Resource scheduling and optimization in dynamic and complex transportation settings

Abstract

Resource optimization has always been a challenge both in traditional fields, such as logistics, and particularly so in most emerging systems in the sharing economy. These systems are by definition founded on the sharing of resources among users, which naturally creates many coordination needs as well as challenges to ensure enough resource supply to cover customer demand. This thesis addresses these challenges in the application of vehicle sharing systems, as well as in the context of multi-operation companies that provide a wide range of services to their users. More specifically, the first part of this thesis focuses on models and algorithms for the optimization of bike sharing systems. Shortage of bikes and docks is a common issue in bike sharing systems, and, to tackle this problem, operators use a fleet of vehicles to redistribute bikes across the network.

We study multiple aspects of these operations, and develop models that can capture all user trips that are performed successfully in the system, as well as algorithms that generate complete redistribution plans for the operators to maximize the served demand, in running times that are fast enough to allow real-time information to be taken into account. Furthermore, we propose an approach for the estimation of the actual user demand which takes into account both the lost demand (users that left the system due to lack of bikes or docks) and shifted demand (users that had to walk to nearby stations to find available resources). More accurate demand representations can then be used to inform better decisions for the daily operations, as well as the long-term planning of the system. The second part of this thesis is focused on schedule generation for resources of large companies that must support a complex set of operations.

Different operation types come with a variety of constraints and requirements that need to be taken into account. Moreover, specialized employees with a variety of skills and experience levels are required, along with an heterogeneous fleet of vehicles with various properties (e.g., refrigerator vehicles). We introduce the Complex Event Scheduling Problem (CESP), which captures known problems such as pickup-and-delivery and technician scheduling as special cases. We then develop a unified optimization framework for CESP, which relies on a combination of metaheuristics (ALNS) and Linear Programming. Our experiments show that our framework scales to large problem instances, and may help companies and organizations improve operation efficiency (e.g., reduce fleet size).