Optimal service selection and composition for service-oriented manufacturing network

International Journal of Computer Integrated Manufacturing (Impact Factor: 1.01). 05/2011; 24(5):416-430. DOI: 10.1080/0951192X.2010.511657
Source: DBLP


The management of services is the kernel content of service-oriented manufacturing. However, it is difficult to realise the integration and optimisation of services in an open environment, which contains large amounts randomicity and uncertainty. The key problem is how to realise the optimal service selection and composition. In this article, the comprehensive performance evaluation metrics for service-oriented manufacturing network is proposed, which combines the key performance indicators of services in business, service and implementation level. The performance evaluation model is brought forward to analyse the local and global performance. An uncertainty and genetic algorithm-based method is developed to realise the optimal service selection and composition in effective and efficient way.

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    • "Complicated service composition modes are usually translated into simple equivalent sequence ones to get the overall QoS[5][6], but such methods are static and do not consider the factors from the implementation aspect. More comprehensive performance evaluation metrics of SS have been proposed by synthetically considering key performance indicators from business, service and implementation aspects[7]. However, these methods cannot deal with inevitable, unexpected disruptions during the service execution, which may turn the original plan obsolete[8]. "
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    ABSTRACT: Cloud manufacturing can manage mass manufacturing resources and capabilities, and provide them as services via the Internet. Undoubtedly, multiple manufacturing clouds (MCs) will have extremely abundant services in terms of function, price, reliability, location, etc. Selecting and using services from multiple MCs is a natural evolution in the best interests of service consumers. On the other side, various uncertainties in today’s highly-dynamic business environment can easily disrupt manufacturing activities, rendering original schedules obsolete. However, little work has been done to take advantages of abundant services from MCs and to effectively deal with uncertainties. To address this requirement, we propose a dynamic service selection (SS) method across multiple MCs. The proposed method uses IoT’s real-time sensing ability on service execution, Big-Data’s knowledge extraction ability on services in MCs, and event-driven dynamic SS optimization to deal with disturbances from users and service market and to continuously adjust SS to be more effective and efficient.
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    • ". Functional diagram of a service unit [12] SOA is based on information communication technologies and mainly uses internet-based technologies to support the integration of systems that offer and use services [18]. Huang et al. [19] propose a product service system in which various information services support production along a supply chain. Furthermore, in [10], this approach is extended to material-related services, and called manufacturing oriented services (MOSs). "
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    ABSTRACT: Strong operations support is one of the key requirements for competitive success of modern manufacturing organisations. An important aspect of operations support is Statistical Process Control (SPC); the use of statistical methods for monitoring and control of manufacturing processes and products. However, implementation of SPC requires a certain amount of statistical knowledge and understanding. Although this is not an issue for big companies (e.g. in automotive sector), smaller companies are unable to provide the required knowledge in-house. In this paper, a service-driven approach for SPC is proposed, in which SPC is outsourced through the use of modern information and communication technologies, such as web services. This Statistical Process Control as a Service approach is illustrated and discussed through an industrial case study.
    Full-text · Article · Dec 2013
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    • "Kameshwaran, Viswanadham and Desai (2009) propose a decision framework that enables the manufacturing firms to decide upon the product-service bundling and pricing, where after-sales repair and maintenance services are considered. Huang et al. (2011) present a comprehensive performance evaluation method for the SOM system and develop an efficient algorithm to find out the optimal solution for service selection and composition. However, all the above papers devote to the specific case studies or the simulation of some specific SOM systems, but not focus on the analytical analysis of a more general model. "
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    ABSTRACT: In decades, many traditional manufacturing firms are making the transition from products manufacturers to the providers of product-service systems (PSSs). The new business mode to support this transition is termed service-oriented manufacturing (SOM) paradigm by researchers and practitioners. In this paper, we address the problem of optimal control for a SOM system in which one factory manufactures products in the first stage and then one service centre realises PSSs by adding some product-based services on the products in the second stage. There are two-class external demands: demands for products and PSSs. The PSSs orders have higher priority and are fully accepted whereas the product orders can either be accepted or rejected. The optimal dynamic admission control of the product orders and the optimal dynamic production control of products are examined simultaneously. We first model the system in the context of a simple production-inventory-queue system and formulate the optimal integrated control problem as a continuous Markov decision process in which the objective is to maximise the profit of the firm. Then we characterise the optimal integrated dynamic control by two state-dependent threshold functions. The monotonicity of two optimal control curves verifies that, to achieve an effective management for the SOM system, the controls of manufacturing subsystem and service subsystem in the SOM system must be coo. We also illustrate the impacts of the revenues of both product orders and PSS orders on the optimal policy numerically. It is interesting that the optimal control policy is independent of the revenue of PSS order.
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