Scheduling in Industry 4.0 and Cloud Manufacturing

Cloud manufacturing is a new service-oriented manufacturing model that integrates distributed manufacturing resources to provide on-demand manufacturing services over the Internet. Service composition that builds larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers’ complex requirements is an important issue in cloud manufacturing. Meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. However, these algorithms require complex design flows and lack adaptability to dynamic environment. Deep reinforcement learning provides an alternative approach for solving cloud manufacturing service composition issues. This chapter proposes a deep Q-network (DQN) based approach for service composition in cloud manufacturing, which is able to find optimal service composition solutions through repeated training and learning. Results of experiments that take into account changes of service scales and service unavailability reveal the scalability and robustness of the DQN algorithm-based service composition approach.

Cloud manufacturing is an emerging novel business paradigm for the manufacturing industry. In cloud manufacturing, distributed manufacturing resources are encapsulated into services and aggregated in a cloud manufacturing platform. Through centralized service management, cloud manufacturing is capable of dealing with multiple requirement tasks simultaneously. The ability to deal with multiple tasks at the same time is an important characteristic that distinguishes cloud manufacturing from the previous networked manufacturing models such as manufacturing grid. When it comes to multiple tasks in cloud manufacturing, a critical issue is how to schedule massive services to complete them with shortest makespan, lowest cost, and highest quality, etc. In order to facilitate the research on this issue, we in this paper propose a model for multitask-oriented service composition and scheduling in cloud manufacturing, in which key factures of cloud manufacturing such as service orientation, involvement of logistics, and dynamical change of service availability are taken into account. New concepts such as service efficiency, enterprise capability, and task workload are introduced, and various types of times including service time, logistics time, and waiting time are analyzed in detail. Moreover, this model can be conveniently extended by incorporating new elements such as task constraints, task priority, and continuous task arrival. An example that motivates the current model is presented. Simulation experiments with different numbers of tasks are performed to demonstrate the feasibility of the model.

Within the cloud manufacturing paradigm, Cloud Manufacturing Service Composition (CMSC) is a core technology for intelligent resource orchestration in Cloud Manufacturing Platforms (CMP). However, existing research faces critical limitations in real-world CMP operations: single-task-centric optimization ignores resource sharing/competition among coexisting manufacturing tasks (MTs), causing performance degradation and resource “starvation”; traditional heuristics require full re-execution for new scenarios, failing to support real-time online decision-making; single-agent reinforcement learning (RL) lacks mechanisms to balance global efficiency and inter-task fairness, suffering from inherent fairness defects. To address these challenges, this paper proposes a fairness-aware multi-task CMSC method based on Multi-Agent Reinforcement Learning (MARL) under the Centralized Training with Decentralized Execution (CTDE) framework, targeting the symmetry-breaking issue of uneven resource allocation among MTs and aiming to achieve symmetry restoration by restoring relative balance in resource acquisition. The method constructs a multi-task CMSC model that captures real-world resource sharing/competition among concurrent MTs, and integrates a centralized global coordination agent into the MARL framework (with independent task agents per MT) to dynamically regulate resource selection probabilities, overcoming single-agent fairness defects while preserving distributed autonomy. Additionally, a two-layer attention mechanism is introduced—task-level self-attention for intra-task subtask correlations and global state self-attention for critical resource features—enabling precise synergy between local task characteristics and global resource states. Experiments verify that the proposed method significantly enhances inter-task fairness while maintaining superior global Quality of Service (QoS), demonstrating its effectiveness in balancing efficiency and fairness for dynamic multi-task CMSC.

Cloud manufacturing as an emerging service-oriented manufacturing paradigm integrates and manages geographically distributed manufacturing resources such that complex and highly customized manufacturing tasks can be performed cooperatively. The service composition and optimal selection (SCOS) problem, in which manufacturing cloud services are optimally selected for performing subtasks, is one of the key issues for implementing a cloud manufacturing system. In this paper, we propose a new mixed-integer programming model for solving the SCOS problem with sequential composition structure. Unlike the majority of previous research on the problem, in the proposed model, the transportation between distributed resources and its effects on quality of services are considered. Although a wide variety of metaheuristics have been tailored for solving the SCOS problem, no consistent and comprehensive conclusion has been reached so far on the superiority of a specific algorithm. Therefore, for the first time, solution space landscape of the problem was analyzed through several statistical criteria which demonstrated that the landscape is rugged and local optima are clustered in a small region of the search space. Therefore, to find good solutions, a metaheuristic algorithm needs to perform both proper exploitation and exploration of the search space. According to the landscape analysis, the basic imperialist competitive algorithm (ICA) was hybridized with a local search (LS) algorithm resulting in the hybrid ICA (HICA). To examine the performance of the proposed HICA, an example of online motorcycle production in the USA as well as four randomly generated large-scale instances, were solved through the LS, ICA, and HICA. Computational results showed that transportation consideration is indispensable for obtaining more realistic solutions in cloud manufacturing. The results also revealed that the HICA outperformed the LS and basic ICA in terms of the value of cost objective function, the stability of solutions and convergence speed. Hence, not only statistically but also analytically, it was proved that algorithms incorporating both exploitation and exploration are able to solve the SCOS problem more efficiently.

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.

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.

Intelligent Service Platform of Manufacturing Process and Tool Based on Data Warehouse

Service composition and scheduling in cloud manufacturing (CMfg) has attracted increasing attention due to CMfg's platform's superior ability in integrating distributed resources and distributing integrated resources. Existing studies assume that the manufacturing cloud services (MCSs) of enterprises are all available throughout the planning horizon, yet an MCS may be occupied during specific time intervals. To deal with this issue, we study the multitask‐oriented service composition and scheduling problem (SCSP) in CMfg with unavailable time windows and subtask rejection (MTO‐SCSP‐UTWSR), whereby some subtasks of the CMfg platform's customers may be rejected, which will incur a penalty cost due to the limited service capacity of the MCSs. The customers are divided into subtask‐rejection‐sensitive customers and subtask‐rejection‐insensitive customers, and the total subtask rejection cost of each subtask‐rejection‐sensitive customer cannot exceed a predefined threshold. The objective is to determine the customer‐enterprise allocation scheme, the MCS composition for serving the subtasks allocated to each enterprise, and the subtask schedule for each MCS, subject to some service flexibility constraints, so as to minimize the sum of the customer‐enterprise allocation cost, opening cost of MCSs, and total subtask rejection cost of the subtask‐rejection‐insensitive customers. To solve the problem, we develop a Benders decomposition‐based hybrid algorithm in which the Benders subproblems are solved by a tailored branch‐and‐price‐and‐cut algorithm, which incorporates several enhancement techniques. Extensive numerical studies demonstrate the superiority of the developed algorithm in solving the problem and quantify the impacts of some model parameters on the solution structure and algorithm performance.

Cloud manufacturing (CMfg) is a new type of service-oriented manufacturing paradigm, in which all kinds of manufacturing resources are encapsulated as manufacturing services (MSs) and can be invoked by customers on demand. Manufacturing service composition (MSC) is a key technology in CMfg for creating value-added services to complete complicated manufacturing tasks by aggregating qualified MSs together. However, current MSC approaches have some drawbacks and there still exist some issues remained to be solved: (1) large quantities of candidate services increase the complexity of service dynamic composition, which poses scalability concerns and on-demand efficient solutions; (2) the service domain features (e.g., service prior, correlation, and similarity) that have a strong influence on the efficiency of service composition are not considered adequately, which causes undesirable efficiency in practical service applications; and (3) dynamic characteristics of QoS (quality of service) values in an open network environment are not considered adequately. To effectively address such problems, this paper first proposes a context-aware artificial bee colony (caABC) algorithm based on the principle of ABC and service features in the cloud environment. Then the differential evolution-enhanced caABC, i.e., the so-called DE-caABC, is designed to increase the searching performance of ABC further. Additionally, dynamics of trust QoS is investigated with the introduction of time decay function. Finally, the feasibility and effectiveness of DE-caABC are validated through the experiments.

ABSTRACTThe purpose of this paper is to construct a causal model of dimensions and their attributes that influence composite service resilience in Cloud manufacturing (CM) system. The composite services are regarded as critical components of CM to accomplish manufacturing jobs and are executed in a distributed, heterogeneous and autonomous environment with high uncertainty and dynamicity. The dimensions and attributes of the proposed model were first identified based on resource-based theory and related literature. Then, the DEMATEL technique was used to measure the strength of influence among the studied factors. The required data were collected through the questionnaires replied by experts from industry and academia. The results of data analysis indicate that virtual resource pool and elastic resource management have the most impact on composite service quality of resilience. This study presents a novel causal model to improve the existing knowledge on composite service resilience in the context of CM. Furthermore, the research findings provide system analysts and designers with a clear definition of composite service resilience. They are useful to design explicit strategies for improving the resilience level of the composite services at different layers of CM architecture in practice.

Cloud manufacturing (CMfg) is a relatively new manufacturing paradigm which promotes easier collaboration among geographically-dispersed manufacturers. Various manufacturing resources can be shared via cloud manufacturing platform as services. The efficacy of such resource sharing and thus collaboration in this service-oriented mode of manufacturing is highly dependent to the efficiency of underlying mechanisms used for processing these services. Activities such as service matching, service retrieval, service composition and optimal selection, and service scheduling are among the most critical of those service processing mechanisms. In this paper, we introduce and implement a comprehensive service composition and optimal selection (SCOS) technique that takes advantage of a real-world manufacturing capability dataset, deep learning models as well as Word2Vec technique to retrieve appropriate candidate sets for each submitted manufacturing subtask in the cloud manufacturing platform. Then, a genetic algorithm is implemented to obtain a near-optimal composite service that achieves the highest Quality of Service (QoS). A series of experiments were conducted to prove the feasibility and efficacy of the approach and results were presented.

Cloud manufacturing is a new service-oriented intelligent manufacturing paradigm. Knowledge is a core part and the foundation to realize its intelligence. In this paper, the importance and functions of knowledge to cloud manufacturing was first investigated from the lifecycle of cloud service. Then a knowledge management system was designed and the layered architecture and key technologies were analyzed. A case study was conducted to demonstrate the feasibility of the proposed knowledge management system.

Manufacturing industry is undergoing a major transformation from production-oriented business to service-oriented business, inspired by the advancement of smart technologies and sophisticated demand for product-service systems from the dynamic market. Cloud manufacturing is emerging as a key enabler for manufacturing companies to deliver highly customisable services over the Internet. This paper aims to investigate how cloud manufacturing systems is able to facilitate effective service-oriented business. Major challenges in developing a cloud-based manufacturing marketplace are discussed. A system framework for cloud-based service provision is proposed.

Cloud manufacturing systems (CMSs) are networked, distributed and loosely coupled, so they face great uncertainty and risk. This paper combines the complex network model with multi-agent simulation in a novel approach to the robustness analysis of CMSs. Different evaluation metrics are chosen for the two models, and three different robustness attack strategies are proposed. To verify the effectiveness of the proposed method, a case study is then conducted on a cloud manufacturing project of a new energy vehicle. The results show that both the structural and process-based robustness of the system are lowest under the betweenness-based failure mode, indicating that resource nodes with large betweenness are most important to the robustness of the project. Therefore, the cloud manufacturing platform should focus on monitoring and managing these resources so that they can provide stable services. Under the individual server failure mode, system robustness varies greatly depending on the failure behavior of the service provider: Among the five service providers (S1-S5) given in the experimental group, the failure of Server 1 leads to a sharp decline in robustness, while the failure of Server 2 has little impact. This indicates that the CMS can protect its robustness by identifying key servers and strengthening its supervision of them to prevent them from exiting the platform.