85 research outputs found
Una comparación de algoritmos basados en trayectoria granular para el problema de localización y ruteo con flota heterogénea (LRPH)
Indexación: Scopus.We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high quality solutions within short CPU times, improving the results obtained by the other proposed approaches.https://revistas.unal.edu.co/index.php/dyna/article/view/55533/5896
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problems
TSP is a classical NP-hard combinatorial optimization problem with many
practical variants. LKH is one of the state-of-the-art local search algorithms
for the TSP. LKH-3 is a powerful extension of LKH that can solve many TSP
variants. Both LKH and LKH-3 associate a candidate set to each city to improve
the efficiency, and have two different methods, -measure and POPMUSIC,
to decide the candidate sets. In this work, we first propose a Variable
Strategy Reinforced LKH (VSR-LKH) algorithm, which incorporates three
reinforcement learning methods (Q-learning, Sarsa, Monte Carlo) with LKH, for
the TSP. We further propose a new algorithm called VSR-LKH-3 that combines the
variable strategy reinforcement learning method with LKH-3 for typical TSP
variants, including the TSP with time windows (TSPTW) and Colored TSP (CTSP).
The proposed algorithms replace the inflexible traversal operations in LKH and
LKH-3 and let the algorithms learn to make a choice at each search step by
reinforcement learning. Both LKH and LKH-3, with either -measure or
POPMUSIC, can be significantly improved by our methods. Extensive experiments
on 236 widely-used TSP benchmarks with up to 85,900 cities demonstrate the
excellent performance of VSR-LKH. VSR-LKH-3 also significantly outperforms the
state-of-the-art heuristics for TSPTW and CTSP.Comment: arXiv admin note: text overlap with arXiv:2107.0687
Deep Policy Dynamic Programming for Vehicle Routing Problems
Routing problems are a class of combinatorial problems with many practical
applications. Recently, end-to-end deep learning methods have been proposed to
learn approximate solution heuristics for such problems. In contrast, classical
dynamic programming (DP) algorithms guarantee optimal solutions, but scale
badly with the problem size. We propose Deep Policy Dynamic Programming (DPDP),
which aims to combine the strengths of learned neural heuristics with those of
DP algorithms. DPDP prioritizes and restricts the DP state space using a policy
derived from a deep neural network, which is trained to predict edges from
example solutions. We evaluate our framework on the travelling salesman problem
(TSP), the vehicle routing problem (VRP) and TSP with time windows (TSPTW) and
show that the neural policy improves the performance of (restricted) DP
algorithms, making them competitive to strong alternatives such as LKH, while
also outperforming most other 'neural approaches' for solving TSPs, VRPs and
TSPTWs with 100 nodes.Comment: 21 page
A Constraint Programming Approach for the Team Orienteering Problem with Time Windows
The team orienteering problem with time windows (TOPTW) is a NP-hard combinatorial optimization problem. It has many real-world applications, for example, routing technicians and disaster relief routing. In the TOPTW, a set of locations is given. For each, the profit, service time and time window are known. A fleet of homogenous vehicles are available for visiting locations and collecting their associated profits. Each vehicle is constrained by a maximum tour duration. The problem is to plan a set of vehicle routes that begin and end at a depot, visit each location no more than once by incorporating time window constraints. The objective is to maximize the profit collected. In this study we discuss how to use constraint programming (CP) to formulate and solve TOPTW by applying interval variables, global constraints and domain filtering algorithms. We propose a CP model and two branching strategies for the TOPTW. The approach finds 119 of the best-known solutions for 304 TOPTW benchmark instances from the literature. Moreover, the proposed method finds one new best-known solution for TOPTW benchmark instances and proves the optimality of the best-known solutions for two additional instances
Une heuristique de recherche à voisinage variable pour le problème du voyageur de commerce avec fenêtres de temps
Nous adaptons une heuristique de recherche à voisinage variable pour traiter le problème du voyageur de commerce avec fenêtres de temps (TSPTW) lorsque l'objectif est la minimisation du temps d'arrivée au dépôt de destination. Nous utilisons des méthodes efficientes pour la vérification de la réalisabilité et de la rentabilité d'un mouvement. Nous explorons les voisinages dans des ordres permettant de réduire l'espace de recherche. La méthode résultante est compétitive avec l'état de l'art. Nous améliorons les meilleures solutions connues pour deux classes d'instances et nous fournissons les résultats de plusieurs instances du TSPTW pour la première fois.We adapt a general variable neighborhood search heuristic to solve the traveling salesman problem with time windows (TSPTW) where the objective is to minimize the completion time. We use efficient methods to check the feasibility and the profitability of a movement. We use a specific order to reduce the search space while exploring the neighborhoods. The resulting method is competitive with the state-of-the-art. We improve the best known solutions for two classes of instances and provide the results of multiple instances of TSPTW for the first time
- …