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The manufacturing industry has evolved with the emergence of cloud computing, leading to the development of cloud manufacturing — a customer-oriented paradigm. The integration of scheduling and logistics in cloud manufacturing is paramount and distinguishes it from traditional manufacturing. Tasks can have three different structures, so this study presents three models that integrate scheduling and logistics for tasks with sequential, parallel, and loop structures, respectively. These models aim to minimize the total cost of the manufacturing system, which includes the implementation cost of subtasks, logistical services cost among factories in different geographical locations, logistical services cost for delivery of the order (task) to the customers, and the earliness/tardiness cost of orders. The numerical examples in small and medium sizes are solved using the CPLEX solver in the GAMS software. However, due to the high complexity of the models presented, a genetic algorithm is developed to solve large examples. To showcase the significance of the main features of the proposed models, two comparable models are employed, each with one feature removed. In addition to the factors that are considered to get the models close to reality, a sensitivity analysis is conducted to design effective guidelines for cloud manufacturing managers.