An integrated approach to manufacturing planning : optimisation in process planning and job shop scheduling.
Within manufacturing, increasing interest in being placed in the possibilities of integrated
process planning and scheduling. Separating these two related tasks can impose
constraints, on the final schedule, which are both undesirable and unnecessary. These
constraints arise from premature decisions regarding the allocation of manufacturing
resources. By making use of flexible process plans, these decisions can be delayed until
the most appropriate time: during scheduling. The decisions can then be made on the basis
of objectives common to both tasks (such as the minimisation of manufacturing cost).
This thesis outlines an approach to manufacturing planning which is based on a highly
general formulation of the problem. This integrated process planning/scheduling problem
can be viewed as a generalisation of process plan optimisation, a task which is also
considered in detail. A novel approach to plan optimisation is proposed, which in turn
forms the basis for integrated planning and scheduling.
Some research into integrated planning/scheduling has been reported in the literature.
However, researchers differ in the way they formulate the integrated task. This thesis
therefore attempts to outline a general framework for the characterisation of integrated
process planning and scheduling problems. This considers both the degree and
representation of process plan flexibility, and also the level of detail at which the shop
floor is modelled. This framework forms a basis for a comparison of solution approaches.
Published solution approaches are mostly based on the use of dispatching rules, but
attempts have been made to use optimal search. The use of dispatching rules is essentially
an ad hoc approach and, although relatively easy to apply in practice, produces solutions
of mediocre quality. However, new research using simulated annealing suggests that
neighbourhood search may offer a valuable alternative. This observation is supported by
ambitious research published on the use of genetic algorithms. Because of the extreme
combinatorial complexity of the combined task, optimal search methods are unlikely to be
usable in practice. Furthermore, such methods exhibit a severe lack of generality because
they make highly specific assumptions about problem formulation. Neighbourhood search
techniques have inherent properties which give them a much higher level of generality.
Although it is not an optimal search method, simulated annealing has been shown to
provide solutions of significantly higher quality than those achieved by dispatching rule
techniques. Also, and unlike optimal search techniques, it appears able to handle the
immense complexity of the integrated planning/scheduling task.
For the above reasons, it is argued that neighbourhood search techniques, such as
simulated annealing, provide the best compromise available between solution quality and