scholarly journals Simultaneous Pickup and Delivery Traveling Salesman Problem considering the Express Lockers Using Attention Route Planning Network

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yu Du ◽  
Shaochuan Fu ◽  
Changxiang Lu ◽  
Qiang Zhou ◽  
Chunfang Li
Keyword(s):  
Stochastic Optimization ◽  
Traveling Salesman Problem ◽  
Large Scale ◽  
Optimization Methods ◽  
Traveling Salesman ◽  
Computational Time ◽  
Pickup And Delivery ◽  
Total Cost ◽  
Simultaneous Pickup And Delivery ◽  
Back Order

This paper presents a simultaneous pickup and delivery route designing model, which considers the use of express lockers. Unlike the traditional traveling salesman problem (TSP), this model analyzes the scenario that a courier serves a neighborhood with multiple trips. Considering the locker and vehicle capacity, the total cost is constituted of back order, lost sale, and traveling time. We aim to minimize the total cost when satisfying all requests. A modified deep Q-learning network is designed to get the optimal results from our model, leveraging masked multi-head attention to select the courier paths. Our algorithm outperforms other stochastic optimization methods with better optimal solutions and O(n) computational time in evaluation processes. The experiment has shown that reinforcement learning is a better choice than traditional stochastic optimization methods, consuming less power and time during evaluation processes, which indicates that this approach fits better for large-scale data and broad deployment.

scholarly journals An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem

2017 ◽  
Vol 7 (6) ◽  
pp. 2260-2265 ◽  
Author(s):  
H. Jafarzadeh ◽  
N. Moradinasab ◽  
M. Elyasi
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Nearest Neighbor Search ◽  
Traveling Salesman ◽  
Computational Time ◽  
Test Problems ◽  
Solution Quality ◽  
Generalized Traveling Salesman Problem

The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.

scholarly journals The traveling salesman problem with pickup and delivery: polyhedral results and a branch-and-cut algorithm

2008 ◽  
Vol 121 (2) ◽  
pp. 269-305 ◽  
Author(s):  
Irina Dumitrescu ◽  
Stefan Ropke ◽  
Jean-François Cordeau ◽  
Gilbert Laporte
Keyword(s):  
Traveling Salesman Problem ◽  
Branch And Cut ◽  
Traveling Salesman ◽  
Pickup And Delivery ◽  
The Traveling Salesman Problem

A hybrid heuristic approach for the multi-commodity pickup-and-delivery traveling salesman problem

2016 ◽  
Vol 251 (1) ◽  
pp. 44-52 ◽  
Author(s):  
Hipólito Hernández-Pérez ◽  
Inmaculada Rodríguez-Martín ◽  
Juan-José Salazar-González
Keyword(s):  
Traveling Salesman Problem ◽  
Heuristic Approach ◽  
Traveling Salesman ◽  
Pickup And Delivery ◽  
Hybrid Heuristic

scholarly journals ON SAMPLING BASED METHODS FOR THE DUBINS TRAVELING SALESMAN PROBLEM WITH NEIGHBORHOODS

2015 ◽  
Vol 2 (2) ◽  
pp. 57-61
Author(s):  
Petr Váňa ◽  
Jan Faigl
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Computational Time ◽  
Solution Quality ◽  
Goal Region ◽  
Dubins Vehicle ◽  
Proposed Modification ◽  
Dubins Traveling Salesman Problem

In this paper, we address the problem of path planning to visit a set of regions by Dubins vehicle, which is also known as the Dubins Traveling Salesman Problem Neighborhoods (DTSPN). We propose a modification of the existing sampling-based approach to determine increasing number of samples per goal region and thus improve the solution quality if a more computational time is available. The proposed modification of the sampling-based algorithm has been compared with performance of existing approaches for the DTSPN and results of the quality of the found solutions and the required computational time are presented in the paper.

scholarly journals Simple Constructive, Insertion, and Improvement Heuristics Based on the Girding Polygon for the Euclidean Traveling Salesman Problem

Algorithms ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 5 ◽  
Author(s):  
Víctor Pacheco-Valencia ◽  
José Alberto Hernández ◽  
José María Sigarreta ◽  
Nodari Vakhania
Keyword(s):  
Traveling Salesman Problem ◽  
Real Life ◽  
Traveling Salesman ◽  
Computational Time ◽  
Problem Instance ◽  
Dimensional Euclidean Space ◽  
Benchmark Problem ◽  
Small Constant ◽  
Euclidean Traveling Salesman Problem ◽  
Problem Instances

The Traveling Salesman Problem (TSP) aims at finding the shortest trip for a salesman, who has to visit each of the locations from a given set exactly once, starting and ending at the same location. Here, we consider the Euclidean version of the problem, in which the locations are points in the two-dimensional Euclidean space and the distances are correspondingly Euclidean distances. We propose simple, fast, and easily implementable heuristics that work well, in practice, for large real-life problem instances. The algorithm works on three phases, the constructive, the insertion, and the improvement phases. The first two phases run in time O ( n 2 ) and the number of repetitions in the improvement phase, in practice, is bounded by a small constant. We have tested the practical behavior of our heuristics on the available benchmark problem instances. The approximation provided by our algorithm for the tested benchmark problem instances did not beat best known results. At the same time, comparing the CPU time used by our algorithm with that of the earlier known ones, in about 92% of the cases our algorithm has required less computational time. Our algorithm is also memory efficient: for the largest tested problem instance with 744,710 cities, it has used about 50 MiB, whereas the average memory usage for the remained 217 instances was 1.6 MiB.

The pickup and delivery traveling salesman problem with first-in-first-out loading

2009 ◽  
Vol 36 (6) ◽  
pp. 1800-1808 ◽  
Author(s):  
Güneş Erdoğan ◽  
Jean-François Cordeau ◽  
Gilbert Laporte
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Pickup And Delivery ◽  
First In First Out

scholarly journals Solving Large-Scale TSP Using a Fast Wedging Insertion Partitioning Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zuoyong Xiang ◽  
Zhenyu Chen ◽  
Xingyu Gao ◽  
Xinjun Wang ◽  
Fangchun Di ◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Time Complexity ◽  
Large Scale ◽  
Construction Method ◽  
Traveling Salesman ◽  
Experimental Results ◽  
Insertion Method ◽  
Symmetric Traveling Salesman Problem ◽  
Traditional Construction

A new partitioning method, called Wedging Insertion, is proposed for solving large-scale symmetric Traveling Salesman Problem (TSP). The idea of our proposed algorithm is to cut a TSP tour into four segments by nodes’ coordinate (not by rectangle, such as Strip, FRP, and Karp). Each node is located in one of their segments, which excludes four particular nodes, and each segment does not twist with other segments. After the partitioning process, this algorithm utilizes traditional construction method, that is, the insertion method, for each segment to improve the quality of tour, and then connects the starting node and the ending node of each segment to obtain the complete tour. In order to test the performance of our proposed algorithm, we conduct the experiments on various TSPLIB instances. The experimental results show that our proposed algorithm in this paper is more efficient for solving large-scale TSPs. Specifically, our approach is able to obviously reduce the time complexity for running the algorithm; meanwhile, it will lose only about 10% of the algorithm’s performance.

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