Scheduling problem using genetic algorithm, simulated annealing and the effects of parameter values on GA performance

Genetics algorithms have been designed as general purpose optimisation methods. Simulated annealing simulates the cooling process of solid materials-known as annealing. However this analogy is limited to the physical movement of the molecules without involving complex thermodynamic systems. Physical annealing refers to the process of cooling a solid material so that it reaches a low energy state. Initially the solid is heated up to the melting point. Then it is cooled very slowly, allowing it is to come to thermal equilibrium at each temperature. This process of slow cooling is called annealing. The goal is to find the best arrangement of molecules that minimises the energy of the system, which is referred to as the ground state of the solid material. If the cooling process is fast, the solid will not attain the ground state, but a locally optimal structure. In this paper presents a general purpose schedule optimiser for manufacturing shop scheduling using genetic algorithms and simulated annealing. Then, the ‘uniform order-based’ crossover and mutation operators and novel general effects of parameter values on minimised objective value are presented.