Finite Capacity Scheduling
Finite capacity scheduling is so-called because it takes capacity into account from the very outset. The schedule is based on the capacity available. Infinite capacity scheduling - the approach used in MRP II - schedules using the customers' order due date and then tries to reconcile the result with the capacity available. There is no single accepted way to carry out Finite Capacity Scheduling, and of the various approaches that exist, some are proprietary secrets.
It is however possible to define certain approaches, or types of scheduler:
Electronic scheduling board
The simplest scheduler is the electronic scheduling board, which mimics the old fashioned card-based loading boards, but the system calculates times automatically and will warn of any attempt to load two jobs on the same machine. There is no scheduling algorithm as such involved.
Order Based Scheduling
In Order Based Scheduling the tasks are scheduled on the basis of order priority. The sequence at individual resources is determined by the overall priority of the order for which the parts are destined. It is a distinct improvement on infinite capacity schedulers but its biggest drawback is that it allows gaps to appear on resources. Some schedulers allow the process to be iterated to try and reduce gaps and therefore reduce the time through the system. This iteration can be very time consuming.
Constraint based schedulers, Synchronised Manufacturing
With the Constraint based schedulers, also known as Synchronised Manufacturing, the idea is to locate the bottleneck in the line and ensure that it is always loaded. The assumption is that non-bottlenecks can take everything thrown at them, and this allows them to be synchronised to the bottleneck through the Master Production Schedule (MPS). The MPS is generated by loading the orders onto the bottleneck and thus determining when they will be ready. This system is inclined to produce gaps and is also very sensitive to small changes such as a customer wanting to reschedule an order.
Discrete Event Simulation
In Discrete Event Simulation the simulation loads all resources at a point of time. When all contentions and queues are resolved it moves on to the next set of events. Because the simulation moves from one set of events to the next, there are far fewer gaps in schedules produced this way and they are far more stable. The problems with simulations are that they are: laborious ;and also difficult to incorporate into other systems such as data feedback from the shop floor.
Algorithms, Genetic algorithms
Algorithms usually suffer from being highly mathematical and therefore user unfriendly, however more recently a new approach has emerged under the general title of `genetic algorithms'. These use a 'fitness' criterion. A typical example would be to minimise the total time for jobs to stay in production. The procedure starts with a schedule or family of schedules. The idea is to try and improve them using a selection mechanism akin to natural selection. 'Children' (new schedules) are bred using characteristics (such as sequences of work) from parent schedules. If the new child shows improved fitness i.e. is faster than the parents, it replaces the worst schedule. While the approach looks promising it is still in the early stages.
There remains the question of how these new approaches fit with existing schedulers, particularly MRP in which companies have invested vast sums. In the first three cases they tend to replace the scheduling heart of the MRP system while leaving the rest unchanged. To that extent the MRP system acts like a database manager.
- Harrison. M., "MRP II & Finite Capacity Scheduling - a combination for the 90's", Works Management, December 1991.
- Kirchmier. W., "Finite capacity Scheduling", Proceedings of the 37th International Conference APICS, Falls Road, VA, 1994