A Hybrid Search Method for Evolutionary Dynamic Optimization of the 3-dimensional Personnel Assignment Problem and its Case Study Evaluation at The City of Calgary
Abstract
Evolutionary dynamic optimization is receiving more attention as it continues to deliver value in more application areas. In this thesis, an evolutionary dynamic optimization method for solving both static and dynamic personnel assignment optimization problems is proposed. This evolutionary method is based on the idea of genetic algorithms. Starting with the static scenario, I build an evolutionary algorithm that utilizes two variations of an OR-tree-based search method in generating the initial population and offspring. I then use the evolutionary algorithm for the static scenario as a framework into which I integrate two new strategies for tackling dynamism in the problem. In this thesis, I focus on the type of changes that require getting new (i.e. up-to-date) solutions as fast as possible right after the change. This type of changes has many applications in areas such as aircraft landing and take-off scheduling and mobile wireless network routing.
Motivated by a real-world problem (at the City of Calgary), I focus on the changes in the availability of personnel in personnel assignment problems. I have two proposed solution approaches to the dynamism which utilize OR-tree-based search features and epigenetics mechanisms (both inside the evolutionary algorithm). Three additional strategies for tackling other types of changes are also presented. In this research, multidimensionality and dynamism are for the first time brought together into the personnel assignment problem. Using both real-world data (for the case study) and synthetic data (for additional evaluation) in the experiments, the experimental results prove the usefulness and responsiveness of my proposed solution approaches. Moreover, the feedback from the domain experts and higher level management in the case study show a strong preference for using my system and its outputs over the current practice.
The case study took place in the Waste and Recycling Services business unit at the City of Calgary in Alberta, Canada. The synthetic data was generated based on the real-world data.
Description
Keywords
Artificial Intelligence, Computer Science, Engineering--Operations Research
Citation
Niknafs, A. (2016). A Hybrid Search Method for Evolutionary Dynamic Optimization of the 3-dimensional Personnel Assignment Problem and its Case Study Evaluation at The City of Calgary (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24763