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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].