A two-stage ant colony optimization approach based on a directed graph for process planning

Abstract

An innovative approach based on the two-stage ant colony optimization (ACO) approach is used to optimize the process plan with the objective of minimizing total production costs (TPC) against process constraints. First, the process planning (PP) problem is represented as a directed graph that consists of nodes, directed/undirected arcs, and OR relations. The ant colony finds the shortest path on the graph to achieve the optimal solution. Second, a two-stage ACO approach is introduced to deal with the PP problem based on the graph. In the first stage, the ant colony is guided by pheromones and heuristic information of the nodes on the graph, which will be reduced to a simple weighed graph consisting of the favorable nodes and the directed/undirected arcs linking those nodes. In the second stage, the ant colony is guided by heuristic information of nodes and pheromones of arcs on the simple graph to achieve the optimal solution. Third, the simulation experiments for two parts are conducted to illustrate the application of the two-stage ACO approach to the PP problem. The compared results with the results of other algorithms verify the feasibility and competitiveness of the proposed approach.