Enhanced simulated-annealing-based algorithms and their applications to process planning in reconfigurable manufacturing systems

Capabilities of enhanced simulated-annealing-based algorithms in solving process planning problems in reconfigurable manufacturing are investigated. The algorithms are enhanced by combining variants of the simulated annealing technique with other algorithm concepts such as (i) knowledge exploitation and (ii) parallelism. Four configurations of simulated annealing algorithms are devised and engaged to solve an instance of a process planning problem in reconfigurable manufacturing systems. These configurations include; a basic simulated annealing algorithm, a variant of the basic simulated annealing algorithm, a variant of the simulated annealing algorithm coupled with auxiliary knowledge and a variant of the simulated annealing algorithm implemented in a quasi-parallel architecture. Although differences in performances were observed, the implemented algorithms are capable of obtaining good solutions in reasonable time. Experimental results show that the performances of the variants of simulated annealing based algorithms are better in comparison to a basic simulated annealing algorithm. A computational analysis and comparison using ANOVA indicates that improvements towards a better optimal solution can be gained by implementing variants of the simulated annealing algorithm. In addition, little speed gains can be obtained by implementing variants of the simulated annealing algorithms that are coupled with other algorithmic concepts.