Research on a collaborative multi-order allocation method for mass customization in cloud manufacturing

In mass customization, order tasks must strike a balance between batch production and customization, minimizing costs while ensuring fast delivery. Effective collaboration between enterprises and suppliers necessitates careful consideration of order allocation granularity and dynamic resource production capacities to achieve optimal task distribution. This paper introduces a multi-order collaborative allocation model based on a product-process mix, aimed to address the order allocation challenges faced by core manufacturing enterprises in cloud manufacturing under mass customization. First, the limitations of existing order decomposition and resource constraint strategies in the order allocation process are analyzed. A product-process mixed multi-order collaborative allocation model is then established, focusing on minimizing multiple-order costs while accounting for dynamic resource capacity constraints. Next, an improved ant colony algorithm (IACO-a&b) is proposed, tailored to the features of the model. This algorithm enhances the initialization of the ant search solution space by combining the optimal solution searched by the ants, while dynamically adjusts pheromone volatility coefficients and sets path pheromone concentration intervals, so as to avoid preventing the algorithm from precociousness. Finally, the performance of IACO-a&b is compared with several other algorithms, and the proposed order allocation model is evaluated against traditional models in this paper. The experimental results demonstrate that, in large-scale case studies, IACO-a&b improves the best fitness value by 11.89% and increases the fitness excellence rate by 60%. Furthermore, the proposed model reduces the leveling volatility index (LVI) of resources by 50.79% without significantly increasing order costs.