Application of further development of the Intelligent Tender Selection Algorithm and reinforcement Learning for Optimizing Tender Management – Ewals Cargo Care

 Overview

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 approximately 2050 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 4000 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:

The project’s aim is to improve the process of validating client enquiries (process referred to as tendering) – especially in peak seasons it’s critical to select the right initiatives to exploit maximum value and dedicate business resources to the right opportunities. Therefore the project resides in the commercial domain (i.e., organizational stream Business & Product Development). In specifically the project is organized within the Product Intelligence department – in which multiple TU/e alumni reside, whom focus on research and development and technological possibilities to enact upon, to become ready for the future.

In collaboration with the European Supply Chain Form, Ewals Cargo Care and the ESCF have composed the “UNIVERS” Roadmap, which is journey in joint effort, supporting and enabling Ewals with the effective transition to a data driven business model. In an earlier phase of the roadmap an Intelligent Tender Selection Algorithm 1.0 has been created – in this context you are required to further develop a comprehensive V2.0 for Ewals Cargo Care that encompasses all the critical aspects to select the right client enquiries.

Reinforcement learning (RL), a type of machine learning where an agent learns to make decisions by interacting with its environment, can potentially be used to optimize the tender selection process. It operates on the concept of reward optimization – an agent, through trial and error, learns to make decisions that maximize a cumulative reward. In this scenario, the ‘reward’ can be defined based on multiple parameters including strategic aim of Ewals Cargo Care, competitiveness, maximized profitability, and more.

Goals of the project:

Your primary task for this project will involve further developing a reinforcement learning model – improving the one that already exists – to optimize the tender selection and validation procedure. The RL model should: (1) leverage the historical data and strengthen the current algorithm with new value added parameters (e.g., macro level variables) (2) reflect strategic development direction of Ewals Cargo Care to propose decisions about which tenders to proceed with.

Deliverables:

• A comprehensive RL prediction model (i.e., Intelligent Tender Selection 2.0).
• A report detailing the development process, the challenges faced, the solutions implemented, and the final results. This report should include an analysis of the effectiveness of the proposed system in optimizing the tender selection process.

Essential student knowledge:

This project provides an exciting opportunity to delve into the intersection of tender management and reinforcement learning for optimizing logistics operations. 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.

As a master student, you are expected to demonstrate a clear understanding of the techniques employed, and your ability to creatively and effectively apply these techniques to real-world challenges. You are encouraged to take a proactive approach in problem-solving, consider alternative methods where necessary, and communicate your findings clearly and concisely.

• Programming knowledge (preferential Python)
• Reinforcement Learning Modelling
• Supply chain knowledge and affinity (Transport and Logistics)

More information: escf@tue.nl