Grant: Engage Grants Program

Scheduling an enterprise's operation (e.g., work shifting, vehicle-routing, and training rotations) is an example of resource allocation, a difficult combinatorial optimization problem to solve. Even for specific cases, such as a staff scheduling problem, finding a polynomial-time algorithm to solve it is equivalent to solving the most important problem in theoretical computer science (whether P = NP). Restricting the context to training scheduling in a certain industry does not make the problem any less difficult. On the other hand, resource allocation problem or scheduling problem is one of most common encountered management issues in every organization/enterprise, and it often lies at the core of efficient management. Our proposal is to investigate the impact of various heuristic schemes in the search for optimal resource allocation and staff scheduling, and to study the effeteness in relation with the basic structures in the sparse scheduling problems. Understanding of the mechanism of heuristic schemes with respect to the sub-structure of the proposed problems will enable us to develop robust algorithms to solve the staff or resource allocation in a timely manner; furthermore, it will enable us to optimize the operation of an enterprise by utilizing the available resources. The research team at TRU intends to join forces with Pelesys Learning Systems Inc., an industry leader in providing training solutions to airlines and airline training organizations, to integrate resources, data management and record systems together to automate its pilot training service. The integration will improve the accuracy and efficiency of training scheduling, and to provide a real-time schedule for the clients.