Job description
Key takeaways
The PhD research will be performed as part of an industry funded project on optimization of last-mile delivery operations. The goal of this project is to conceive, develop, and evaluate optimization models and algorithms for increasing the economic efficiency of last-mile delivery operations, including routing of delivery vehicles and picking of customer orders in warehouses. The algorithms need to calculate intelligent routing and picking decisions in real time while being exposed to a dynamic environment with uncertainty about upcoming customer orders and other resources. A particular focus will therefore be on models and algorithms that are able to take into account this uncertainty about the future. Designing such models and algorithms requires working at the intersection of machine learning and mathematical optimization. Evaluating such algorithms requires close interaction with our industrial project partner from the last-mile delivery sector. Concretely, the PhD candidate will perform research on understanding how self-learning optimization algorithms can facilitate efficient last-mile delivery operations.
The challenge
In recent years, online shopping has for many consumers become the standard way of purchasing products. Yet, the number of online orders continues to increase strongly as more and more consumers switch to online shopping of fast-moving consumer goods (FMCG), such as packaged foods, beverages, and non-durable household goods. As a consequence of this trend, FMCG delivery services face the challenge of having to process large numbers of orders at a high pace to guarantee fast delivery.
The key to meeting this challenge is to maximize the efficiency of last-mile delivery operations. To this end, delivery services need methods that are able to derive optimal decisions about vehicle routing and order picking in the course of each business day. Developing such methods is challenging, as optimal decision making requires taking into account the uncertainty about upcoming customer orders of the remaining day.
This PhD position focuses on designing, developing, and evaluating self-learning optimization algorithms that are able to cope with this challenge. By leveraging real-time data, developed algorithms continuously adapt to customer behavior and respond to changing market signals with economically efficient routing and picking decisions.
Within this project, you will have the opportunity to work not only with colleagues at the HBE department of the University of Twente, but also with researchers from our industrial partner in the last-mile delivery sector, including the opportunity for regular visits to our partner’s optimization department.
Your profile
We look for a highly motivated researcher who is driven by curiosity and has/is:
- Master’s degree or equivalent in Computer Science, Operations Research, Mathematics, Industrial Engineering, or related discipline;
- Affinity and/or experience with computer programming, linear programming, and other optimization and machine learning techniques;
- A good team spirit and feel at home at the intersection of academia and industry;
- Willing to work both at the University of Twente (Enschede) and at our nearby industry partner from the last-mile delivery sector;
- Able to do independent research within an ongoing research project;
- Willing to develop skills in coding, writing, and publishing;
- Exhibit a strong passion and possess outstanding skills in algorithmic design;
- Possess good communication skills and an excellent command of English.
Our offer
We encourage high responsibility and independence, while collaborating with colleagues, researchers, other university staff and industry partners. We follow the terms of employment by the Dutch Collective Labour Agreement for Universities (CAO). Our offer contains: a fulltime 4-year PhD position with a qualifier in the first year; excellent mentorship in a stimulating research environment with excellent facilities; and a personal development program within the Twente Graduate School. It also includes:
- Gross monthly salary of € 2.901 in the first year, increasing each year up to € 3.707 in the fourth year;
- Excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
- 29 holidays per year in case of full-time employment;
- A training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
- A green campus with free access to sports facilities and an international scientific community;
- A family-friendly institution that offers parental leave (both paid and unpaid);
- A full status as an employee at the UT, including pension, health care benefits and good secondary conditions are part of our collective labour agreement CAO-NU for Dutch universities.
Information and application
Are you interested after reading about this vacancy? Applications for this position will be reviewed on a rolling basis. The vacancy will remain open until we find and onboard the right candidate. Early applications are encouraged as positions may be filled before the vacancy's official closure. You can apply for this position before May 31th, 2025, by clicking the ‘apply now’ button below. Please include:
- A cover letter (maximum 2 pages A4), emphasizing your specific interest, qualifications, and motivations to apply for this position;
- A Curriculum Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications and references;
- An IELTS-test, Internet TOEFL test (TOEFL-iBT), or a Cambridge CAE-C (CPE). Applicants with a non-Dutch qualification and who have not had secondary and tertiary education in English can only be admitted with an IELTS-test showing a total band score of at least 6.5, internet. TOEFL test (TOEFL-iBT) showing a score of at least 90, or a Cambridge CAE-C (CPE).
Additional information can be acquired via email from Stephan Meisel ([email protected]). Please do not send applications to this email address.
About the department
The HBE cluster is dedicated to encouraging a supportive and inclusive working culture. Our aim is that all job applicants are given equal opportunities. When we select candidates for employment, it will be on the basis of their competence and ability. To support the workforce diversity, we are open to offer flexible working conditions on an individual basis to support work-life balance, that may include contract of employment, working hours and location, or child care arrangements.
The High Business Entrepreneurship corporate video can be watched via this link
About the organisation
The Faculty of Behavioral, Management and Social sciences (BMS) aims to play a key role in understanding, jointly developing and evaluating innovations in society. Technological developments are the engine of innovation. As a technical university that puts people first, we tailor them to human needs and behavior. We also ensure adequate governance at public and private level, and robust, inclusive and fair organizational structures. We do this by developing, sharing and applying high-quality knowledge in Psychology, Business Administration, Public Administration, Communication Sciences, Philosophy, Educational Sciences and Health Sciences. Our research and education in these disciplines revolves around tackling and solving societal challenges. The research programs of BMS are closely linked to the research of the UT institutes Mesa+ Institute for Nanotechnology, TechMed Center and Digital Society Institute.
As an employer, the Faculty of BMS offers work that matters. We equip you to create new possibilities for yourself and for our society. With us, you will become part of a leading technical university with increasing, positive social impact. We offer an open, inclusive and entrepreneurial atmosphere, in which we encourage you to make healthy choices, for example through our flexible, adaptable benefits.
