Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods
Uncertainty propagation of large-scale discrete supply chains can be prohibitive when numerous events occur during the simulated period and when discrete-event simulations (DES) are costly. We present a time-bucket method to approximate and accelerate the DES of supply chains. Its stochastic version...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | Operations Research Perspectives |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221471601930140X |
| _version_ | 1818389615487221760 |
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| author | Nai-Yuan Chiang Yiqing Lin Quan Long |
| author_facet | Nai-Yuan Chiang Yiqing Lin Quan Long |
| author_sort | Nai-Yuan Chiang |
| collection | DOAJ |
| description | Uncertainty propagation of large-scale discrete supply chains can be prohibitive when numerous events occur during the simulated period and when discrete-event simulations (DES) are costly. We present a time-bucket method to approximate and accelerate the DES of supply chains. Its stochastic version, which we call the L(logistic)-leap method, can be viewed as an extension of the leap methods (e.g., τ-leap [36]and D-leap [6] developed in the chemical engineering community for the acceleration of stochastic DES of chemical reactions). The L-leap method instantaneously updates the system state vector at discrete time points, and the production rates and policies of a supply chain are assumed to be stationary during each time bucket. We propose using the multilevel Monte Carlo (MLMC) method to efficiently propagate the uncertainties in a supply chain network, where the levels are naturally defined by the sizes of the time buckets of the simulations. We demonstrate the efficiency and accuracy of our methods using four numerical examples derived from a real-world manufacturing material flow application. In these examples, our multilevel L-leap approach can be faster than the standard Monte Carlo (MC) method by one or two orders of magnitude without compromising accuracy. |
| first_indexed | 2024-12-14T04:44:33Z |
| format | Article |
| id | doaj.art-de2ee1cd2ac240cb847ef61a831c252e |
| institution | Directory Open Access Journal |
| issn | 2214-7160 |
| language | English |
| last_indexed | 2024-12-14T04:44:33Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Operations Research Perspectives |
| spelling | doaj.art-de2ee1cd2ac240cb847ef61a831c252e2022-12-21T23:16:42ZengElsevierOperations Research Perspectives2214-71602020-01-017100144Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methodsNai-Yuan Chiang0Yiqing Lin1Quan Long2United Technologies Research Center, 411 Silver Lane, East Hartford, CT, USAUnited Technologies Research Center, 411 Silver Lane, East Hartford, CT, USACorresponding author.; United Technologies Research Center, 411 Silver Lane, East Hartford, CT, USAUncertainty propagation of large-scale discrete supply chains can be prohibitive when numerous events occur during the simulated period and when discrete-event simulations (DES) are costly. We present a time-bucket method to approximate and accelerate the DES of supply chains. Its stochastic version, which we call the L(logistic)-leap method, can be viewed as an extension of the leap methods (e.g., τ-leap [36]and D-leap [6] developed in the chemical engineering community for the acceleration of stochastic DES of chemical reactions). The L-leap method instantaneously updates the system state vector at discrete time points, and the production rates and policies of a supply chain are assumed to be stationary during each time bucket. We propose using the multilevel Monte Carlo (MLMC) method to efficiently propagate the uncertainties in a supply chain network, where the levels are naturally defined by the sizes of the time buckets of the simulations. We demonstrate the efficiency and accuracy of our methods using four numerical examples derived from a real-world manufacturing material flow application. In these examples, our multilevel L-leap approach can be faster than the standard Monte Carlo (MC) method by one or two orders of magnitude without compromising accuracy.http://www.sciencedirect.com/science/article/pii/S221471601930140XUncertainty modelingDiscrete event simulationMultilevel Monte CarloL-leapSupply chain |
| spellingShingle | Nai-Yuan Chiang Yiqing Lin Quan Long Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods Operations Research Perspectives Uncertainty modeling Discrete event simulation Multilevel Monte Carlo L-leap Supply chain |
| title | Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods |
| title_full | Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods |
| title_fullStr | Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods |
| title_full_unstemmed | Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods |
| title_short | Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods |
| title_sort | efficient propagation of uncertainties in manufacturing supply chains time buckets l leap and multilevel monte carlo methods |
| topic | Uncertainty modeling Discrete event simulation Multilevel Monte Carlo L-leap Supply chain |
| url | http://www.sciencedirect.com/science/article/pii/S221471601930140X |
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