In Process Planning Problem, Application Of Simulated Annealing Algorithm

Basically, process planning is to discover a plan that minimizes the cost component. Though, searching via the solution space exclusively is not possible because this is a NP - complete problem. A simple approach termed as gradient descent is first utilized.

The concept is to start along with a random but valid solution and then consider little changes to this. Merely those changes leading to a smaller cost are accepted. It is repeated till no changes can be made that lead to a cost reduction. The problem along with this type of approach is that the solution is often trapped in local minima. To resolve such problem, the principle of SA is incorporated into the gradient descent algorithm. There are various reports of employing simulated annealing to resolve the process sequencing and planning problem. Conversely, simulated annealing algorithm explores the search space in a quite limited scope. In this manner, a new variant is until now to be explored in order to address the process planning problem comprehensively. The SA based search algorithm can be usually implemented as given:

The body of the algorithm has of two iterative loops. The inner loop's body generates changes to the configuration beneath one temperature, and accepts them along with several probabilities according to Boltzman's expression. The outer loop's body is executed as long as the configuration is unstable. Such outer loop executes and after that reduces the recent temperature as per to the cooling schedule. This generic algorithm for implementing, several of the specific matters are discussed as given below: