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A genetic algorithm-based optimisation model for performance parameters of manufacturing tasks in constructing virtual enterprises

The performance parameters of the manufacturing tasks (PPMT) are considered as the key parameters in constructing virtual enterprises (VE). It is difficult to determine the optimal or near-optimal values of PPMT. In this paper, the optimisation process in VE is perceived as a bidirectional optimisation process which consists of the forward and reverse process. By analysing the process, a reverse optimisation model based on vector norm theory is proposed and the target function is defined as a formula of the sum of the weighting Euclidean distance between two PPMT vectors. The existence of optimisation solution for the problem is investigated. Then an adaptive genetic algorithm based on natural number encoding is developed to solve the problem. A practical example is implemented to verify the validity of the proposed model and approach. The discussed results show that the optimal or near-optimal PPMT is helpful for the candidate enterprises selection in the process of constructing a VE.