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Manufacturing scheduling is one of the most researched areas since its optimality plays an
important role in the operation of the shop floor. Manufacturing has a vital contribution in the overall
economy of a country as it generates and attracts commercial activities. The whole framework of
business has changed in view of the fluctuating global customer demands and fierce opposition from
technologically advanced competitors. There is always a pressure on the manufacturer to produce the
designed products in the shortest possible time to capture the market. To the challenge of changing
product requirements and market demands, flexible manufacturing system is the answer.
Flexible job shop is employed to produce a medium variety of products in a medium volume
category. In contrast to the conventional job shop, it offers flexibility in performing operations on
different machines; hence providing space for the manufacturing planner / scheduler for arranging
parts as per corporate requirements. When seen in the context of optimal operation, this setting while
offering such great advantage, also poses the scheduler with the decision regarding assignment of
operations to available machines in addition to sequencing of operations. In this way, the complexity
of the problem grows exponentially even in the small settings of the shop.
The flexible job shop scheduling is a NP-hard combinatorial optimization problem with
regards to complexity and its exact solution requires many lifetimes to reach. Consequently,
techniques built around the concepts of artificial intelligence have been popularly used to solve the
problem. Genetic Algorithm (GA) is one of the most attempted and widespread technique from this
domain. GA can produce good results of the scheduling problems, however when stuck in the local
minima, the algorithm normally fails to escape, and solution quality is badly affected.
In this research work, problem is formulated mathematically and insights to a selected
benchmark is provided. Problem complexity is then evaluated in a quantitative way through
estimation of search space of the selected datasets and an understanding to the actual area of search
is developed.
Priority rules are then integrated with the GA (GA-PR) to solve the FJSSP. In this regard,
competitive modification in the rule has been proposed in addition to the integration scheme. The
algorithm is also equipped with adaptive operators which also contribute to its performance. In
addition to this a standalone pure GA (GA-IDT) is also proposed to efficiently solve the target
problem. An iterative diversification technique is embedded into the proposed algorithm which
proficiently manages the intensification and diversification of the population.
The efficacy of both algorithms is tested against standard benchmark problems and it is
concluded that proposed techniques are competitive with other concepts in literature.