The purpose is to optimize supply chain management (SCM) programs in global organizations and promote international economic development. This paper first discusses the development of cloud technology and the international organization supply chain under big data, artificial intelligence, the internet of things, mobile internet and cloud computing (BIIMC). Then, the application design concept of cloud technology in the global organization supply chain is discussed. Finally, based on the current international organization supply chain foundation, the management optimization program is designed using cloud technology and evaluated. The results show that the program performs prominently in the SCM of Taobao and Amazon.

Precast component production is an important part of industrialization in construction. However, in actual production, it often faces problems such as high resource occupation, long production cycles, and high delivery costs caused by unreasonable scheduling arrangements. This study proposes a hybrid multi-objective optimization framework that combines Q-learning with the non-dominated sorting genetic algorithm, termed QL-NSGA-II, to address the highly coupled problem of component grouping on pallets and production scheduling in precast component manufacturing. The model aims to collaboratively minimize the number of pallets used, the makespan, and the penalty cost. The algorithm constructs a dual Q-table Markov decision architecture, in which the agent adaptively coordinates PC component grouping and scheduling strategies based on reward feedback from the evolutionary state. Through extreme-value tests on multiple benchmark instances from the literature, the results show that the proposed QL-NSGA-II not only achieves the accuracy of the benchmark solutions, but also, in some cases, reduces the makespan and penalty cost by 34.77% and 60.24%, respectively, compared with existing benchmark algorithms. In a real industrial order experiment with 87 components, the algorithm notably outperforms the comparison algorithms across comprehensive convergence metrics such as hypervolume and inverted generational distance, confirming its favorable practical application potential for balancing pallet resource consumption and flow cycle.