Short Description

We are looking for a Ph.D. candidate with a background in Industrial Engineering, (Applied) Mathematics, Operations Research, Econometrics, Artificial Intelligence, or Operations Management, interested in rigorous research with practical relevance in the area of sustainable and smart logistics.

Job Description

Project

Today's transportation systems, facilitating the movement of goods in our supply chains, are facing several challenges in the form of environmental concerns but also through changes in policy, infrastructure and shifts in consumer demands. Transportation systems and the related logistics processes need to be more flexible to adjust to this dynamic environment, while, at the same time, improving the associated environmental footprint to achieve the goal of zero emission transport. A way to realize this, is by transforming current transportation networks towards smart systems in which actors make more informed and coordinated decisions while sharing tasks and resources.

This transformation, however, requires collaboration of different actors in the chain to encourage resource sharing and reach good solutions to complex decision problems that benefit all parties involved. The aim of this project is to investigate these different collaboration scenarios between actors in the chain. This includes both horizontal as well as vertical collaboration in routing settings and the sharing of available storage space at transport hubs and intermediary depots. To facilitate such collaborative settings, it is important to introduce financial allocation rules that divide costs and benefits in a fair way.

To identify fair allocation and align incentives between actors, you will make use of cooperative game theory. In this theory, an important question is how to fairly allocate an amount of money amongst a collaborating group of players (e.g., actors in a transport network). A well-known, and fair, solution concept for answering this question is the core. The core is the set of all allocations that divides the money in such a way that no individual player or group of players is worse off.

In the project, you will formulate several transportation-oriented cooperative games and introduce several associated allocation rules. Thereafter, you will investigate whether the allocations of these allocation rules do belong to the core (i.e., investigate whether the allocations are fair). Checking whether an allocation belongs to the core is difficult, as it requires solving a transportation problem per subgroup of players -and the number of subgroups of players is exponential in the number of players. To deal with this computational complexity, you make use of data driven algorithms and machine learning (ML) techniques that can help to discover underlying patterns within our problem environments and identify good solutions within limited computational time.

You, as a successful applicant, will perform the Ph.D. project outlined above. The research will be concluded with a Ph.D. thesis. You will be supervised by dr. ir. Loe Schlicher and dr. Sonja Rohmer. A small teaching load is part of the job.

Academic and Research environment

Group

You will be part of the Operations Planning, Accounting & Control group (OPAC). OPAC currently consists of 25 staff members, 10 postdocs and 30 PhD students. The faculty teaches and conducts research in the area of operations planning and control in manufacturing, maintenance services, logistics and supply chains. Research is generally quantitative in nature, while many of the researchers also engage in empirical research. The OPAC group is responsible within the university for all teaching in the areas of operations management, transportation, manufacturing operations, reliability and maintenance, and accounting and finance, both at undergraduate and graduate level. The OPAC group has close collaborations with the industry, which gives direct access to challenging operations management problems, new technologies, and empirical data.

• A Master's degree in Operations Research, Econometrics, Industrial Engineering, Operations Management, Applied Mathematics, or a related field.
• Strong analytical and mathematical skills and demonstrated competence for quantitative modelling.
• The ability to work on a challenging topic that has both fundamental and applied research aspects.
• Proven excellent verbal and written communication skills, as is your proficiency in English and your ability to collaborate in an international setting.
• Experience with game theory, AI and/or machine learning is an advantage for this position

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.