• A framework for cloud remanufacturing, encompassing a series of remanufacturing macroscopic phases, is established. • A multi-phase integrated scheduling problem for the proposed cloud remanufacturing system is introduced. • A mathematical model is developed to explain the scheduling problem. • An improved whale optimization algorithm integrating enhanced population updating mechanisms is designed to address this problem. The cloud remanufacturing system embraces a series of interdependent remanufacturing macroscopic phases (RMAs) with intricate precedence relationships, increasing the complexity of task scheduling and resource allocation. Thus, the multi-phase integrated scheduling is necessary to manage remanufacturing tasks and optimize resources and capabilities effectively in the cloud environment. This research investigates the multi-phase integrated scheduling problem for cloud remanufacturing system involving a series of RMAs including initial inspection, disassembly, reprocessing, reassembly, and final test. A mathematical model is created to explain the scheduling issue using the suggested cloud remanufacturing framework. Due to the high complexity of integrated scheduling, traditional meta -heuristic algorithms cannot be directly applied to solving the problem. Thus, an improved whale optimization algorithm (IWOA) incorporating the self-adaptive weighting and quadratic interpolation techniques is proposed for addressing the studied problem efficiently. A case study is designed and conducted, and the findings indicate that the IWOA is more effective than other methods in addressing the proposed complex scheduling issues with better accuracy, faster computation, and improved convergence efficiency.