[1] Shipping Optimization Challenge: Forecasting Delivery Time for Shipments | bitgrit

[1] Shipping Optimization Challenge: Forecasting Deli…

Help a logistics firm choose the best way to ship goods and predict estimated delivery times and sh…

CTRL F Consulting Ltd

103

399

Nov. 17, 2020, 8 a.m. UTC (Ends in 20 days)

Description
This is a 3-part data challenge made possible by a client of CTRL F who is a London-based international logistics firm. Each part of the challenge is designed to help this client optimize their cross-border deliveries and ensure affordable, secure, and reliable shipping. *NOTE: The prize money displayed is the total prize for all three Shipping Optimization Challenges. Please see the Rules section for more info* Using data from this logistics client with a daily shipment volume ranging from 5,000 to 10,000, you can identify a variety of factors to most effectively choose the ideal freight vendor, reduce shipment costs and duration, and ultimately find the best possible way to deliver goods. The goal of this competition is to create an algorithm to help this logistics firm choose the best way to ship goods and accurately predict estimated delivery times and shipment quantities.
Prizes
1

1st Prize

The prize money displayed is the total prize for all three Shipping Optimization Challenges. Please see the Rules section for more info!
$4,000
2

2nd Prize

The prize money displayed is the total prize for all three Shipping Optimization Challenges. Please see the Rules section for more info!
$2,500
3

3rd Prize

The prize money displayed is the total prize for all three Shipping Optimization Challenges. Please see the Rules section for more info!
$1,000
Timeline
  • Oct. 6, 2020 Competition Starts
  • Nov. 17, 2020 Competition Ends
  • Dec. 1, 2020 Winners Announced (Subject to change based on submission results)
Guidelines
Purpose In this part of the competition, we will forecast the time (indicated as Shipping Time in the dataset) required to successfully process each shipment. The target variable to be predicted is the Shipping Time in units of days with a maximum of 5 decimals (ex. "5.3" days or "5.45673" days are acceptable in the solutions file). The train_2_pr.csv file contains historical shipments with known Shipping Times and can be used to train the model. Shipping Times should be forecasted for each Shipment ID in test_2.csv. Input Dataset • train_2_pr.csv - File that contains historical shipments over the 16-month period between 2019-02-14 and 2020-06-13, shipping time, and other shipment details. # shipment_id - Unique Shipment ID # send_timestamp - Date when the order was sent to destination country # pick_up_point - Pick Up Point abbreviation # drop_off_point - Drop Off Point abbreviation # source_country - Country from where the goods need to be shipped # destination_country - Country to where the goods need to be shipped # freight_cost - Cost of transportation/kg # gross_weight - Gross weight in kg which needs to be shipped # shipment_charges - Fixed cost per shipment # shipment_mode - Method of shipment (e.g. air, ocean) # shipping_company - Candidate shipping company # selected - Whether the company in 'shipping_company' was selected or not # shipping_time - The amount of time that it takes for goods to reach their destination • shipping_companies_details_1.csv - File that contains all necessary information about shipping companies. # source_country - Country from where the goods need to be shipped # destination_country - Country to where the goods need to be shipped # shipment_mode - Method of shipment (e.g. air, ocean) # cut_off_time - The time window in which goods can be picked up (e.g. "10 - 2 and 3 - 6 BST" means that pick up is available between the times of 10 a.m. and 2 p.m., and again between the ties of 3 p.m. and 6 p.m). All times are in Bangladesh Standard Time. # tat - Turnaround Time is the time it takes from a package order to shipment. T refers to the time a package was sent, and CO means cut-off time, or the time after which a package can no longer be shipped. "Before CO – T+0" and "After CO – T+1" means that if the goods are sent to the shipping company before the cut off time, then the package will be sent on the same day. If the package arrives to the shipping company after the cut off time, then it will be shipped out on the following day. # processing_days - Days on which Pick up Point works. # pick_up_point - Pick Up Point abbreviation # drop_off_point - Drop Off Point abbreviation # min_sc - Minimum cost of shipment # max_sc - Maximum cost of shipment # shipping_company - Name of shipping company # shipment_charges - Fixed cost per shipment • test_2.csv - File that contains historical shipment data. Shipping Time is unknown and should be predicted. # shipment_id - Unique Shipment ID # send_timestamp - Date when the order was sent to destination country # pick_up_point - Pick Up Point abbreviation # drop_off_point - Drop Off Point abbreviation # source_country - Country from which the goods need to be shipped # destination_country - Country to where the goods need to be shipped # freight_cost - Cost of transportation/kg # gross_weight - Gross weight in kg which needs to be shipped # shipment_charges - Fixed cost per shipment # shipment_mode - Method of shipment (e.g. air, ocean) # shipping_company - Candidate shipping company # selected - Whether the company in 'shipping_company' was selected or not Submission File • submission_2.csv - File which will be submitted by the participants. The file SHOULD NOT CONTAIN ANY ID OR FIELD TITLES; the order of the target variables should be according to the submission file format but the first row and first column should be removed prior to submission. The platform will only accept csv files that consist only of numeric values. # shipment_id - Unique ID of shipment (should be removed before the submission) # shipping_time - Estimated time of delivery (in hours) or how long it will take to deliver the goods (target variable). This column is empty and should be filled out by the participants.
FAQs
Who do I contact if I need help regarding a competition?
For any inquiries, please contact us at info@bitgrit.net.
How will I know if I’ve won?
If you are one of the top three winners for this competition, we will email you with the final result and information about how to claim your reward.
How can I report a bug?
Please shoot us an email at info@bitgrit.net with details and a description of the bug you are facing, and if possible, please attach a screenshot of the bug itself.
If I win, how can I receive my reward?
Prizes can be delivered via PayPal, wire transfer, or another suitable method. We understand that everyone prefers different payment methods, and we will endeavour to accommodate your needs as best as possible depending on your location and our ability to do so.
Rules
1. This competition is governed by the following Terms of Participation. Participants must agree to and comply with these terms to participate. 2. This competition consists of 3 problem statements. The winner will be determined by the total score calculated as: 20% of problem statement 1 + 40% of problem statement 2 + 40% of problem statement 3. 3. The maximum limit of submissions per day is 3. If users want to submit new files, they will have to wait until the following day to do so. Please keep this in mind when uploading a submission.csv file. 4. A competition prize will be awarded after we have received, successfully executed, and confirmed the validity of both the code and the solution. Once winners are announced and our team reaches out to them, the winners must provide the following by November 25, 2020 in order to avoid disqualification: a. All source files required to preprocess the data b. All source files required to build, train and make predictions with the model using the processed data c. A requirements.txt (or equivalent) file indicating all the required libraries and their versions as needed d. A ReadMe file containing the following: • Clear and unambiguous instructions on how to reproduce the predictions from start to finish including data pre-processing, feature extraction, model training and prediction generation • Environment details regarding how the model was developed and trained, including OS, memory (RAM), disk space, CPU/GPU used, and any required environment configurations required to execute the code • Clear answers of the following questions: - Which data files are being used? - How are these files processed? - What is the algorithm used and what are its main hyperparameters? - Any other comments considered relevant to understanding and using the model In the event these items are not provided or do not meet the minimum requirements listed above, we will not be able to award the winner with their respective prize. 5. If two or more participants have the same score on the leaderboard, the participant who submitted the winning file first will be considered the winner. 6. The dataset used for this competition is derived from real-world data that has been anonymized, so please do not use any models developed utilizing this data on similar matching services. 7. If you have any inquiries about this competition, please don’t hesitate to reach out to us at info@bitgrit.net. We ask that users do not contact CTRL F directly.
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