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With Industry 4.0, IT infrastructure has started to be used more effectively in the manufacturing sector. Cyber physical systems, IoT, cloud manufacturing, big data are some of the technologies that make up the concept of Industry 4.0. These technologies have solved many problems in the manufacturing sector. One of these technologies, cloud manufacturing technology, has emerged with the idea of pay as you go. This technology has enabled manufacturing resources to be leased and shared on a global scale. However, it has problems arising from its central structure and the need for a reliable 3rd party. Reliability, security, continuity, scalability, data lock-in, single point failure, data manipulation are some of the main problems. Blockchain (BC) is a decentralized and distributed technology. The data stored on the BC network cannot be altered in any way. With these features, we believe that BC-supported cloud manufacturing systems can overcome the aforementioned problems and eliminates the need for a reliable 3rd party. Based on this belief, in this study the agreements and communication between the resource provider and the customer, which is one of the basic functions of cloud manufacturing platforms, are realized with a decentralized application using BC-based smart contracts (SCs). The designed application is called the decentralized cloud manufacturing application (DCMApp). DCMApp does not operate on a fully public BC network, it has a hybrid structure and uses the Ethereum network as a public BC network. These features make DCMApp different from other BC-based cloud manufacturing applications. DCMApp's hybrid structure has enabled more transparent, economic and safe manufacturing agreements. It is also possible to store agreements on the BC network at a low cost without installing any server infrastructure. The use of Ethereum network makes it almost impossible to manipulate agreements. INDEX TERMS Blockchain, cloud manufacturing, decentralized manufacturing.

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Traditionally process planning, scheduling and due date assignment are treated separately. Some works are done on integrated process planning and scheduling and on scheduling with due-date assignment. However integrating all of these functions is not treated. Since scheduling problems alone belong to NP-hard class problems, integrated problems are even harder to solve. In this study process planning and scheduling and SLK due date assignment are integrated using genetic algorithms and Random search techniques. Earliness, Tardiness and length of due-dates are punished. While earliness and tardiness are punished quadratically, due-date is punished linearly. Three results were compared. One is ordinary solution, another one is random search solution and the third one is genetic algorithm solution. Genetic algorithm solution outperforms the other solutions and Random search solution is the second best and ordinary solution is the worst solution. 1

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