Cercia investigated algorithms that can prioritise task lists and allocate individual tasks to vehicles and personnel. For efficiency, the solutions should distribute new work items on an on-demand basis, taking into account all the relevant constraints. This process needs to be extremely rapid, with the interval between independent task completions potentially measured in seconds. Individual staff should receive a new task instantaneously, when requested. The work distribution should be adaptive, able to cope with warehouse configuration changes such as incorrectly-recorded (or damaged) stock; new personnel arriving for work, or existing personnel leaving; vehicles breaking down; job prioritisation changes; new incoming stock; new purchase orders; updates to the warehouse state.

Cercia developed more than a dozen scheduling algorithms, and evaluated the most useful using a variety of simulated warehouses and task lists. For most warehouse scenarios, our intelligent scheduling methods were able to outperform more naive assumptions (such as 'nearest task first') by up to ten percent, in terms of task completion rate. They also resulted in a reduction in the rate of tasks completed late, after their allotted deadline. In some scenarios, for example, rapid small order picking, an advanced scheduling system was unable to compete with much simpler, human-like methods.

Cercia also investigated the feasibility and ease of database communication via Open Database Connectivity (ODBC) between the intelligent scheduling code and the client's Progress database.

The study showed that an ODBC interface should be able to handle the data transfer requirements using a relatively naïve approach. A more sophisticated approach that only communicates changes in the task list is expected to be much faster.

Potential future projects include: the implementation of one or more of the scheduling algorithms within the client's products; The collection of data on task completion time in order to improve the time estimates used in scheduling; Data mining of task completion information to provide management information.