An ant algorithm for optimization of hole-making operations
Computers and Industrial Engineering
Robotics and Computer-Integrated Manufacturing
Ant colony optimization combined with taboo search for the job shop scheduling problem
Computers and Operations Research
An ant colony optimization algorithm for scheduling virtual cellular manufacturing systems
International Journal of Computer Integrated Manufacturing
A simulated annealing-based optimization approach for integrated process planning and scheduling
International Journal of Computer Integrated Manufacturing
Applications of particle swarm optimisation in integrated process planning and scheduling
Robotics and Computer-Integrated Manufacturing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Using genetic algorithms in process planning for job shop machining
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Journal of Intelligent Manufacturing
Efficient metaheuristics for pick and place robotic systems optimization
Journal of Intelligent Manufacturing
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One objective of process planning optimization is to cut down the total cost for machining process, and the ant colony optimization (ACO) algorithm is used for the optimization in this paper. Firstly, the process planning problem, considering the selection of machining resources, operations sequence optimization and the manufacturing constraints, is mapped to a weighted graph and is converted to a constraint-based traveling salesman problem. The operation sets for each manufacturing features are mapped to city groups, the costs for machining processes (including machine cost and tool cost) are converted to the weights of the cities; the costs for preparing processes (including machine changing, tool changing and set-up changing) are converted to the `distance' between cities. Then, the mathematical model for process planning problem is constructed by considering the machining constraints and goal of optimization. The ACO algorithm has been employed to solve the proposed mathematical model. In order to ensure the feasibility of the process plans, the Constraint Matrix and State Matrix are used in this algorithm to show the state of the operations and the searching range of the candidate operations. Two prismatic parts are used to compare the ACO algorithm with tabu search, simulated annealing and genetic algorithm. The computing results show that the ACO algorithm performs well in process planning optimization than other three algorithms.