Optimization of operation sequencing in computer aided process planning using hybrid S Gensat algorithm

Abstract

Computer-aided process planning (CAPP) is an important interface between computer-aided design and computer-aided manufacturing in computer integrated manufacturing environment. The operation sequencing problem may usually be modeled as a large-scale and combinatorial optimization problem and have been solved using integer programming, search heuristics, Genetic Algorithm (GA), Simulated Annealing Technique (SAT), Ant Colony Algorithm (ACA), Particle Swam Optimization (PSO) and hybrid GA approaches. The draw backs reported with the above mentioned methods are trapping in local minima, probing large solution space, consuming more computational time and failing to find alternate optimal feasible sequences. To overcome these difficulties, by combining GA and SAT in an appropriate way a new super hybrid genetic algorithms-simulated annealing technique (S-GENSAT) is developed in this research work. The feasible sequences of operations are generated using this hybrid algorithm, based on the inputs precedence cost matrix (PCM), and reward-penalty matrix (REPMAX). Further, incorporation of solution space reduction technique (SSRT) based on PCM and REPMAX upgrades the procedure to superhybridization. The present technique is divided into two phases, the GA and SAT. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. Five benchmark case studies are taken to demonstrate the feasibility and robustness of the proposed super-hybrid algorithm. These test cases demonstrate that the newly developed S-GENSAT algorithm can solve complex problems in CAPP by providing equal or better operation sequences and lower optimal cost with significant reduced computational time, when compared to that reported in the literature newline