Introduction to Scheduling in Industry 4.0 and Cloud Manufacturing Systems

Chapter 1. Introduction to scheduling in Industry 4.0 and cloud manufacturing systems.- Chapter 2. Proactive scheduling and reactive real time control in Industry 4.0 .- Chapter 3. Using a digital-twin for production planning and control in Industry 4.0.- Chapter 4. Adaptive Scheduling in the era of Cloud Manufacturing.- Chapter 5. Cloud Material Handling Systems: conceptual model and cloud-based scheduling of handling activities.- Chapter 6. Coupling robust optimization and Model-Checking techniques for robust scheduling in the context of Industry 4.0.- Chapter 7. Integrated scheduling of information services and logistics flows in the omnichannel system.- Chapter 8. Human-oriented assembly line balancing and sequencing model in the Industry 4.0 era.- Chapter 9. A generic decision support tool to planning and assignment problems: Industrial applications & Industry 4.0.- Chapter 10. The Manufacturing Planning and Control system: a journey towards the new perspectives in Industry 4.0 architectures.- Chapter 11. Multi-criteria single batch machine scheduling under time-of-use tariffs.- Chapter 12. Service composition in cloud manufacturing: a DQN-based approach.- Chapter 13. The Tolerance Scheduling Problem in a single machine case.

Manufacturing service scheduling (MSS) is an important step in managing the social resource services in the cloud manufacturing (CMfg) system. However, recent research investigates the problem almost from the task level, and little research considers the demands of multiple users in MSS problem. In this paper, the obvious characteristics of multi-user-oriented MSS are analysed by comparing with the multi-task-oriented MSS problem, and then a multi-user-oriented MSS mathematical model is built to cater to the practical demands of multiple users. In order to solve the proposed model, an improved NSGA-II (INSGA-II), integrating k-means algorithm and local search strategy, is developed to improve the quality of solutions. Six scenarios are given to verify the effectiveness of the proposed algorithm by comparing with other three algorithms from four metrics. The flexibility and universality of the proposed model is examined and the effect of user requirements on the Pareto solution is analysed. The results present the efficiency of k-means cluster and local search in the INSGA-II algorithm and provide a practical solution to select the better schedule for users.

In this study, we proposed a game-theory based framework to model the dynamic pricing process in the cloud manufacturing (CMfg) system. We considered a service provider (SP), a broker agent (BA), and a dynamic service demander (SD) population that is composed of price takers and bargainers in this study. The pricing processes under linear demand and constant elasticity demand were modeled, respectively. The combined effects of SD population structure, negotiation, and demand forms on the SP’s and the BA’s equilibrium prices and expected revenues were examined. We found that the SP’s optimal wholesale price, the BA’s optimal reservation price, and posted price all increase with the proportion of price takers under linear demand but decrease with it under constant elasticity demand. We also found that the BA’s optimal reservation price increases with bargainers’ power no matter under what kind of demand. Through analyzing the participants’ revenues, we showed that a dynamic SD population with a high ratio of price takers would benefit the SP and the BA.

Scheduling in Industry 4.0 systems belongs to a class of problems that have mixed structural-temporal-logical constraints. In other words, a strong coupling is considered when product and process are created simultaneously. As a result of the proven NP-hardness of such problems, solution methods have extensively utilized different decomposition principles. The known decomposition methods in discrete optimization are founded on the difficulties in deriving analytical properties. The existing solutions in continuous optimization are based on the maximum principle and yield a dynamic process decomposition using the natural logic of time. By combining the advantages of continuous and discrete optimization, this chapter develops a decomposition method for shop floor scheduling in Industry 4.0 manufacturing systems. Technically, this study proposes to decompose dynamically the large-scale assignment matrix according to the precedence relations between the operations of the jobs and considers only the operations that satisfy these precedence relations at a given time point in small-dimensional, discrete optimization models. Continuous optimization is used to generate a schedule from the assignments found in the discrete optimization models at each time point by extremizing the Hamiltonian function at this time point subject to scheduling objective(s). In addition, the execution of the operations in time can be accurately modeled in continuous time as a continuous state variable; the machine availability and capacity disturbances at the machines are also considered. The method developed provides further insights into decomposition methods for scheduling and is supported by an analytical analysis and an algorithmic realization.

Industry 4.0 is the digitalization of the manufacturing systems based on Information and Communication Technologies (ICT) for developing a manufacturing system to gain efficiency and improve productivity. Cloud Manufacturing (CM) is a paradigm of Industry 4.0. Cloud Manufacturing System (CMS) considers anything as a service. The end product is developed based on the service composition in the CMS according to consumers’ needs. Also, composite services are developed based on the interaction of MCS providers from different geographical locations. Therefore, the appropriate Manufacturing Cloud Service (MCS) composition is an important problem based on the real-world conditions in CMS. The game theory studies the mathematical model development based on interactions between MCS providers according to real-world conditions. This research develops an Equilibrial Service Composition Model in Cloud Manufacturing (ESCM) based on game theory. MCS providers and consumers get benefits mutually based on ESCM. MCS providers are players in the game. The payoff function is developed based on a profit function. Also, the game strategies are the levels of Quality of Service (QoS) based on consumers’ needs in ESCM. Firstly, the article develops a composite service based on a non-cooperative game. The Nash equilibrium point demonstrates the QoS value of composite service and the payoff value for the players. Secondly, the article develops a composite service based on a cooperative game. The players participate in coalitions to develop the composite service based on formal cooperation. The grand coalition demonstrates the QoS value of composite service and the payoff value for the players in the cooperative game. The research has compared the games’ results. The players’ payoff and the QoS value are better in the cooperative game than in the non-cooperative game. Therefore, the MCS providers and consumers are satisfied mutually in the cooperative game based on ESCM. Finally, the article has applied ESCM in a Healthcare Service to equip 24 hospitals in the best time.

In order to realize the manufacturing resource sharing and optimal allocation, many advanced manufacturing modes and technologies have been proposed and researched, but few works have been emphasized on resource service and capability transaction and scheduling management. With these conditions, in this article, the concept of cloud manufacturing mode is first briefly introduced, and a general cloud manufacturing resource service scheduling model is established. Then, the comprehensive utility models, which consider energy consumption, cost, and risk for the three sides (i.e. provider, consumer, and operator), are established in the resource service scheduling process in cloud manufacturing system. Four kinds of resource service scheduling modes, which are provider centered, consumer centered, operator centered, and system centered, are studied. These modes are based on the utility models that are investigated, as well as the system-centered cooperative scheduling method that is designed with risk-sharing strategy. The experimental results show that the system-centered scheduling method has the highest potential for realizing the aim of cloud manufacturing, and the system-centered cooperative scheduling method is rational to obtain the higher maximal utilities of the whole system and the three key users at the same time. It promotes the higher efficiency and utility, a higher rate of sharing, green and on-demand use of decentralized manufacturing resources and capability services in cloud manufacturing systems.

The 4th Industrial Revolution comprises of SMART factories, SMART manufacturing, Cloud manufacturing and makes use of Internet of Things, Internet of Services and Cyber Physical Systems. The rapid expansion has left shortcomings in defining proper communication protocols between the different components within this industrial revolution. A number of research articles have set out to establish communication protocols between SMART manufacturing units and Cloud manufacturing systems however, there is still not enough to prove for the use of their own protocols within a working case study. A research void exists within this field to establish a foundation to classify the machinery as SMART and to standardize the protocols needed. The purpose of this paper is to review the different communication protocols in the different layers of Cloud manufacturing. This is done by reviewing relevant research done in this field in the recent past. This analysis will give an indication of the progress made in this field and the possible areas for future research.

The globalised machine-tool manufacturing enterprises are eager to develop intelligent machine tools and novel business models to increase their competitiveness. In recent years, cloud manufacturing, encapsulating distributed manufacturing resources into cloud services for supporting all tasks in a product life cycle, has emerged as a promising concept and approach for the machining industry to achieve such a goal and gain profits. However, there has been no systematic approach to the development of cloud manufacturing systems (CMSs) for the machining industry so far. In this paper, we propose a novel cloud manufacturing framework (CMF) with auto-scaling capability (called CMFAS) aimed at providing a systematic and rapid development approach for building CMSs. The proposed CMFAS contains a cloud-based architecture which can transform single-user manufacturing functions (MFs) into cloud services that can be accessed by many users simultaneously. Also, a user-acceptable time-based scaling algorithm is designed so that the CMFAS can automatically perform scale-out or scale-in on the number of virtual machines (VMs) according to the user arrival rate, while confining the average service time for a user to be less than a specified user-acceptable time using a minimum number of VMs. Finally, we develop an Ontology Inference Cloud Service (OICS) for machine tools based on the CMFAS and deploy it on a public cloud platform for conducting integrated tests. Testing results show that the OICS can successfully recommend proper machine tools and cutting tools for machining tasks, and the proposed scaling algorithm outperforms traditional CPU-load-based scaling algorithms in terms of a smaller average service time for a user (i.e. quicker processing time) and a smaller number of created VMs (i.e. less cost of leasing cloud resources). The proposed CMFAS, together with its detailed designs, can serve as a useful reference approach for systematically and rapidly building CMSs for the machining industry.

Smart manufacturing refers to integrating new generation information technologies and advanced manufacturing technologies to improve enterprise efficiency, increase customer satisfaction and enhance market competitiveness. Cloud manufacturing (CMfg) is a typical paradigm of smart manufacturing. With the rapid deployment and application of various CMfg systems, it is extremely urgent and significant to implement the standardized semantic description of manufacturing resources (MRs) characterized by geographical dispersion, high autonomy, and heterogeneity. In this paper, a metadata based manufacturing resource ontology modeling method is proposed to solve the consistency description problem of MRs in cloud. Firstly, the characteristic attributes of MRs in CMfg systems are analyzed, and a metadata-based manufacturing resource ontology-modeling framework is constructed. Then, a manufacturing resource domain ontology (MR_Onto) and a metadata model (MR_Meta) are designed, the mapping rules between them are discussed. Finally, a prototype tool describing manufacturing resources is developed, the feasibility and effectiveness of the proposed method and the developed tool are verified in an application case.

A Mathematical Model for Task Scheduling in Cloud Manufacturing Systems focusing on Global Logistics

Cloud manufacturing is a new globalized manufacturing framework which has improved the performance of manufacturing systems. The service-oriented architecture as the main idea behind this framework means that all resources and capabilities are considered as services. The agents interact by way of service exchanging, which has been a part of service composition research topics. Service allocations to demanders in a cloud manufacturing system have a dynamic behavior. However, the current research studies on cloud-based service composition are mainly based on centralized global optimization models. Therefore, a distributed deployment and real-time synchronization platform, which enables the globalized collaboration in service composition, is required. This paper proposes a method of using blockchain to solve these issues. Each service composition is considered as a transaction in the blockchain concept. A block includes a set of service compositions and its validity is confirmed by a predefined consensus mechanism. In the suggested platform, the mining role in blockchain is interpreted as an endeavor for proposing the proper service composition in the manufacturing paradigm. The proposed platform has interesting capabilities as it can increase the response time using the blockchain technology and improve the overall optimality of supply-demand matching in cloud manufacturing. The efficiency of the proposed model was evaluated by investigating a service allocation problem in a cloud manufacturing system in four large scale problems. Each problem is examined in four centralized modes, two, three and four solvers in blockchain-based model. The simulation results indicate the high quality of the proposed solution. The proposed solution will lead to at least 15.14% and a maximum of 34.8 percent reduction in costs and 20 to 68.4 percent at the solving time of the problem. It is also observed that with increasing the number of solvers (especially in problems with larger dimensions) the solution speed increases sharply (more than 68% improvement in some problems), which indicates the positive effect of distribution on reducing the problem-solving time.

Service-oriented industrial internet of things gateway for cloud manufacturing

Cloud manufacturing is a new globalized manufacturing framework which has improved the performance of manufacturing systems. The service-oriented architecture as the main idea behind this framework means that all resources and capabilities are considered as services. The agents interact by way of service exchanging, which has been a part of service composition research topics. Service allocations to demanders in a cloud manufacturing system have a dynamic behavior. However, the current research studies on cloud-based service composition are mainly based on centralized global optimization models. Therefore, a distributed deployment and real-time synchronization platform, which enables the globalized collaboration in service composition, is required. This paper proposes a method of using blockchain to solve these issues. Each service composition is considered as a transaction in the blockchain concept. A block includes a set of service compositions and its validity is confirmed by a predefined consensus mechanism. In the suggested platform, the mining role in blockchain is interpreted as an endeavor for proposing the proper service composition in the manufacturing paradigm. The proposed platform has interesting capabilities as it can increase the response time using the blockchain technology and improve the overall optimality of supply-demand matching in cloud manufacturing. The efficiency of the proposed model was evaluated by investigating a service allocation problem in a cloud manufacturing system in four large scale problems. Each problem is examined in four centralized modes, two, three and four solvers in blockchain-based model. The simulation results indicate the high quality of the proposed solution. The proposed solution will lead to at least 15.14% and a maximum of 34.8 percent reduction in costs and 20 to 68.4 percent at the solving time of the problem. It is also observed that with increasing the number of solvers (especially in problems with larger dimensions) the solution speed increases sharply (more than 68% improvement in some problems), which indicates the positive effect of distribution on reducing the problem-solving time.

Cloud manufacturing is a new globalized manufacturing framework which has improved the performance of manufacturing systems. The service-oriented architecture as the main idea behind this framework means that all resources and capabilities are considered as services. The agents interact by way of service exchanging, which has been a part of service composition research topics. Service allocations to demanders in a cloud manufacturing system have a dynamic behavior. However, the current research studies on cloud-based service composition are mainly based on centralized global optimization models. Therefore, a distributed deployment and real-time synchronization platform, which enables the globalized collaboration in service composition, is required. This paper proposes a method of using blockchain to solve these issues. Each service composition is considered as a transaction in the blockchain concept. A block includes a set of service compositions and its validity is confirmed by a predefined consensus mechanism. In the suggested platform, the mining role in blockchain is interpreted as an endeavor for proposing the proper service composition in the manufacturing paradigm. The proposed platform has interesting capabilities as it can increase the response time using the blockchain technology and improve the overall optimality of supply-demand matching in cloud manufacturing. The efficiency of the proposed model was evaluated by investigating a service allocation problem in a cloud manufacturing system in four large scale problems. Each problem is examined in four centralized modes, two, three and four solvers in blockchain-based model. The simulation results indicate the high quality of the proposed solution. The proposed solution will lead to at least 15.14% and a maximum of 34.8 percent reduction in costs and 20 to 68.4 percent at the solving time of the problem. It is also observed that with increasing the number of solvers (especially in problems with larger dimensions) the solution speed increases sharply (more than 68% improvement in some problems), which indicates the positive effect of distribution on reducing the problem-solving time.

Cloud manufacturing is a new globalized manufacturing framework which has improved the performance of manufacturing systems. The service-oriented architecture as the main idea behind this framework means that all resources and capabilities are considered as services. The agents interact by way of service exchanging, which has been a part of service composition research topics. Service allocations to demanders in a cloud manufacturing system have a dynamic behavior. However, the current research studies on cloud-based service composition are mainly based on centralized global optimization models. Therefore, a distributed deployment and real-time synchronization platform, which enables the globalized collaboration in service composition, is required. This paper proposes a method of using blockchain to solve these issues. Each service composition is considered as a transaction in the blockchain concept. A block includes a set of service compositions and its validity is confirmed by a predefined consensus mechanism. In the suggested platform, the mining role in blockchain is interpreted as an endeavor for proposing the proper service composition in the manufacturing paradigm. The proposed platform has interesting capabilities as it can increase the response time using the blockchain technology and improve the overall optimality of supply-demand matching in cloud manufacturing. The efficiency of the proposed model was evaluated by investigating a service allocation problem in a cloud manufacturing system in four large scale problems. Each problem is examined in four centralized modes, two, three and four solvers in blockchain-based model. The simulation results indicate the high quality of the proposed solution. The proposed solution will lead to at least 15.14% and a maximum of 34.8 percent reduction in costs and 20 to 68.4 percent at the solving time of the problem. It is also observed that with increasing the number of solvers (especially in problems with larger dimensions) the solution speed increases sharply (more than 68% improvement in some problems), which indicates the positive effect of distribution on reducing the problem-solving time.