Ant colony optimization approach based on precedence constraint matrix for flexible process planning
An innovative approach integrated into search based on ant colony optimization (ACO) is used to optimize the flexible process planning with the objective of minimizing total weight costs (TWC) against precedence constraints. First, the flexible process planning (FPP) is described as the ordering of the alternative machining operations by decomposing processing operation into several optional machining operations based on different tool access directions, and determining the precedence constraints of the alternative machining operations. Due to the determination of the set of feasible alternative machining operations of processing operation, and the use of the precedence constraint matrix to describe the precedence constraint relationship, the sequence of precedence constraint becomes the limitation of search space for seeking optimal solution. Then, the ant colony algorithm is employed to search the set sequence of the alternative machining operations based on the search space limitation method. Since each ant gets a feasible operation routing, the optimal manufacturing resource of each alternative operation is obtained from the randomly selected manufacturing resource by the minimum cost rule. Finally, compared with the existing genetic algorithm, tabu search, simulated annealing and general ant colony algorithm, the proposed algorithm is proved to be feasibility and competitiveness by instance.