Thesis track: Operations Research and Logistics - MSc Biosystems Engineering
At the Operations Research and Logistics groups, we are optimising the material flows, e.g. agricultural products, in supply chain networks. The goal is to get the right product, in the right quantity, and the right quality, at the right time, at the right place as efficient as possible while fulfilling the requirements of the stakeholders (such as on resource availability, quality of service and product, and regulations from the government). This requires optimising the design, planning and control of the supply and distribution network, and the processes that transform raw materials into final products.
We do so by mathematical modelling, simulation, and computer programming to evaluate and/or optimise key performance indicators, like efficiency, profitability, sustainability, or customer satisfaction. Applications can be found at growers, farms, processing companies, warehouses, distribution centers, stores, or at consumer households. Examples of questions that we answer are:
- Where to install processing facilities and distribution centers (DC), such that transportation costs and CO2 emissions are minimised and/or profits are maximised, while accounting for uncertainties such as harvest yield?
- How many crops to grow, to harvest, and to keep in a warehouse/DC/store to meet the uncertain demand by consumers?
- What route or path should a robot/truck/autonomous guided vehicle (AGV) follow to collect/drop off all items?
- How to reduce inefficiencies and reuse losses and waste in agriculture supply chains?
Learn more about the Operations Research and Logistics group.
Courses
Following are some relevant courses associated with this research group:
Decision Science 1
In many applications, decisions are to be evaluated and optimised based on multiple criteria, and often risk and uncertainty is involved. Which solution is ‘best’ may depend on the risk profile of the decision maker (rational, risk-seeking, risk-averse), and the weighting of the different criteria. The course Decision Science 2 deals with multi-criteria decision-making under uncertainty. You will learn about multi-criteria decision-making, discrete event simulation, and risk analysis.
Decision Science 2
In many applications, decisions are to be evaluated and optimised based on multiple criteria, and often risk and uncertainty is involved. Which solution is ‘best’ may depend on the risk profile of the decision maker (rational, risk-seeking, risk-averse), and the weighting of the different criteria. The course Decision Science 2 deals with multi-criteria decision-making under uncertainty. You will learn about multi-criteria decision-making, discrete event simulation, and risk analysis.
Data-Driven Supply Chain Management
Modern supply chains get more and more digitalised, and more and more data is recorded and stored. In the course Data-driven Supply Chain Management, you will learn how to use data in supply chains to support decision-making processes. Thereby you will learn using machine learning toolboxes and (auto-)regression methods to clustering, regression, and classification problems. Examples are related to demand forecasting, prediction of product quality (decay), and clustering to solve large-scale optimisation problems.
Decision Science for Technology
Decision Science for Technology (DST) broadens and builds on the fundamentals of Decision Science 1 with a strong focus on design-oriented, quantitative decision support for its application in technological domains. You learn how to solve large-scale practical problems by creating valid models and genuine solution approaches.
Non-linear Decision Science
Many real-life optimisation problems, in particular in (process) engineering, are non-linear. For instance, product quality may decline non-linear over time. Non-linear models can be more complex to solve, as one risks ending up in a local optimum rather than a globally best solution. In this course you learn to create, implement, and apply solution methods to solve non-linear decision problems.