/shipments-match-cli

Primary LanguageTypeScript

Shipments Match CLI

About This

This is a coding exercise requested by Platform Science and submitted by Albert Sanchez.

Problem

I have to provide a CLI tool capable of pairing drivers and destinations. The catch is that we can only route one shipment to one driver per day.

The mathematical model for determining which drivers are best suited to deliver each shipment is the following:

  • If the length of the shipment's destination street name is even, the base suitability score is the number of vowels in the driver’s name multiplied by 1.5
  • If the length of the shipment's destination street name is odd, the base suitability score is the number of consonants in the driver’s name multiplied by 1
  • If the length of the shipment's destination street name shares any common factors (besides 1) with the length of the driver’s name, the base suitability score is increased by 50%

Example

If provided a driver file with Daniel Davidson on one line and an address file with 44 Fake Dr., San Diego, CA 92122 on one line, the pairing’s suitability score between those two would be 9.

Solution

I will try to solve 44 Fake Dr., San Diego, CA 92122 in a few different ways, because the example does not specify how much of the destination string we are actually using, or if special characters are being considered. Here are my possible inputs:

Input Destination
A 44 Fake Dr., San Diego, CA 92122
B 44 Fake Dr.
C 44 Fake Dr, San Diego, CA 92122
D 44 Fake Dr

First I will check how many vowels and consonants the driver names has:

Driver Length Vowels (EVEN) Consonants (ODD)
Daniel Davidson 15 aieaio DnlDvdsn
Total 6 8

Next we calculate common factors (besides 1) for each input:

Input Destination Length Driver Length Common Factors Multiply By
A 32 15 1
B 11 15 1
C 31 15 1
D 10 15 5 1.5 (50%)

Next we perform the calculations for each input in the same order as the model explains:

Input Destination Length EVEN or ODD BSS Multiply (EVEN or ODD) First Result Multiply (Common Factor) Final Result
A 44 Fake Dr., San Diego, CA 92122 32 EVEN 6 1.5 9 1 9
B 44 Fake Dr. 11 ODD 8 1 8 1 8
C 44 Fake Dr, San Diego, CA 92122 31 ODD 8 1 8 1 8
D 44 Fake Dr` 10 EVEN 6 1.5 9 1.5 13.5

After calculating all the results we come to the conclusion that Input A is the way to go.

Ignored Posible Assumptions

There are some assumptions that I might be ignoring (but I will ask about them on interview).

  • Is the EVEN/ODD BSS considered base before or after multiplying against vowels/constants length
  • Which brings me to... Is the third bullet applied to BSS before multiplying the EVEN/ODD condition?

Requirements

Installation

# Make a copy or repo on your system
$ git clone https://github.com/AlbertSanIza/shipments-match-cli.git
$ cd shipments-match-cli

# Install dependencies
$ yarn install

# Build project
$ yarn build

# Create a symlink for "shipments-match-cli"
$ npm link

Usage

# Run from anywhere in the system (thanks to npm link)
$ shipments-match-cli <destinations_file_path> <drivers_file_path>

# Add "-w" or "--write_file" flag to generate a result.csv
$ shipments-match-cli <destinations_file_path> <drivers_file_path> --write_file

# Add "-v2" or "--version_two" flag to use the v2 solution
$ shipments-match-cli <destinations_file_path> <drivers_file_path> --version_two

Develop

# Run latest source code (before "yarn build")
$ yarn ts-node src/main.ts <destinations_file_path> <drivers_file_path>

# Hot reload (using nodemon and the example_files)
$ yarn start

Open Source Tools

Some the open source tools I used: