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A TQCS-based service selection and scheduling strategy in cloud manufacturing

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Abstract

Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

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Authors and Affiliations

  1. The State Key Laboratory of Mechanical Transmission, Chongqing University, No. 174 Shazhengjie, Shapingba District, Chongqing, 400044, China

    Yang Cao, Shilong Wang & Ling Kang

  2. Department of Mathematics, University of Waterloo, Waterloo, ON, Canada

    Yuan Gao

Authors
  1. Yang Cao
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  2. Shilong Wang
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  3. Ling Kang
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  4. Yuan Gao
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Correspondence to Yang Cao.

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Cao, Y., Wang, S., Kang, L. et al. A TQCS-based service selection and scheduling strategy in cloud manufacturing. Int J Adv Manuf Technol 82, 235–251 (2016). https://doi.org/10.1007/s00170-015-7350-5

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  • DOI: https://doi.org/10.1007/s00170-015-7350-5

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