Hybrid GA and SA dynamic set-up planning optimization

Abstract

Set-up planning is used to determine the set-up of a workpiece with a certain orientation and fixturing on a worktable, as well as the number and sequence of set-ups and operations performed in each set-up. This paper presents a concurrent constraint planning methodology and a hybrid genetic algorithm (GA) and simulated annealing (SA) approach for set-up planning, and re-set-up planning in a dynamic workshop environment. The proposed approach and optimization methodology analyses the precedence relationships among features to generate a precedence relationship matrix (PRM). Based on the PRM and inquiry results from a dynamic workshop resource database, the hybrid GA and SA approach, which adopts the feature-based representation, optimizes the set-up plan using six cost indices. The PRM acts as the main constraints for the set-up planning optimization. Case studies show that the hybrid GA and SA approach is able to generate optimal results as well as carry out re-set-up planning on the occurrence of work-shop resource changes.