1. Designing.

2. Production planning.

3. Scheduling.

4. Controlling.

Design of FMSs involves a selection of equipment and layout design, including:

1. The  number  and capacity  of stations.

2. The number and capacity of the storage     units.

3. The  design of material handling  system.

With the advance of automation technology, the associated decision supporting systems, production planning, scheduling and  control, have gained  importance [13].

Production planning involves establishing production levels for a given length of time. It determines production parameters, such as production mix, production levels, resource availability, and due dates. With the specified production  parameters, the goal of scheduling is to make efficient use of resources to complete tasks in a timely manner. Clossen and Malstrom [14] stated that hundreds of  robots  and  millions  of  dollars worth of computer-controlled equipment are worthless if they are under-utilised or if they spend their time working on the wrong part because of poor planning and scheduling. Control of the system is considered to be part of production planning and scheduling. Shop floor control is  concerned  with monitoring the process and progress of orders in the system and reporting the current status to management. In considering these four stages of  planning  in  FMSs, scheduling  plays  a crucial role.

There have been extensive studies on scheduling manufactur- ing systems. These studies can be pided into three basic approaches [13]:

1. Operations  research  (OR) approach.

2. Artificial intelligence (AI)-based  approach.

3. Combination of OR  and AI-based  approaches.

Spano et al. [15] pided the scheduling research  into  two major approaches:

1. Traditional approach.

2. Artificial intelligence (AI)-based  approach.

The traditional approach can be further pided into two categ- ories:

Theoretical research dealing with optimisation procedures. Experimental research dealing with dispatching   rules.

Scheduling of FMSs has  been  extensively  investigated  over the last three decades  and  it  continues  to  attract  the interest of both the academic and industrial sectors. Ramasesh [16] provided a state-of-the-art survey of simulation-based research on dynamic job shop scheduling a focusing first on simulation modelling and experimental considerations, then on findings about the job shop performance criteria of interest. This excel- lent review covers simulation studies for job shops  from  1960 to 1987.

Theoretical research has focused on the development of mathematical models and optimal or suboptimal algorithms [17–19], using integer, mixed integer, and linear programming [20–22]. The theoretical results have not been widely used in industry because of the associated high computational com- plexity. Mathematical programming models,  which  are based on simplified assumptions for the system under study, are specific to inpidual manufacturing enterprises and processes. These models also require a high degree  of  accuracy  in the data used. Experimental research has been concerned primarily with dispatching rules and heuristic procedures that solve the scheduling problems efficiently. Dispatching rules are used primarily to help the production manager on the shop floor to make decisions. A heuristic procedure is a procedure or set of rules that provides a good solution for a limited class of problems [23,24] This solution may or may not be the optimal solution, but can be derived with less computational effort than in optimisation approaches [15]. In short-term scheduling, as opposed to medium-term scheduling that is implemented through MRP systems, dispatching rules are widely used. For example the first-in-first-out (FIFO) rule selects the part which first entered  the  input/output  buffer  at/from  a  machine,  as the next part to be serviced. Dispatching rules are employed extensively in discrete event simulation models    [25–38].