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14 results for “Traveling Salesman Problem”
Data of the INFORMS Journal on Computing paper: Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses
<p>In what follows, you will find data of the paper:<br> "Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses" published in INFORMS Journal on Computing</p> <p>List of files:<br> - Computational_results_BB_NN_RW_CPLEX.xlsx: Excel file that gives all results<br> - instance_gen.cc: Instance generator<br> - instances.zip: compressed file of all instances that are sorted by Sections. It additionally includes the generator<br> - Makefile: Makefile for compiling/debugging, i.e., "make all" or "make debug" do the jobs<br> - MersenneTwister.h: needed by schedule_finder.cc<br> - results_Section_5_1.zip: compressed file of all output files of Section 5.1<br> - results_Section_5_2.zip: compressed file of all output files of Section 5.2<br> - results_Section_5_3.zip: compressed file of all output files of Section 5.3<br> - schedule_finder.cc: Main program containing the B&B, the S-shape, and Nearest Neighbor procedure (see details for customizing the parameters at the top of this file)<br> - valgrind_debug.txt: Only contains the used debug command</p> <p>instances/instance_gen.cc generates a problem instance in file problems.txt<br> The structure of the these problem files is the following:<br> /*<br> NE Total number of experiments given by the currently considered file<br> -2 Separator<br> EXPGRP Index of the current experiment group the current experiment belong to<br> N Number of vacant positions in the warehouse<br> M Number of requests to be stored by the tour<br> P Number of pickers to be scheduled in the warehouse<br> A Number of vertical aisles<br> B Number of horizontal (cross) aisles<br> L_A Length of each vertical aisle<br> L_B Length of each cross aisle<br> UF_VA Up-factor of each vertical aisle (A values)<br> DF_VA Down- factor of each vertical aisle (A values)<br> UF_CA Up-factor of each cross aisle (B values)<br> DF_CA Down- factor of each cross aisle (B values)<br> x_pos_vertical_aisle x-position of vertical aisle (A values)<br> y_pos_cross_aisle y-position of cross aisle (B values)<br> warehouse_graph values For each node of the warehouse graph all entries (15 each) are given (total_number_of_warehouse_graph_nodes*15)<br> FS << warehouse_graph[curr_node].free_position << " " << endl;<br> FS << warehouse_graph[curr_node].depot_node << " " << endl;<br> FS << warehouse_graph[curr_node].vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].succ_cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].succ_vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_cross_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].succ_cross_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].pred_vertical_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].succ_vertical_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].region << " " << endl;<br> FS << warehouse_graph[curr_node].x_position << " " << endl;<br> FS << warehouse_graph[curr_node].y_position << " " << endl;<br> shortest_path_distance For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the distance<br> shortest_path_length_including_start_and_end For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the number of visited nodes<br> shortest_path_visited_nodes For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the detailed path (length is respectively given by shortest_path_length_including_start_and_end)<br> dd_free_position For each free position and the depot (here with index N) the due date is transferred (N+1 values) (only relevant for the extended problem, is ignored here)<br> weight_of_free_position For each free position and the depot (here with index N) the weight is transferred (N+1 values) (only relevant for the extended problem, is ignored here)<br> capacity_of_free_position For each free position the storage capacity transferred (N values)<br> -2 Separator indicating the end of an instances<br> -3 Separator indicating the end of all experiments (i.e., indicating the end of the file)<br> */</p> <p>output files (results_Section_5_1.zip/results_Section_5_2.zip/results_Section_5_3.zip):<br> results_BB_NXXX_MYYY_A10_B05: Output file of applying B&B<br> results_RW_NXXX_MYYY_A10_B05: Output file of applying s-shape random walk<br> results_NN_NXXX_MYYY_A10_B05: Output file of applying nearest neighbor</p> <p>In these files you find all outputs of schedule_finder.cc. <br> Among others, you will find the generated tour schedules (for Experiment with index I) in the output files by searching the phrase: "Experiment I completed with result="<br> or for the next Experiment " completed with result="</p> <p>Example (results_BB_N030_M150_A10_B05.txt, experiment 0, the tardiness values are to be ignored, see comments in schedule_finder.cc)<br> Pos 0 depot node with index 80 Number of stored items 0 CT 0 No tardiness<br> Pos 1 position 27 Number of stored items 4 Current accumulated number of stored items 4 CT 163 DD 5629 No additional tardiness<br> Pos 2 position 28 Number of stored items 5 Current accumulated number of stored items 9 CT 399 DD 2962 No additional tardiness<br> Pos 3 position 29 Number of stored items 4 Current accumulated number of stored items 13 CT 670 DD 12631 No additional tardiness<br> Pos 4 position 26 Number of stored items 5 Current accumulated number of stored items 18 CT 840 DD 10142 No additional tardiness<br> Pos 5 position 23 Number of stored items 7 Current accumulated number of stored items 25 CT 1134 DD 6000 No additional tardiness<br> Pos 6 position 22 Number of stored items 4 Current accumulated number of stored items 29 CT 1170 DD 6396 No additional tardiness<br> Pos 7 position 16 Number of stored items 5 Current accumulated number of stored items 34 CT 1448 DD 8962 No additional tardiness<br> Pos 8 position 11 Number of stored items 10 Current accumulated number of stored items 44 CT 1674 DD 6336 No additional tardiness<br> Pos 9 position 0 Number of stored items 10 Current accumulated number of stored items 54 CT 2062 DD 1141 Additional tardiness 921<br> Pos 10 position 2 Number of stored items 9 Current accumulated number of stored items 63 CT 2201 DD 7742 No additional tardiness<br> Pos 11 position 4 Number of stored items 5 Current accumulated number of stored items 68 CT 2407 DD 4846 No additional tardiness<br> Pos 12 position 3 Number of stored items 5 Current accumulated number of stored items 73 CT 2717 DD 2316 Additional tardiness 401<br> Pos 13 position 1 Number of stored items 8 Current accumulated number of stored items 81 CT 2876 DD 9917 No additional tardiness<br> Pos 14 position 6 Number of stored items 3 Current accumulated number of stored items 84 CT 3073 DD 7299 No additional tardiness<br> Pos 15 position 5 Number of stored items 8 Current accumulated number of stored items 92 CT 3144 DD 6152 No additional tardiness<br> Pos 16 position 8 Number of stored items 6 Current accumulated number of stored items 98 CT 3337 DD 3705 No additional tardiness<br> Pos 17 position 12 Number of stored items 9 Current accumulated number of stored items 107 CT 3452 DD 7622 No additional tardiness<br> Pos 18 position 13 Number of stored items 4 Current accumulated number of stored items 111 CT 3522 DD 7833 No additional tardiness<br> Pos 19 position 14 Number of stored items 5 Current accumulated number of stored items 116 CT 3647 DD 9877 No additional tardiness<br> Pos 20 position 19 Number of stored items 1 Current accumulated number of stored items 117 CT 3922 DD 2905 Additional tardiness 1017<br> Pos 21 position 20 Number of stored items 5 Current accumulated number of stored items 122 CT 3923 DD 2538 Additional tardiness 1385<br> Pos 22 position 21 Number of stored items 7 Current accumulated number of stored items 129 CT 3976 DD 2769 Additional tardiness 1207<br> Pos 23 position 18 Number of stored items 10 Current accumulated number of stored items 139 CT 4173 DD 3552 Additional tardiness 621<br> Pos 24 position 25 Number of stored items 4 Current accumulated number of stored items 143 CT 4482 DD 11710 No additional tardiness<br> Pos 25 position 24 Number of stored items 7 Current accumulated number of stored items 150 CT 4509 DD 10599 No additional tardiness<br> Pos 26 visiting the node with index 80 Number of stored items 0 CT 4710 DD 10893 No additional tardiness<br> opt_makespan=4710 opt_total_tardiness=5552<br> TSP_procedure returned value 4710<br> Experiment 0 completed with result=3<br> BFS Branch&Bound report: Consumed time: 1</p> <p>Copied from schedule_finder.cc:<br> Note that the procedure used as a solution procedure in the paper is int TSP_procedure(struct bb_node *curr_bb_node, int version)</p> <p>It is called by BB_procedure() as a subroutine for computing a lower bound value of an extended problem<br> (for instance, this extended problem additionally covers due dates. Therefore, due dates are also part of the problem instances, but can be ignored)<br> Specifically, TSP_procedure(struct bb_node *curr_bb_node, int version) is called once by lb_computation()</p>
Instances for the multiple Traveling Salesman Problem with Drone Stations (mTSP-DS)
<p>This dataset contains instances for the mTSP-DS. The files contain the locations of the depot, the customers, and the drone stations in the euclidean space.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 7. Execution plot, for instance Eil51 (left figure) and KroB100 (right figure)
<p>The evolution of the best solution found by the proposed algorithm is plotted in Figure 7 during a typical execution when solving instance Eil51 and KroB100. In this figure, the horizontal and vertical axes show the number of iterations and gained values of the proposed algorithm respectively. Besides, there is a fast convergence toward the BKS at the beginning of the execution while in the rest of the search the evolution of the BKS is not that fast. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 5. The process of MICALK for solving the TSP
<p>Moreover, in order to prevent the ICA from getting trapped in stagnation, we used a local searching algorithm when the algorithm attained a better solution compared to previous iterations. In fact, the probability of finding better solutions near a good solution is relatively high. There exist many algorithms for the local search and they have of course their pros and cons. Since LinKernighan algorithm is simple and it is one of the most successful methods for generating optimal or near optimal solutions for the TSP, we have used it in this study. The main steps of MICALK are summarized in the pseudo-code given in Figure 5. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 6. Some best routes found by the proposed algorithm
<p>Figure 6 shows some of the best solutions searched by the proposed method. In this figure, the horizontal axis represents the x-axis with increasing positive values to the right and the vertical axis represents the y-axis with increasing positive values upward. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 3. Flowchart of the ICA
<p>At last, the most powerful empire will take the possession of other empires and will win the competition. In other words, imperialistic competition hopefully converges to a state in which there exists only one empire and its colonies are in the same position and have the same cost as the imperialist. Figure 3 shows the flowchart of the basic ICA.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 1. The Initial Empires
<p>The ICA is a novel global search strategy which uses imperialism and imperialistic competition process as a source of inspiration. This algorithm is based on the fact that in a real world, countries try to extend their power over other countries in order to use their resources and bolster their own government. The first step in ICA is to generate an initial population like other evolutionary algorithms. The population set includes a number of feasible solutions called a ‘country’, which corresponds to the term ‘chromosome’ in the GA method. These countries are of two types: colonies and imperialists that altogether form some empires. As it is shown in Figure 1 (Atashpaz Gargari & Lucas, 2007), bigger and stronger empires have more colonies than smaller and weaker ones.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 2. Eliminate the weakest colony of the weakest empire
<p>After initial empires are formed, their colonies start moving toward their relevant imperialist country. This movement is a simple model of assimilation policy which was pursued by some of the imperialist states. If one of the colonies possesses more power than its relevant imperialist after this movement, they will exchange their positions. To begin the competition between empires, the total objective function of each empire should be calculated. It depends on the objective function of both an imperialist and its colonies. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take the possession of their colonies - Figure 2 (Atashpaz Gargari & Lucas, 2007). The empire, which has lost all its colonies, will collapse. </p>
Instances for the Traveling Salesman Drone Station Location Problem (TSDSLP)
<p>This dataset contains some TSDSLP instances. More precisely, each instance specifies the location of the depot, the drone stations, and the customers as coordiantes in the Euclidean space.</p>
Results of Bet-and-Run Strategies with Different Decision Makers on the Traveling Salesman Problem and the Minimum Vertex Cover Problem
<p><em><strong>Results of Bet-and-Run Strategies with Different Decision Makers on the Traveling Salesman Problem and the Minimum Vertex Cover Problem</strong></em></p> <p><strong>1. Introduction</strong></p> <p>In this repository, we provide the implementation and results of an improved generic Bet-and-Run strategy for black-box optimization.<br> The goal our new Bet-and-Run method is to obtain the best possible results within a given time budget <em>T</em> using a given black-box optimization algorithm.<br> If no prior knowledge about problem features and algorithm behavior is available, the question about how to use the time budget most efficiently arises. We propose to first start <em>n>=1</em> independent runs of the algorithm during an initialization budget <em>T1<T</em>, pausing these runs, then apply a decision maker <em>D</em> to choose <em>1<=m<n</em> runs from them (consuming <em>T2>=0</em> time units in doing so), and then continuing these runs for the remaining <em>T3=T-T1-T2</em> time units.</p> <p>In previous bet-and-run strategies, the decision maker <em>currentBest</em> would simply select the run with the best-so-far results at negligible time.<br> We propose using more advanced methods and test several different approaches, including neural networks trained or polynomials fitted on the current trace of the algorithm to predict which run may yield the best results if granted the remaining budget.<br> Applying this implementation to run "virtual experiments," one can find that this approach can yield better results than the previous methods, but also find that the `currentBest` method is a very reliable and robust baseline approach.</p> <p>Here you can find the results of such experiments on the Traveling Salesman Problem and the Minimum Vertex Cover Problem. Both betAndRun_tsp.tar.xz and betAndRun_vertex_cover.tar.xz are extracted in the scale of 30 GiB of size.</p> <p><strong>2. Copyright</strong></p> <p>The source code in this repository is under MIT License and is published in the most recent version at http://github.com/thomasWeise/betAndRun, while the results are under the Creative Commons Attribution 4.0 License.</p> <p>The code on bet-and-run (mainly under cn.edu.hfuu.iao.betAndRun) is jointly developed by Dr. Thomas Weise (http://iao.hfuu.edu.cn), tweise@hfuu.edu.cn, tweise@gmx.de) and Dr. Markus Wagner (http://cs.adelaide.edu.au/~markus/, markus.wagner@adelaide.edu.au).</p> <p>The <em>jpack</em> (http://github.com/marmakoide/jpack) code for Artificial Neural Networks, Linear Algebra, and Evolution Strategies (e.g., CMA-ES) has originally been developed by Dr. Alexandre Devert (http://www.marmakoide.org, marmakoide@hotmail.fr, and http://github.com/marmakoide), who kindly granted us the permission to include it in our repository. The code published here is a slightly modified version of his code, but the copyright and authorship remains entirely with Dr. Devert, who provides it under the MIT license at GitHub under http://github.com/marmakoide/jpack. Please contact Dr. Devert for any questions, in particular regarding licensing and (re-)distribution.</p>
Dataset and code for the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm
<p>Dataset and code for the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm</p>
Instances for the Hampered Travelling Salesman Problem with Neighbourhoods
<p>This repository contains the instances used for studying the Hampered Travelling Salesman Problem with Neighborhoods (TSPN). The instances are saved in .csv format. For each folder, a README.txt is included to load the instances to include to the model.</p>
Problem Instances used for the Drone-Assisted Traveling Salesman Problem with Robot Stations
<p>This dataset contains the test instances used in our Article " Drone-Assisted Traveling Salesman Problem with Robot Stations" written for the 53rd Hawaii International Conference on System Science (HICSS 53).</p> <p>The instances are to be interpreted as a list of points in the Euclidean plane.</p>
The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material
<p>These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. </p> <p><strong>Code</strong></p> <ul> <li><strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP.</li> <li><strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. </li> <li><strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.</li> <li><strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.</li> <li><strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions.</li> <li><strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong></li> <li><strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) </li> <li><strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy.</li> <li><strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model.</li> <li><strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>.</li> <li><strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022</li> </ul>
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