•Shared Manufacturing (SharedMfg) is promoted by Industry 4.0, Servitization and Sharing Economy.•P2P-based SharedMfg extends the things or service sharing in both the scope and degree.•SharedMfg ...requires hybrid manufacturing integration and interoperability to operate the service.•Complex network analysis technologies support the dynamic scheduling in SharedMfg.
Sharing Economy promotes Shared Manufacturing (SharedMfg) that allows self-organized individuals to get involved in manufacturing activities through peer-to-peer (P2P) collaborations. It extends the scope and depth of resources sharing and enables society-based manufacturing integration. SharedMfg is complex and is still in its infancy. It demands encouraging research to clarify the concept and understand the key operations. In this paper, the SharedMfg is analysed along with two significant manufacturing evolutional trends, (i.e., Servitization and Industry 4.0). Based on this, to reduce the complexities in operations, the concept and definition of SharedMfg are presented on the basis of a service hierarchy that includes Product-Service System, Configuration-Service System and Resource-Service System. At the same time, this paper looks SharedMfg as an independent socialization manufacturing mode (e.g. compared with Cloud Manufacturing), and also, the benefit in contributing to the integration of the manufacturing and service sectors is revealed. However, apart from the complexity, high-degree dynamics and uncertainties related to service operations challenge the application of SharedMfg as well. To meet both requirements, based on the construction of a three-dimensional SharedMfg Service (SMS) modelling framework, a hybrid integration and interoperable SharedMfg architecture is proposed to ensure the provision of SMS in efficiency and effectiveness. Furthermore, a dynamic SMS scheduling method in support of the technologies of Complex Network Analysis is presented. A case study associated with the networked fabrication of an assistive glove is applied by implementing a prototype system to validate the proposed frameworks and methods. Finally, an experiment for evaluating the efficiency of service matching is carried out to prove the SharedMfg that can provide better performance than Cloud Manufacturing and Social Manufacturing.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A digital twin-driven service model of shared manufacturing resource is built.•The credit of resource providers is considered in the resource allocation model.•A distributed optimization method is ...used to keep providers’ decision autonomy.•Results of a case study showed the effectiveness of the proposed method.
The sharing economy has been recognized a mutually beneficial economic mode. Deriving from the concept of sharing economy, shared manufacturing was proposed under the support of advanced information and manufacturing technologies. As a core part of implementing shared manufacturing, manufacturing resource allocation aims to coordinate cross-organizational resources to provide on-demand services for personalized manufacturing requirements. However, some challenges still hinder effective and efficient resource allocation in shared manufacturing. Traditional centralized optimization methods with only one decision model are difficult to maintain autonomous decision rights of resource providers. Thus, they could hardly adapt to the situation of cross-organizational resource coordination. In addition, the credit of resource providers is rarely considered in the resource allocation process, which is unfavorable for promoting more reliable trades in shared manufacturing. To address these issues, this study proposes an integrated architecture to promote the resource allocation in shared manufacturing. A digital twin-driven service model is built to perform the seamless monitoring and control of shared manufacturing resources. The resource allocation model is constructed based on the consideration of the credit of resource providers. To keep the decision autonomy of resource providers, augment Lagrangian coordination is adopted to analyze the constructed resource allocation model. A case study is further employed to validate the effectiveness and efficiency of the proposed method in performing the resource allocation in shared manufacturing.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
Shared Manufacturing is a new mode of social manufacturing based on the principles of a sharing economy. This paper presents a scalable framework for blockchain-based Shared Manufacturing that ...preserves the transparency and immutability characteristics of transaction records, which is critical to building trust between entities in blockchain-based systems. We define a blockchain-based protocol for the service execution according to the design principles of the sharing economy. We present a scalable integration of blockchain technology into the concept of Shared Manufacturing by employing cross-chain solutions. We discuss existing cross chain technologies regarding the requirements of Shared Manufacturing and propose hybrid approach. We compare implementations of the proposed framework on two different blockchain networks: Ethereum public network and Xdai sidechain network. We conduct user-oriented test to explore the performance (cost and time) of the implementations in realistic situations in order to justify the use of the sidechain technology. Results indicate that the implementation on the sidechains provides greater scalability than the implementation on the public blockchain network.
•Shared Manufacturing opens up manufacturing to unused local resources•Blockchain technology enables trusted environment between entities•Using cross-chain technology increases scalability of the blockchain-based Shared Manufacturing.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Blockchain-based shared manufacturing (BSM) framework enables a P2P-based resource sharing mode.•BSM framework is a solution to the trust problem in shared manufacturing.•Resource operation ...blockchain (ROB) facilitates P2P-based resource sharing based on the smart contract network (SCN).•Proof-of-Participation (PoP) and SCN assure the stability and sustainability of ROB.
Shared Manufacturing (SharedMfg), a Peer-to-Peer (P2P)-based resource sharing paradigm boosted by the wide-spread of sharing economy, servitization and Internet of things, tends to massively extend the scope of resource sharing in both vertical and horizontal directions, and as a consequence, it amplifies a credibility gap in the manufacturing area. To respond to this problem, and meanwhile, promoting the SharedMfg, blockchain is attempted to integrate into the SharedMfg. Hence, this paper proposes the Blockchain-based SharedMfg (BSM) framework in support of the application of Cyber Physical Systems (CPS). At the same time, Resource Operation Blockchain (ROB) is constructed for the core operation of BSM framework, which carries out on the basis of a consensus mechanism (i.e., Proof-of-Participation) and a Smart Contract Network (SCN), to facilitate the P2P-based resource sharing paradigm. A prototype system is implemented by the Ethereum framework together with discussions to validate the feasibility of ROB. In brief, BSM framework complementarily combines the blockchain and SharedMfg, beneficial to promote both modes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
With the rapid development of information technologies, shared manufacturing is proposed to meet the prevailing tendency of servitization and digitalisation in the industry. As the crucial section ...for performing shared manufacturing, resource monitoring and maintenance aim to detect production exceptions and ensure normal task execution. Existing research mainly uses a resource-centric strategy to acquire production-related data and make decisions for the management of shared resources. The experience data from the users/customers of the shared resources or its similar resources is rarely acquired actively in a cost-effective manner. However, the user/customer's experience data may contain essential knowledge that can be used for effective production performance identification and maintenance. To fill this gap, a hybrid sensing-based approach is proposed to perform the monitoring and maintenance of the shared manufacturing resources. It leverages both the sensor-sensed production data and user/customer-generated data for value creation in a cost-effective manner. Based on the acquired hybrid data, a service model is constructed to achieve the monitoring of the shared manufacturing resource, and a knowledge-based mechanism is designed to perform efficient maintenance. A case study is further presented to verify the effectiveness of the proposed approach.
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BFBNIB, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Shared factory (SharedF), succeed from the sharing economy, is defined as a kind of fenceless plant which enables high-efficiency self-organization and sustainability of the shared manufacturing ...resources. This paper proposes a brand-new Enhanced Self-organizing Agent (ESA) in the context of sustainable SharedF, which helps the shared manufacturing resources realize cross-sharing with self-organizing communication and negotiation mechanisms. ESA is a kind of dynamic software and hardware integration, which can sense and pre-process the real-time external social contexts, have the ability of self-decision makings and self-learning, and socialize with other ESAs. Then, the definition, components, operational logic, classification, evolution, and self-organizing interactions of ESAs are proposed. After that, by illustrating a 3D-printing SharedF prototype, it is proved that the ESAs in SharedF is an economical and efficient organizational architecture, which can gain the utilization of the shared manufacturing resources and fit the idea of sustainable manufacturing. This work gives a reliable and flexible way to reconfigure the idle or excess shared resources that minimize negative environmental impacts while conserving energy and natural resources.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
7.
Blockchain-based Shared Additive Manufacturing Lupi, Francesco; Cimino, Mario G.C.A.; Berlec, Tomaž ...
Computers & industrial engineering,
September 2023, 2023-09-00, Volume:
183
Journal Article
Peer reviewed
Open access
•Resource pooling increases utilization in presence of unexpected circumstances.•Ontology of the problem domain for protocol developed in process language.•Smart contracts and blockchain technology ...enable advanced manufacturing.•Increased resilience of shared manufacturing system is proven by simulation.•Python code developed for simulation publicly available.
Today, globalized markets require more resilient and agile manufacturing systems, as well as customized and virtualized features. Classical self-standing manufacturing systems are evolving into collaborative networks such as Cloud Manufacturing (based on centralized knowledge and distributed resources) or Shared Manufacturing (based on fully decentralized knowledge and distributed resources) as a solution to ensure business continuity under normal as well as special circumstances. Additive Manufacturing (AM), one of the enablers of Industry 4.0 (I4.0), is a promising technology for innovative production models due to its inherent distributed capabilities, digital nature, and product customization ability. To increase the adaptivity of distributed resources using AM technology, this paper proposes a mechanism for sharing workload and resources under unexpected behaviours in the supply chain. Smart contracts and blockchain technology in this concept are used to provide decentralized, transparent, and trusted operation of such systems, which provide more resilience to disruptive factors. In this paper, the proposed Blockchain-based Shared Additive Manufacturing (BBSAM) protocol, ontology, and workflow for AM capacity pooling are discussed and analysed under special conditions such as anomalous demand. Discrete-time Python simulation on a real Italian AM market dataset, also provided, is available on GitHub.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Shared manufacturing integrates resources into a set with defined service types.•The matching of supply and demand is completed by abstractly representing types.•Realizing joint scheduling from ...supply and demand matching to workshop production.•Hybrid estimation of distribution algorithm and Tabu search was proposed.•Multi-population strategy and non-dominated solutions memory mechanism was designed.
Shared Manufacturing (sharedMfg) is a peer-to-peer (P2P) paradigm for sharing manufacturing resources, derived from the sharing economy. In sharedMfg, manufacturing service resources are integrated into a large set with defined service types. Based on its resource organization structure, we build a model for the shared manufacturing-based distributed flexible job shop scheduling problem (SM-DFJSP) with supply-demand matching. The goal is to minimize both the total cost and the makespan. The SM-DFJSP model enables the scheduling of jobs requiring different manufacturing services across distributed, heterogeneous, and flexible service resource units (SRUs) with diverse manufacturing functions. To solve the SM-DFJSP, we propose a hybrid estimation of distribution algorithm and Tabu search (EDA-TS), including EDA and TS components. Additionally, a multi-populations strategy and non-dominated solutions memory mechanism are designed to improve the exploration ability of the algorithm. Within the EDA component, three probability distribution models and some dispatching rules are designed to generate a new population. In the TS component, three neighborhood search structures are built that adopt hybrid short and long memory tabu strategy. Finally, comparison and ablation experiments on 25 instances demonstrate the superior performance of the EDA-TS algorithm in solving the SM-DFJSP, highlighting the effectiveness of the multi-population strategy and non-dominated solutions memory mechanism.
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In this paper, we aim at the cross-period matching problem on a third-party shared manufacturing platform (TSMP) that provides matching services for capacity demanders (CDs) and capacity suppliers ...(CSs). Considering the impact of delay in meeting demand, we formulate a bi-level multi-objective optimization model (BMOM) with the objective functions of maximizing the manufacturers’ matching rate and the TSMP’s commission revenue, in which the lower-level model optimizes the matching strategy for the current period, and the upper-level model matches the cross-period demand (i.e., the demand with delay). Then, we propose a method that combines the ε-constraint and a three-stage solution algorithm to solve the BMOM, and the performance of the proposed method is verified by comparing it with the optimization solver CPLEX in numerical experiments. The results of numerical experiments show that the method proposed in the paper has advantages in computational effectiveness and efficiency and can solve the current-period and cross-period joint matching problem of the TSMP, which provides a reference method for TSMPs to optimize the matching strategies.
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