Open AccessSpare parts supply network optimization with uncertain distributed lead times and demands
Author(s) -
Yadong Wang,
Qin Shi,
Lei Li,
Feng Li,
Wei Xia
Publication year - 2021
Publication title -
revista internacional de métodos numéricos para cálculo y diseño en ingeniería
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 9
eISSN - 1886-158X
pISSN - 0213-1315
DOI - 10.23967/j.rimni.2020.11.002
Subject(s) - spare part , robustness (evolution) , ambiguity , computer science , mathematical optimization , robust optimization , moment (physics) , economic shortage , constraint (computer aided design) , sensitivity (control systems) , reliability engineering , mathematics , engineering , operations management , biochemistry , chemistry , physics , linguistics , philosophy , geometry , classical mechanics , government (linguistics) , gene , programming language , electronic engineering
In the spare parts supply network, there are many uncertain factories, such as unpredictable demands and changeable lead times. The spare parts shortage caused by those uncertainties may lead to severe losses. To solve the uncertainty of supply network, a determined optimization model is developed and then reformulated as a robust counterpart. In the robust model, it is only necessary to know the moment information of the uncertain parameters rather than the true probability distribution. The solution obtained by the robust model can satisfy the constraints in the worst-case, that is, feasible for any probability distribution within the moment based ambiguity set. Two moment based robust models are studied in this work. The result of the experiment indicates that the robustness of the robust model is stronger than that of the determined model and chance constraint model, and the effect of safety tolerance on the robustness is revealed by sensitivity analysis. Finally, the second order moment model is verified to be superior to the first order moment model in spare parts supply network optimization.