Yan Tian

Bio: Yan Tian is an academic researcher from Tsinghua University. The author has contributed to research in topics: Equal-cost multi-path routing & Static routing. The author has an hindex of 2, co-authored 2 publications receiving 29 citations.

Dynamic vehicle routing problem using hybrid ant system

Abstract: The dynamic vehicle routing problem, DVRP, consists in optimally routing a fleet occur at random. The optimization consists in finding the solution that minimizes the total length of tours and satisfying the customer delivery time window. In this paper, we present a hybrid ant system to handle the dynamism by means of modifying the pheromone matrix and candidate sets strategy. In addition, we make further improvements on vehicle routes with the local search heuristics. The results show that the hybrid ant system works effectively to find competitive solutions.

Re-optimization in dynamic vehicle routing problem based on Wasp-like agent strategy

Abstract: The dynamic vehicle routing problem, DVRP, is focused on optimally routing a fleet of vehicles of fixed capacity when the real-time requests of customers occur. The optimization is to search for the solution in minimizing the total length of tours and to satisfy the customer delivery time window. In this paper, we present a Wasp-like agent strategy to decide when to deal with the real-time requests and reoptimize the vehicle routes. The results show that the method works effectively to find competitive solutions.

Uncertainty Region Discovery and Model Refinement for Domain Adaptation in Road Detection

Abstract: Road detection is an important task in intelligent transportation systems. In regard to the domain adaptation, although self-training methods generate pseudo-labels for retraining the model, redundancy and noise in pseudo-labels lead to limited improvement. We propose that necessary annotations are required to effectively handle this challenge. First, we introduce the classifier discrepancy to discover and annotate uncertainty regions in the target domain. Then, we also design a recurrent teacher–student module to consider both prior knowledge and correction signals, avoiding the risk of suboptimal solution entrapment. Experiments on public data sets show that our approach is competitive with state-of-the-art approaches.

Metaheuristics: From Design to Implementation

Abstract: A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

Abstract: As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehicle rerouting strategy based on global distance and local pheromone is employed to assign alternative routes to selected vehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to further alleviate traffic congestion levels. We propose and evaluate two traffic light control strategies, depending on whether or not to consider downstream traffic conditions. The unified pheromone-based traffic management framework is compared with seven other approaches in simulation environments. Experimental results show that the proposed framework outperforms other approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution and fuel consumption. Moreover, experiments over various compliance and penetration rates show the robustness of the proposed framework.

DVRP: a hard dynamic combinatorial optimisation problem tackled by an evolutionary hyper-heuristic

Abstract: In this paper we propose and evaluate an evolutionary-based hyper-heuristic approach, called EH-DVRP, for solving hard instances of the dynamic vehicle routing problem. A hyper-heuristic is a high-level algorithm, which generates or chooses a set of low-level heuristics in a common framework, to solve the problem at hand. In our collaborative framework, we have included three different types of low-level heuristics: constructive, perturbative, and noise heuristics. Basically, the hyper-heuristic manages and evolves a sophisticated sequence of combinations of these low-level heuristics, which are sequentially applied in order to construct and improve partial solutions, i.e., partial routes. In presenting some design considerations, we have taken into account the allowance of a proper cooperation and communication among low-level heuristics, and as a result, find the most promising sequence to tackle partial states of the (dynamic) problem. Our approach has been evaluated using the Kilby's benchmarks, which comprise a large number of instances with different topologies and degrees of dynamism, and we have compared it with some well-known methods proposed in the literature. The experimental results have shown that, due to the dynamic nature of the hyper-heuristic, our proposed approach is able to adapt to dynamic scenarios more naturally than low-level heuristics. Furthermore, the hyper-heuristic can obtain high-quality solutions when compared with other (meta) heuristic-based methods. Therefore, the findings of this contribution justify the employment of hyper-heuristic techniques in such changing environments, and we believe that further contributions could be successfully proposed in related dynamic problems.

Annotated Bibliography in Vehicle Routing

Abstract: One of the most significant problems of supply chain management is the distribution of products between locations, most known as the Vehicle Routing Problem (VRP). The vehicle routing problem is one of the most challenging problems in the field of combinatorial optimization. Dantzig and Ramser first introduced the VRP in 1959. They proposed the first mathematical programming formulation. In 1964 Clarke and Wright proposed an effective greedy heuristic that improved Dantzig and Ramser approach. Since then, hundreds of models and algorithms were proposed for the optimal and approximate solution of the different versions of the VRP. In this paper, we present an annotated bibliography of the vehicle routing problem and its variant.