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Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithm

  • S.I. : India Intl. Congress on Computational Intelligence 2017
  • Published:
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Abstract

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.

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(adapted from Akbaripour et al. [24])

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Acknowledgements

The authors express their gratitude to the editor and anonymous reviewers of Neural Computing and Applications journal for their valuable comments toward improving the early version of the paper. We also express our deepest appreciation and gratitude to Dr. Ellips Masehian for his contributions and assistance.

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Authors and Affiliations

  1. Industrial Engineering Department, Sharif University of Technology, Tehran, Iran

    Hossein Akbaripour & Mahmoud Houshmand

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  1. Hossein Akbaripour
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  2. Mahmoud Houshmand
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Correspondence to Hossein Akbaripour.

Appendix

Appendix

See Table 12.

Table 12 Inter-city geographical distances (km) between the US cities in the CAB dataset
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Akbaripour, H., Houshmand, M. Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithm. Neural Comput & Applic 32, 10873–10894 (2020). https://doi.org/10.1007/s00521-018-3721-9

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