I am an EngD student in my last year of research in the department of computer science at the University of York. My research interests include machine learning, heuristic methods and optimisation problems.
My current research considers an optimised scheduling strategy to support a large-scale asset maintenance system.
If you're interested in my work please get in touch:
I also try to build learning based hyperheuristic algorithms that can easily translate across different optimisation problem domains.
Chen, Y., Cowling, P., & Remde, S. (2014). Dynamic Period Routing for a Complex Real-World System : A Case Study in Storm Drain Maintenance. In Evolutionary Computation in Combinatorial Optimisation.
Chen, Y., Polack, F., Cowling, P., Mourdjis, P., Remde, S. (2016). Risk driven analysis of maintenance for a large-scale drainage system. In International Conference on Operations Research and Enterprise Systems.
Chen, Y., Cowling, P., Remde, S., Polack, F. (2016). Efficient large-scale road inspection routing. In International Conference on Operations Research and Enterprise Systems.
Mourdjis, P., Polack, F., Chen, Y., Robinson, M., Cowling, P. (2016). The effect of cooperation in pickup and multiple delivery problems. In International Conference on Operations Research and Enterprise Systems.
Chen, Y., Mourdjis, P., Polack, F., Cowling, P.(2016). Evaluating hyperheuristics and local search operators for period routing problem. The 16th European Conference on Evolutionary Computation in Combinatorial Optimisation.
The first line contains the following information: problem_type m n t
The second line contains the following information for each customers: i x y d q f a list
Each visit combination is coded with the decimal equivalent of the corresponding binary bit string. For example, in a 5-day period, the code 10 which is equivalent to the bit string 01010 means that a customer is visited on days 2 and 4. (Days are numbered from left to right.)
The real-world gully pot maintenance map: Download
Here we provide 6 small maps generated based on real-world gully pot locations. They are all strongly structured as shown below. Then we artifically assign other factors such as visit frequency and service duration to each gully pot due to the confidential problem of the orginal data.
Best found results for the 6 instances: Download
More PVRP problems can be found from Periodic VRP Instances