, this integration has yet to be exploited. This phenomenon is mirrored in two largely separated research bodies on logistics planning and driver behavior. Bridging this gap by using and integrating historical data from actually driven tours into last-mile delivery planning is promising for research and practice. Still, it also leads to complex and large-scale routing problems, which require the development of an overall methodology that goes beyond classical optimization approaches as the needed approach requires a multi-stakeholder perspective, calls for a hybrid-analytical approach by incorporating tour prediction and prescription, and requires both data science and optimization methods. Accounting for these challenges, we suggest a hybrid decision support framework for the traveling salesman problem with time windows that combines machine learning techniques and conventional optimization methods and considers the deviation between suggested and predicted tours. We demonstrate the applicability of our framework in a case study that draws on real-world logistics data. Relying on a sensitivity analysis, we investigate and illustrate the trade-off between the level of deviation between predicted and suggested tours and tour costs. Our case study draws general managerial implications and recommendations that guide decision makers in building their decision support systems for last-mile delivery routing by instantiating our generic framework.

Machine Learning–Based Feasibility Checks for Dynamic Time Slot Management

Online grocers typically let customers choose a delivery time slot to receive their goods. To ensure reliable service, the retailer may want to close time slots as capacity fills up. The number of customers that can be served per slot largely depends on the specific order sizes and delivery locations. Conceptually, checking whether it is possible to serve a certain customer in a certain time slot given a set of already accepted customer orders involves solving a vehicle routing problem with time windows. This is challenging in practice as there is little time available and not all relevant information is known in advance. We explore the use of machine learning to support time slot decisions in this context. Our results on realistic instances using a commercial route solver suggest that machine learning can be a promising way to assess the feasibility of customer insertions. On large-scale routing problems it performs better than insertion heuristics. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This research was funded by the Netherlands Organization for Scientific Research under the City Logistics Living Laboratory project [Grant 439.18.424].

Towards Intelligent Road Traffic Management over Weighted Large Graphs Hybrid Meta-heuristic-Based Approach

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

Towards Intelligent Road Traffic Management Over a Weighted Large Graphs Hybrid Meta-Heuristic-Based Approach

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

Efficiently solving very large-scale routing problems

The vehicle routing problem is among the most-studied and practically relevant problems in combinatorial optimization. Yet, almost all research thus far has been targeted at solving problems with not more than a few hundred customers. In this paper, we explore how to design a local search heuristic that computes good solutions for very large-scale instances of the capacitated vehicle routing problem in a reasonable computational time. We investigate different ways to reduce the time complexity with pruning and sequential search, as well as the space complexity by restricting the amount of stored information. The resulting algorithm outperforms a previously introduced heuristic for instances of 10,000 and more customers in relatively short computational time. In order to stimulate more research in this domain, we introduce a new set of benchmark instances that are based on a real-world problem and contain up to 30,000 customers and use our heuristic to solve them.

A Fast and Scalable Re-routing Algorithm based on Shortest Path and Genetic Algorithms J. Lee, J. Yang Jungkyu Lee

This paper presents a fast and scalable re-routing algorithm that adapts to dynamically changing networks. The proposed algorithm, DGA, integrates Dijkstra’s shortest path algorithm with the genetic algorithm. Dijkstra’s algorithm is used to define the predecessor array that facilitates the initialization process of the genetic algorithm. Then the genetic algorithm keeps finding the best routes with appropriate genetic operators under dynamic traffic situations. Experimental results demonstrate that DGA produces routes with less traveling time and computational overhead than pure genetic algorithm-based approaches as well as Dijkstra’s algorithm in largescale routing problems.