Development of a self learning automatic planning proposal Algorithm to support Operations planning – Ewals Cargo Care

 

 Overview

Company Name / Department

Ewals Cargo Care

Contact Person

Jeroen Lamers

Location Tegelen
Optional remote work
Travel expenses (own account or reimbursed by the company)
Housing arranged by company No
Housing expenses (how much per month, own account or subsidized by the company)
Internship compensation  €450 per month
Study program

 OML

ESCF community

EHTC

Start date

September, 2024

 

Company Description

Ewals Cargo Care is a family owned transportation company, which is founded in 1906 by Alfons Ewals. The main office is found in Tegelen near Venlo. Besides this, Ewals has 32 other offices in Europe, which lead to a local presence in 15 European countries with over 2500 employees.

Ewals has grown into a strong international player. Our customers are offered a broad spectrum of logistics products ranging from full- and part loads to Control Tower services. Especially, but certainly not exclusively, we are known  for our European multimodal network that includes more than 4500 Mega Huckepack XL(S) trailers. In addition, we can rely on a solid network of partners. Ewals is also continuously improving work processes and investing in the development of our employees.

 

Project Description

Project description:

Currently the matching of trucks and trailers with to be executed orders is still done manually. We would like to go towards an automatic planning algorithm which can generate a proposal/support for our planners in the Transport management application with their continuous planning puzzle.

First of all it is essential to determine which data is required data for the decision making algorithm. In the past some investigation is done about which parameters and restrictions should be taken into account, but this should be revised.

Given these restrictions and parameters a model should be created that will give the optimal matching solution for a set of incoming equipment and executing jobs. Optimization is determined on several parameters such as empty millage, CO2 emission and obviously costs.

This optimal proposal should be used by the planning department to solve their planning puzzle. The model should also be able to take into account user deviations from the suggested planning, for future suggested plannings. Essential is to enable operations management to understand the consequences of finetuning the model. In addition it is important to visualize why these decisions/proposals are made towards the planner.  

The Project is organized within the ICT solutions department in close cooperation with the business process management and operational department, but focus will be on technological possibilities to build upon, to become ready for the future.

Goals of the project:

Insight in the technological possibilities for a self-learning algorithm for automatic planning. We would like to develop this in such a way that it remains transparent for a planner how the proposal came about.

    Deliverables:

    • A set of restrictions and parameters that should be included in the model.
    • A working algorithm to generate a matching proposal, with self-learning mechanism
    • A report detailing the development process, the challenges faced, the solutions implemented and a proposal how to integrate this within our system architecture.

    Essential student knowledge:

    This project provides an exciting opportunity to investigate in actual practical logistic challenges on yet unexplored territory within Ewals. It will not only equip you with valuable experience in these advanced technologies but also contribute meaningfully to a more sustainable and efficient logistics industry. You are expected to be able to explain the methods and techniques used, and you have the ability to put this into practice at the intersection of IT and business process management.

    • Programming knowledge (preferential Python, SQL)
    • Supply chain knowledge and affinity (Transport and Logistics)

     

       

      More information: escf@tue.nl  

       

      logo PostNL 240x140