Set-up

This tutorial will guide you through an example in Rembrandt.

Introduction

In this tutorial, you will use Rembrandt to model the scenario of a last mile delivery service and optimize the assignment of parcels to drivers. For this purpose, pre-filled databases are provided, and you will be guided step by step through the preparation and usage of the scenario in Rembrandt.

The resource types in this use case are Microdepots, Receivers, Deliverers, and Tours. Receivers represent single parcels, including information on destination address, size, and so on. These parcels have to be delivered from a Microdepot to the actual recipient by a deliverer. For this, receivers have information on their so-called wish-timeframes stored, i.e., information in which time window the shipment is to be delivered by the deliverer. The resource allocation and optimization goal in this scenario is assigning all parcels to deliverers and tours so that these are as efficient as possible.

Getting Started

For following this tutorial, you can use a local installation by following the steps below.

1. We will use Docker for deploying Rembrandt, but extend the provided set-up by the pre-filled database, so make sure you have Docker installed and running.

2. Now, please create a docker-compose.yml file with the following content:

version: '3'
services:
    rembrandt:
        image: bptlab/rembrandt:latest
        ports:
            - 3000:3000
        environment:
            - MONGO_HOST=mongo
        volumes:
            - /var/run/docker.sock:/var/run/docker.sock
            - data-exchange:/usr/src/rembrandt/dataExchange
    smile-data:
        image: bptlab/rembrandt-smile-data:recipe
        links:
            - mongo
    mongo:
        image: mongo:latest
volumes:
    data-exchange:

This docker-compose file has the same content as the regular deployment file but contains an additional service smile-data. This service runs at start up and adds scenario-specific data into Rembrandt's database. The tag recipe determines the degree of prefilling, where as recipe already contains the most possible data. See the DockerHub page of this image to check out the other tags and their contents.

3. To make the exemplary optimization algorithms known to your system, please run the following commands:

$ docker pull bptlab/rembrandt-smile-opal:munkres
$ docker pull bptlab/rembrandt-smile-opal:rule

These commands will download two docker images containing optimization algorithms for the last mile delivery use case. They contain the logic for the actual resource allocation. See the DockerHub page for more details.

4. Now, you can start Rembrandt (see step 2), using either Docker Compose or Docker Stack, and go to the webpage of your local Rembrandt instance: http://localhost:3000.

5. Proceed to the next page, Resource Management.