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32 results for “Warehouse”
The price of safety: Order picking in warehouses with in-house traffic regulations (Supplementary material)
<p>In what follows, you will find the code and results of the paper:</p> <p>"The price of safety: Order picking in warehouses with in-house traffic regulations" published in IISE Transactions.</p> <p> </p> <p>List of files:</p> <p>- Zip file: "Order Picking Problem with in-house traffic regulations" containing C# Code used to generate solutions for all safety policies</p> <p>- Result.csv containing all generated results</p> <p>- createPlots.py containing code to generate figures and tables from the paper</p> <p> </p> <p>The C# code is object-oriented and contains a Main function in the Program.cs file that converts the Example.OPP file with the InstanceReaders to an OPPInstance and uses the Solve function from either the DynamicProgrammic.cs or RuralPostman.cs file to solve the OPPInstance with all the TrafficRegulations as described in the paper.</p> <p> </p> <p>The Example.OPP defines the Depot location (0: decentral, 1: central), AisleLength, i.e. the number of pick positions within each aisle, and other dimensions of the warehouse. Finally, the items are defined by their picking aisle, shelf, position in the shelf, and region.</p> <p> </p> <p>The dynamic program (DP) described in the paper is implemented in DynamicProgrammic.cs. A HashSet of DPNode represents each layer of the DP. A DPNode basically consists of components, nodeDegrees, and a value. Depending on the TrafficRegulation the nodeDegrees are either NodeDegreeClassic, i.e. Null, Uneven, or Even, or NodeDegreeInAndOutDifference, i.e. the difference of the in- and out-degree. To construct the solution at the end, the inEdge is also saved for each DPNode and the additional member depotIsConnected ensures that the depot is visited. The DPNodes in the next layer of the DP are created by the functions MakeNextLayerVertical and MakeNextLayerHorizontal by determining all possibleTransitions per node in the current layer and combining them into a newNode. Products are stored with their position on the shelves in the item list within a PickingAisle. All vertical possibleTransitions are determined in a preprocessing step depending on the TrafficRegulations and are saved within the respective PickingAisle. All horizontal possibleTransitions are determined during the DP with specific functions depending on the TrafficRegulation in HorizontalTransition.cs. When the layers are created, the best feasible DPNode per layer is saved and the best one, i.e. the one with the lowest value, is returned at the end.</p> <p> </p> <p>The paper describes that certain safety policies cannot be solved with the DP. These OPPInstances are solved as a RuralPostman problem (RPP) by generating a Graph that adopts the rectangular structure of the warehouse. Within the Graph, requiredEdges are determined that correspond to PickingAisles containing items. The resulting RPP can be transformed into a traveling salesman problem (TSP) as described by applying an arc-oriented Dijkstra or, in certain cases, to a generalized TSP (GTSP) where one of the two directed edges must be visited. If necessary, the GTSP is transformed to an asymmetric TSP in GTSPInstance and then solved with TSPSolver using LKH-3.exe (Helsgaun 2017, http://webhotel4.ruc.dk/~keld/research/LKH-3/). To use LKH-3.exe, the TSP instance is saved in a TSPLIB format and a parameter file (.par) for LKH and a solution file (.sol) are created in the bin folder. These files are named according to the name specified in the instance.Solve function, where one can also choose to save or delete these files afterward.</p> <p> </p> <p>For more information on LKH-3 see: Keld Helsgaun: An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems (Technical Report, Roskilde University, 2017)</p> <p> </p> <p>Evaluation.py</p> <p>A Python script that generates figures 8, 9, and 10 and tables 6, 7 and 8 (in csv-format) of the paper by processing data from Results.csv.</p> <p>It requires Results.csv to be in the same directory as the code.</p> <p>It also requires the following Python packages:</p> <p>- matplotlib</p> <p>- pandas</p> <p>- seaborn</p>
MSLibrarian: Spectral Warehouse DBs for some common species
<p>Spectral Warehouse DBs for some common species to be used with the R software package MSLibrarian. More information on how to use the Spectral Warehouse DBs, and how to use MSLibrarian, can be found at: https://github.com/MarcIsak/MSLibrarian</p>
Energy consumption data from an office building, waterpark and warehouse in Slovenia and energy production data from a PV Plan (900KW)
<p>The first dataset included 9-month hourly energy data from a waterpark, a warehouse and high-rise office buildings. The second dataset includes 10-year hourly energy production data from a PV plant (900KW). Both datasets refer to Ljubljana, Slovenia. </p>
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>
Dataset: The Chefs' Warehouse, Inc. (CHEF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Sportsman's Warehouse Holdings, Inc. (SPWH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France
<p><strong>Objective</strong></p> <p>The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020, to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models.</p> <p><strong>Results </strong></p> <p>During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health records (EHRs) from the hospital data warehouse improved median relative error at 7 and 14 days by 10.9% and 19.8%, respectively. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection.</p> <p><strong>Discussion </strong></p> <p>Forecast models showed overall good performance both at 7 and 14 days which was improved by the addition of the data from Bordeaux Hospital data warehouse.</p> <p><strong>Conclusions </strong></p> <p>The development of hospital data warehouses might help to get more specific and faster information than traditional surveillance systems, which in turn will help to improve epidemic forecasting at a larger and finer scale.</p>
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18
The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France
Open the record for dataset details and reuse information.
Food Chain pilot raw data - Farm, Transportation, Warehouse
<p>Daily GDD (Growing Degree-day) data collected by a weather station involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Handover, temperature, presence data collected in a transportation vehicle (farm to warehouse, warehouse to supermarket) involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Temperature, humidity data collected in a warehouse involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p>
Remcom Wireless InSite - Warehouse models and simulation configurations
<p>This dataset is provided in scope of the <a href="http://safelog-project.eu/">SafeLog</a> project. It comprises warehouse models and simulation configurations for Remcom Wireless InSite suite used in evaluating UWB signal propagation in warehouse environment.</p>
Food Chain pilot raw data - Farm, Transportation, Warehouse
<p>Daily GDD (Growing Degree-day) data collected by a weather station involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Handover, temperature, presence data collected in a transportation vehicle (farm to warehouse, warehouse to supermarket) involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Temperature, humidity data collected in a warehouse involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p>
PCH17 Warehouse Foundation
Dennison's Warehouse, Pocklington Canal Head. This is a small slot excavated to determine the depth of the foundation. Turns out there are 12 courses of brick below the ground level. Model prepared by Paul Durdin on behalf of Jon Kenny and the Canal and Rivers Trust. Source: Objaverse 1.0 / Sketchfab
Terminal Warehouse tower
This tower sits on the roof of the old [Terminal Warehouse Co](https://explore.baltimoreheritage.org/items/show/493) building on 211 E. Pleasant Street in Baltimore. While it was once one of the city's oldest warehouses in continuous use by the same corporation, it is currently a self-storage facility. Created using 575 images taken with a DJI Mavic Mini and processed in Metashape. Source: Objaverse 1.0 / Sketchfab
COREQ checklist: Focus group for 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in medical Data Warehouses'
<p>Presentation of the 32 items on the consolidated criteria for reporting qualitative research (COREQ) checklist. The information is used for the report on a focus group that was conducted as part of the preparation of a publication. The title of the article is (as of submission on 18.03.2024): 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in Medical Data Warehouses'.</p>
Butler Brothers Warehouse
The Butler Brothers warehouse (now Butler Square) is a massive warehouse in Minneapolis, Minnesota. It was constructed from 1906 and 1908 in the Chicago style of architecture. It was converted to offices in the 1970's after being added to the NRHP in 1971. Its strength was immense with floors withstanding upwards of 300 lbs/sqft. This model was created for the game Cities Skylines and can be accessed from the Steam Workshop here: https://steamcommunity.com/sharedfiles/filedetails/?id=2182776227 Source: Objaverse 1.0 / Sketchfab
The warehouse part of Ogrodzieniec Castle
Inner part of the warehouse. The castle was partially erected on rocks, as can be seen in the model. Visible holes are remains from a wooden structure on the wall. It was a multi-level supply warehouse for the inhabitants and the castle crew. Source: Objaverse 1.0 / Sketchfab
PCH17 Dennison's Warehouse cleaned
This is the same warehouse as shown in the previous model, but the interior of the structure has been cleared and cleaned and two post-holes investigated. There are some issues with the texture in this model, primarily with the grass 'leaking' into the trench base. Model prepared by Paul Durdin on behalf of Jon Kenny and the Canal and Rivers Trust. The project was funded in part by the Heritage Lottery Fund. Source: Objaverse 1.0 / Sketchfab
6D-ViCuT: Six degree-of-freedom visual cuboid tracking dataset for manual packing of cargo in warehouses
Open the record for dataset details and reuse information.
Medieval Warehouse
A medieval warehouse, with barrels, crates and pulley Source: Objaverse 1.0 / Sketchfab
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.