Cloud Manufacturing Research Articles

Platform-enterprise collaborative scheduling in cloud manufacturing with deep reinforcement learning

Platform-enterprise collaborative scheduling in cloud manufacturing with deep reinforcement learning

Multitask scheduling on cloud additive manufacturing using NSGA-II

Purpose: Cloud manufacturing (CM) represents a new manufacturing paradigm that integrates distributed resources to provide on-demand services. The high consumer demand from various locations, coupled with the customizability and complexity of manufacturing, complicates task scheduling. In this context, 3D printers are crucial as innovative manufacturing technologies with significant potential in producing complex and custom products. Scheduling in CM falls under the non-deterministic polynomial time-hard category, where tasks must be scheduled and distributed rapidly. Considerations of distance, minimization of delays, and makespan become critical variables that must be considered. This research aims to schedule and distribute tasks in CM using the non-dominated sorting genetic algorithm II (NSGA-II) to minimize delays, reduce makespan, and decrease costs.Methodology: NSGA-II is employed to tackle the complexities of scheduling in CM. The strength of NSGA-II lies in its ability to determine optimal and efficient solutions for multiobjective problems. Tasks originating from requests at various locations are adjusted based on material parameters and dimensions and then distributed to providers while considering aspects such as makespan, delay minimization, and cost.Findings: The optimization results using NSGA-II demonstrate effective and efficient task distribution to providers. Across the four tested task distribution scenarios, the average computational time required was 5.59 seconds. Pareto analysis indicates a trade-off between various objective functions. Solutions with short distances tend to have increased maximum time and delays.Originality/value: NSGA-II is effective for task distribution with multiobjective considerations. Not all three objective functions can be optimized simultaneously, given the trade-offs between distance, maximum time, and lateness. The priority of the objective functions should be determined to achieve optimal results. If minimizing lateness is most important, the focus should be on points with low lateness values. Further development can be done by modifying the Pareto front to make data-driven decisions that consider these trade-offs.

Comprehensive review of additive manufacturing lifecycle optimization in a cloud environment: from distributed manufacturing to postprocessing

Purpose This paper aims to offer a comprehensive review and categorization of production optimization throughout the additive manufacturing lifecycle in a cloud environment. It aims to provide a structured approach to identifying and addressing issues. Design/methodology/approach This paper systematically reviews 75 technical papers on cloud manufacturing, nesting, scheduling and postprocessing in additive manufacturing. This includes a detailed discussion of the key issues. Findings This paper introduces a production framework for the entire lifecycle of additive manufacturing in a cloud environment. This framework aids in problem identification and decision-making based on the process flow. It provides an integrated view from cloud to postprocessing, examining decision interdependencies and enhancing problem identification and organization. Originality/value To the best of the authors’ knowledge, this paper is the first to review the complete lifecycle of additive manufacturing, emphasizing the often-overlooked aspects of postprocessing and cloud manufacturing. It offers a comprehensive study of lifecycle optimization challenges and suggests ways to streamline the production process.

A multi-process parallel clustering algorithm for resource reconfiguration in cloud manufacturing

A multi-process parallel clustering algorithm for resource reconfiguration in cloud manufacturing

Ransomware Mitigation for Secure and Smart Cloud Manufacturing Using GeniLeaf Decision Tree with Load Balancing

Background: Cyber-attacks related to ransomware have increased significantly in cloud manufacturing industries over the last decade, causing considerable disruptions to organizations. This type of information may also include personal details, patent rights, bank account details, etc. This type of malware requires new and better mitigation methods. Objective: The primary objective of this study was to provide an algorithm to execute experiments using genetic algorithms for load balancing with decision trees and mitigate ransomware. Methods: Hybrid analysis and machine learning techniques were used in this study to identify ransomware. Since a wide range of samples impacted by ransomware share most of the characteristics, it may be possible to use this study to detect current and future malware variants in industries. Results: In patented industrial technology, ransomware mitigation plays a crucial role based on the analysis of various papers and patents-to-science references. In this study, a machine learning mitigation algorithm, GeniLeaf Decision Tree (GLDT), was applied to a featured dataset using a genetic algorithm with a decision tree Conclusion: Machine learning and load balancing are used to gain insight into ransomware behavior. By using the proposed approach to mitigate ransomware and spoofing patterns, a high level of accuracy is achieved. Using GeniLeaf Decision Trees to mitigate ransomware is a significant innovation.

A Hybrid Decision Support System for the Resource Allocation Problem in Cloud Manufacturing Platforms

A Hybrid Decision Support System for the Resource Allocation Problem in Cloud Manufacturing Platforms

Dynamic scheduling of workshop resource in cloud manufacturing environment

In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often limited to solving specific dynamic events. Thus, considering the actual workshop resource scheduling in a cloud manufacturing environment, this article examines the methods to address unexpected events including randomly arriving tasks, resource breakdown, as well as resource maintenance. Besides, a dynamic scheduling method based on the Game Theory, considering workshop capacity in cloud manufacturing, was developed. In the first place, the priority of workshop tasks was evaluated by Game Theory, and the optimal task processing sequence in the workshop was determined to maximize benefits. Secondly, to verify the dynamic regulation performance of the method, it was combined with the particle swarm optimization (PSO) algorithm considering multi-objective factors to obtain an ameliorated PSO algorithm addressing the challenge of resource optimization scheduling in a genuinely dynamic workshop environment. Finally, this method was tested through a case study, and the results demonstrate that it can achieve superior dynamic and static performance compared to alternative algorithms.

Game-Based Collaborative Scheduling With Fuzzy Uncertain Migration in Cloud Manufacturing

Game-Based Collaborative Scheduling With Fuzzy Uncertain Migration in Cloud Manufacturing

Cloud Edge Integrated Security Architecture of New Cloud Manufacturing System

Cloud Edge Integrated Security Architecture of New Cloud Manufacturing System

Modeling Approach of Cloud 4D Printing Service Composition Optimization Based on Non-Dominated Sorting Genetic Algorithm III

The manufacturing industry is currently experiencing a paradigm shift from traditional centralized systems to distributed, personalized, and cloud-based intelligent manufacturing ecosystems. The advent of 4-dimensional (4D) printing technology introduces dynamic characteristics to manufacturing design and functionality, necessitating the effective management of these emergent 4D printing services. This study aims to bridge the gap between the static nature of existing cloud manufacturing services and the dynamic requirements imposed by 4D printing technology. We propose a comprehensive multiobjective optimization model for cloud-based 4D printing service portfolios, incorporating the intricate complexities of 4D printing services and assessing the efficacy of the Non-Dominated Sorting Genetic Algorithm III (NSGA III) in optimizing these service portfolios to meet dynamic demands. In this research, the NSGA III algorithm is employed to develop a multiobjective optimization framework for 4D printing service portfolios, addressing critical issues such as service cost, time, quality, adaptability, and overall service optimization amidst fluctuating demand and service availability. The findings indicate that the NSGA III algorithm demonstrates superior performance in terms of generational distance (GD) and inverted generational distance (IGD), particularly excelling in convergence and diversity for high-dimensional optimization problems when compared to the comparison algorithms. The study concludes that the NSGA III algorithm exhibits significant potential in optimizing the orchestration of cloud-based 4D printing service portfolios, underscoring its effectiveness in managing the complexities associated with these services. This research provides valuable insights for the advancement of intelligent cloud-based 4D printing systems, paving the way for future developments in this field.

A resource scheduling method for cloud manufacturing services based on game theory and supported by blockchain technology

A resource scheduling method for cloud manufacturing services based on game theory and supported by blockchain technology

A QoS and sustainability-driven two-stage service composition method in cloud manufacturing: combining clustering and bi-objective optimization

A QoS and sustainability-driven two-stage service composition method in cloud manufacturing: combining clustering and bi-objective optimization

Fortifying Industry 4.0: Internet of Things Security in Cloud Manufacturing through Artificial Intelligence and Provenance Blockchain—A Thematic Literature Review

Cloud manufacturing allows multiple manufacturers to contribute their manufacturing facilities and assets for monitoring, operating, and controlling common processes of manufacturing and services controlled through cloud computing. The modern framework is driven by the seamless integration of technologies evolved under Industry 4.0. The entire digitalized manufacturing systems operate through the Internet, and hence, cybersecurity threats have become a problem area for manufacturing companies. The impacts can be very serious because cyber-attacks can penetrate operations carried out in the physical infrastructure, causing explosions, crashes, collisions, and other incidents. This research is a thematic literature review of the deterrence to such attacks by protecting IoT devices by employing provenance blockchain and artificial intelligence. The literature review was conducted on four themes: cloud manufacturing design, cybersecurity risks to the IoT, provenance blockchains for IoT security, and artificial intelligence for IoT security. These four themes of the literature review were critically analyzed to visualize a framework in which provenance blockchain and artificial intelligence can be integrated to offer a more effective solution for protecting IoT devices used in cloud manufacturing from cybersecurity threats. The findings of this study can provide an informative framework.

Subtasks scheduling of tasks with different structures in cloud manufacturing systems under maintenance policy and focusing on logistics, tardiness, and earliness aspects

Subtasks scheduling of tasks with different structures in cloud manufacturing systems under maintenance policy and focusing on logistics, tardiness, and earliness aspects

Priority-based two-phase method for hierarchical service composition allocation in cloud manufacturing

Manufacturing service composition (MSC) is an essential issue in cloud manufacturing, which streamlines complex manufacturing tasks into manageable subtasks and integrates distributed services to enhance task completion. Existing studies allocate services for subtasks with maximizing quality of service (QoS) simultaneously, assuming that all subtasks are of equal importance. However, different subtasks hold varied significance and priorities. One rational method is to prioritize the allocation of premium or scarce services to important subtasks. Therefore, this study proposes a two-phase, subtask priority-based approach for the hierarchical allocation of the MSC. The initial phase applies a multi-attribute decision-making method based on complex networks, the Enhanced Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS-EK), to assess subtask importance. The TOPSIS-EK method ascertains subtask importance, delineating the subtasks into Key Manufacturing Subtasks (KMTs) and Ordinary Manufacturing Subtasks (OMTs). The second phase uses bilevel optimization for the hierarchical allocation of the MSC to KMTs and OMTs, respectively. A hybrid Particle Swarm Optimization and Genetic Algorithm with Chaos-sequence and Inheritance (PSOGA-CI) is developed to solve the model. The proposed approach is validated with a case on the production of an airplane engine turbine rotor blade.

A hybrid MCDM model for service composition in cloud manufacturing using BWM-Critic-TOPSIS-TODIM

A hybrid MCDM model for service composition in cloud manufacturing using BWM-Critic-TOPSIS-TODIM

Collaborative optimization for scheduling manufacturing tasks and transport vehicles considering manufacturer’s time availability in cloud manufacturing

ABSTRACT As an advanced networked intelligent manufacturing model, cloud manufacturing (CMfg) effectively integrates available resources by strengthening the connection among various manufacturers to solve complex manufacturing problems. Due to the geographical dispersion of manufacturers and the cooperativity nature of the manufacturing process, logistic impacts are non-negligible in scheduling. This paper focuses on the collaborative scheduling of manufacturing and transportation services. To make full use of the manufacturer’s capacity without affecting his benefit, local tasks are scheduled with higher priority on a manufacturer while the service time for CMfg tasks is assumed to be fragmented and represented as a set of available time windows. Based on these considerations, a collaborative optimization model for CMfg on task and vehicle scheduling with manufacturing service windows is established, in which the completion time of all CMfg orders and the total idle traveling time of vehicles are minimized to ensure the efficiency of both manufacturing and logistic service. A dual-loop variable neighborhood search algorithm is designed to solve the problem. Comprehensive experiments are conducted, showing improvements of 2.41% to 29.57% in objective and fewer vehicles needed compared with some existing methods, which validate the efficacy of the proposed model and method.

An efficient cloud manufacturing service composition approach using deep reinforcement learning

Cloud Manufacturing (CMfg) revolutionizes manufacturing by providing resources as cloud-based services. The main challenge in CMfg is identifying the best combination of these services to meet customer needs efficiently. This paper explores the potential of Deep Reinforcement Learning (DRL), a technology with successful applications in fields such as healthcare and transportation, to tackle scheduling challenges in CMfg. A tailored DRL environment for CMfg is introduced, along with a novel DRL-based algorithm designed to optimize service composition in CMfg. Comparative evaluations against existing algorithms, both in the new DRL-based CMfg environment and a benchmark DRL environment, consistently showed the superior performance of the proposed algorithm. Additionally, the study analyzes different combinations of weight coefficients and probabilities of service failure within the new CMfg environment, revealing their significant impact on scheduling outcomes. These findings underscore both the robustness and efficiency of the proposed solution, showcasing DRL’s potential to significantly enhance CMfg operations.

How to optimize service-oriented cloud manufacturing

Within the context of Industry 4.0, service-oriented cloud manufacturing has emerged as a paradigm of significant interest, noted for enhancing manufacturing efficacy through integration, flexibility, and customization. The challenges of service selection and optimization, alongside transportation optimization, are pivotal for the effective implementation of service-oriented cloud manufacturing. This study explores an integrated problem that incorporates both sequential and parallel structures. To address this intricate issue, a binary-integer programming model is proposed, aiming to minimize the cumulative costs, including both manufacturing and transportation expenses. The validity and effectiveness of the proposed model are demonstrated through a real-world case study in mold manufacturing. Analysis of the experimental results provides managerial insights, which could inform the implementation and improvement of service-oriented cloud manufacturing strategies.

A DNA-Like blockchain model: implementing decentralized management, dynamic scheme adjustment, and data security for optimal service composition

ABSTRACT Cloud manufacturing (CM) promotes the development of small and medium-sized manufacturing enterprises (SMMEs) by integrating resources and collaboratively providing users with diversified services. However, the implementation of CM for SMMEs faces several challenges, such as the excessive centralization of enterprise management, poor dynamic adaptability of service composition, and insecurity of data information. To address these issues, this paper proposes a DNA-like blockchain model (DNABM) based on blockchain technology. The DNABM consists of a service information chain (SIC) and a manufacturing process chain (MPC). The SIC nodes (SIblocks) are formed based on the corresponding subtasks to record information about all manufacturing services that can complete the subtasks. The MPC nodes (MPblocks) are formed by selecting the most suitable services from the SIblocks. The DNABM has advantages of flexible service management, dynamically adaptable service compositions, and securely managed manufacturing enterprise data across various levels. Finally, taking the foundry industry as an example, demonstrating how the DNABM can promote the collaboration of SMMEs in CM. However, the actual implementation of the DNABM is still limited by the large number of heterogeneous tasks and services, the low utilization rate of process data, and the lack of coordination compatibility between services.