As a key technology of the Industrial Internet Platform (IIP), manufacturing service collaboration optimization (MSCO) aims to enhance the efficiency of manufacturing service collaboration (MSC) by integrating distributed manufacturing resources. The current research on MSCO primarily focuses on two single-sided paradigms: customer-based and supplier-based collaboration, each with distinct strengths and limitations. Building upon these paradigms, this paper proposes a two-sided manufacturing service collaboration optimization (TS-MSCO) model that jointly considers both the customer and supplier sides. On the customer side, a service window reservation mechanism is introduced to improve task scheduling efficiency. On the supply side, a multi-path logistics coordination mechanism is designed to support the execution of manufacturing tasks (MT). Finally, a multi-strategy integrated dual-population co-evolutionary algorithm (MSIDCA) is proposed to solve a complex optimization model. The experimental results indicate that the proposed TS-MSCO model achieves an effective balance between customer satisfaction and supplier cost, resulting in superior overall collaboration quality compared with two single-sided collaboration models. Sensitivity analyses conducted under varying disturbance levels further verify the robustness of the proposed model in dynamic manufacturing environments. In addition, the MSIDCA demonstrates superior convergence behavior, solution quality, and overall performance when compared with the latest algorithms.
Wang et al. (Sun,) studied this question.