Part selection and operation-machine assignment in FMS environment: A genetic algorithm with chromosome differentiation based methodology

Production planning of a flexible manufacturing system (FMS) is plagued by two interrelated problems, i.e. part type selection and operation allocation on machines. The combination of these problems is termed the machine-loading problem, which is a well-known complex puzzle and treated as a strongly NP-hard problem. In this research, a machine-loading problem has been modelled, taking into consideration several technological constraints related to the flexibility of machines, availability of machining time, tool slots, etc., while aiming to satisfy the objectives of minimizing the system unbalance, maximizing throughput, and achieving very good overall FMS utilization. The solution of such problems, even for moderate numbers of part types and machines, is marked by excessive computation complexities and therefore advanced random search and optimization techniques are needed to resolve them. In this paper, a new kind of genetic algorithm, termed a genetic algorithm with chromosome differentiation, has been used to address a well-known machine-loading problem. The proposed algorithm overcomes the drawbacks of the simple genetic algorithm and the methodology reported here is capable of achieving a better balance between exploration and exploitation and of escaping from local minima. The proposed algorithm has been tested on ten standard test problems adopted from literature and extensive computational experiments have revealed its superiority over earlier approaches.