In general, you all know the problem iof finding the assignment of resources to execute tasks in a given time horizon aiming to opti- mize a goal, such as minimizing costs, turnaround time (makespan) or maximizing throughput. It lead to the personnel allocation problem, also known as employee timetabling or staff scheduling. The problem consists of finding work timetables for an organization staff so that it can satisfy the demand for goods or ser- vices. Different types of objectives and constraints can be considered, including employees satisfaction, regulations and costs. The complexity of the problem depends on the constraints to be met; however, in most real world applications, problems are NP-complex.
For example in the scheduling problem, work shifts and days-off are assigned to all staff accordingly to satisfy all the demands of work to be done. In the typical scheduling problem, the schedule planning period is usually one month, and the work shifts of each day are three rotations (day, evening, and night shifts) or two rotations (AM and PM shifts). A lot of laws and regulations on the basic scheduling restrictions in schedules exist, for example, the least required amount of the manpower at each shift and the satisfaction of the rights to take rest and days-off. To ensure making the schedule feasibly satisfy all the constraints, the scheduling problem is hard to be solved.
Our approach is to applying the artificial intelligence to solve this problem and provide a cost effective schedule.