Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—The decision of selecting a process plan in a manufacturing system is crucial. Any sequence of manufacturing operations that is generated in a process plan cannot be the best possible sequence every time in a changing production environment. As the complexity of the product increases, the number of feasible sequences increases exponentially and there is a need to choose the best among them. In a dynamic workshop environment, the availability of alternate machines, tools and fixtures must be considered to achieve the global lowest machining cost. In this paper process planning is modeled as a combinatorial optimization problem with constraints, and an Ant Colony Optimization (ACO) approach has been used to solve it. Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The natural metaphor on which ant algorithms are based is that of ant colonies. Fascinated by the ability of the almost blind ants to establish the shortest route from their nests to the food source and back, researchers found out that these ants secrete a substance called ‘pheromone’ and use its trails as a medium for communicating information among each other The ant algorithm is simple to implement and results of the case studies show its ability to provide speedy and accurate solutions.
Index Terms—Process planning, Ant colony optimization, Precedence relationship matrix
Cite: P S Srinivas, V Ramachandra Raju, and C S P Rao, "An Ant Colony Optimization Algorithm for Optimization of Process Plans ," International Journal of Mechanical Engineering and Robotics Research, Vol.1, No.3, pp. 31-42, October 2012.
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