Abstract
Service composition and optimal selection problem (SCOSP) in cloud manufacturing are crucial tasks. However, due to insufficient historical data or accurate forecasting methods, making unbiased decisions for this problem often faces challenges in addressing uncertainties. In this paper, we address the problem of service composition and optimal selection within the framework of adaptive distributionally robust optimization. In particular, we design an event-dependent ambiguity set associated with manufacturing capability in different events, which combines the 1-Wasserstein metric with the box support set to effectively capture the distributional ambiguous information for each event. To solve SCOSP exactly, we reformulate adaptive distributionally robust SCOSP into the mixed integer programming model. In the end, we conduct a series of numerical experiments to assess the value of incorporating event-dependent distributional information and to evaluate the robustness of the model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu YK, Wang LH, Wang XV, Xu X, Zhang L (2019) Scheduling in cloud manufacturing: state-of-the-art and research challenges. Int J Prod Res 57(15–16):4854–4879
Ren LF, Wang WJ, Xu H (2020) A reinforcement learning method for constraint-satisfied services composition. IEEE Trans Serv Comput 13(5):786–800
Wang YK, Wang SL, Yang B, Gao B, Wang SB (2020) An effective adaptive adjustment method for service composition exception handling in cloud manufacturing. J Intell Manuf 33:735–751
Yang B, Wang SL, Li S, Bi FY (2023) Digital thread-driven proactive and reactive service composition for cloud manufacturing. IEEE Trans Industr Inf 19(3):2952–2962
Yang B, Wang SL, Li S, Jin TG (2022) A robust service composition and optimal selection method for cloud manufacturing. Int J Prod Res 60(4):1134–1152
Li BD, Yang Y, Su JF, Liang ZC, Wang S (2020) Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources. J Intell Manuf 8(31):2033–2047
Zhang WY, Ding JP, Wang Y, Zhang S, Xiong ZY (2019) Multi-perspective collaborative scheduling using extended genetic algorithm with interval-valued intuitionistic fuzzy entropy weight method. J Manuf Syst 53:249–260
Zheng H, Feng YX, Tan JR (2016) A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system. Int J Adv Manuf Technol 84:371–379
Li J, Huang YZ, Li YF, Wang SM (2022) Redundancy allocation under state-dependent distributional uncertainty of component lifetimes. Product Operat Manage. https://doi.org/10.1111/poms.13906
Chen Z, Sim M, Xiong P (2020) Robust stochastic optimization made easy with RSOME. Manage Sci 66(8):3329–3339
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Luo, Z., Yin, Y., Wang, D. (2024). Adaptive Distributionally Robust Service Composition and Optimal Selection Problem in Cloud Manufacturing. In: Chien, CF., Dou, R., Luo, L. (eds) Proceedings of Industrial Engineering and Management. SMILE 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-97-0194-0_32
Download citation
DOI: https://doi.org/10.1007/978-981-97-0194-0_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0193-3
Online ISBN: 978-981-97-0194-0
eBook Packages: EngineeringEngineering (R0)