Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285104624> ?p ?o ?g. }
- W4285104624 endingPage "758" @default.
- W4285104624 startingPage "748" @default.
- W4285104624 abstract "There are multiple warehouses in a multiechelon inventory system, and the size of the state space increases exponentially with the number of warehouses. Therefore, the curse of dimensionality becomes unavoidable when performing steady-state analysis. Most existing studies calculate the inventory cost or supply chain reliability based on specific assumptions. For example, it often assumes that the lead time is either zero or an integral multiple of the review period, and that each warehouse adopts a base-stock policy. This article considers a more practical and prevalent situation where the lead time is less than a review period, and a more general (s, S) strategy is adopted. The curse of dimensionality during steady-state analysis is alleviated by decomposing transition probabilities. Then, the cost and supply chain reliability are derived from steady-state distributions. Finally, a case study involving spare part inventory of wind turbines is considered. Nondominated inventory strategies are obtained using the particle swarm optimization method to strike a balance between costs for the wind turbine manufacturer and wind farm owners." @default.
- W4285104624 created "2022-07-14" @default.
- W4285104624 creator A5031978434 @default.
- W4285104624 creator A5040445878 @default.
- W4285104624 creator A5043293214 @default.
- W4285104624 creator A5055984065 @default.
- W4285104624 date "2023-06-01" @default.
- W4285104624 modified "2023-10-10" @default.
- W4285104624 title "Reliability-Driven Multiechelon Inventory Optimization With Applications to Service Spare Parts for Wind Turbines" @default.
- W4285104624 cites W1724228554 @default.
- W4285104624 cites W1804504554 @default.
- W4285104624 cites W1971993345 @default.
- W4285104624 cites W1985832343 @default.
- W4285104624 cites W1994565713 @default.
- W4285104624 cites W2016728818 @default.
- W4285104624 cites W2059627072 @default.
- W4285104624 cites W2061100365 @default.
- W4285104624 cites W2076366951 @default.
- W4285104624 cites W2099897308 @default.
- W4285104624 cites W2139285064 @default.
- W4285104624 cites W2154472672 @default.
- W4285104624 cites W2155561637 @default.
- W4285104624 cites W2160721185 @default.
- W4285104624 cites W2461358474 @default.
- W4285104624 cites W2751425504 @default.
- W4285104624 cites W2766051214 @default.
- W4285104624 cites W2788284535 @default.
- W4285104624 cites W2801289319 @default.
- W4285104624 cites W2808906585 @default.
- W4285104624 cites W2889740273 @default.
- W4285104624 cites W2898141422 @default.
- W4285104624 cites W2898722869 @default.
- W4285104624 cites W2907240515 @default.
- W4285104624 cites W2910887233 @default.
- W4285104624 cites W2911325094 @default.
- W4285104624 cites W2935754432 @default.
- W4285104624 cites W2937314190 @default.
- W4285104624 cites W2955038039 @default.
- W4285104624 cites W2971244231 @default.
- W4285104624 cites W2996700744 @default.
- W4285104624 cites W3024195987 @default.
- W4285104624 cites W3029866640 @default.
- W4285104624 cites W3159599754 @default.
- W4285104624 cites W3199913502 @default.
- W4285104624 cites W3204199614 @default.
- W4285104624 doi "https://doi.org/10.1109/tr.2022.3178596" @default.
- W4285104624 hasPublicationYear "2023" @default.
- W4285104624 type Work @default.
- W4285104624 citedByCount "1" @default.
- W4285104624 countsByYear W42851046242022 @default.
- W4285104624 crossrefType "journal-article" @default.
- W4285104624 hasAuthorship W4285104624A5031978434 @default.
- W4285104624 hasAuthorship W4285104624A5040445878 @default.
- W4285104624 hasAuthorship W4285104624A5043293214 @default.
- W4285104624 hasAuthorship W4285104624A5055984065 @default.
- W4285104624 hasConcept C108713360 @default.
- W4285104624 hasConcept C111030470 @default.
- W4285104624 hasConcept C119599485 @default.
- W4285104624 hasConcept C119857082 @default.
- W4285104624 hasConcept C121332964 @default.
- W4285104624 hasConcept C126255220 @default.
- W4285104624 hasConcept C127413603 @default.
- W4285104624 hasConcept C144133560 @default.
- W4285104624 hasConcept C162324750 @default.
- W4285104624 hasConcept C162853370 @default.
- W4285104624 hasConcept C163258240 @default.
- W4285104624 hasConcept C187736073 @default.
- W4285104624 hasConcept C194648553 @default.
- W4285104624 hasConcept C200601418 @default.
- W4285104624 hasConcept C21547014 @default.
- W4285104624 hasConcept C2776613951 @default.
- W4285104624 hasConcept C2778449969 @default.
- W4285104624 hasConcept C33923547 @default.
- W4285104624 hasConcept C41008148 @default.
- W4285104624 hasConcept C42475967 @default.
- W4285104624 hasConcept C43214815 @default.
- W4285104624 hasConcept C50601946 @default.
- W4285104624 hasConcept C62520636 @default.
- W4285104624 hasConcept C78519656 @default.
- W4285104624 hasConcept C78600449 @default.
- W4285104624 hasConcept C85617194 @default.
- W4285104624 hasConceptScore W4285104624C108713360 @default.
- W4285104624 hasConceptScore W4285104624C111030470 @default.
- W4285104624 hasConceptScore W4285104624C119599485 @default.
- W4285104624 hasConceptScore W4285104624C119857082 @default.
- W4285104624 hasConceptScore W4285104624C121332964 @default.
- W4285104624 hasConceptScore W4285104624C126255220 @default.
- W4285104624 hasConceptScore W4285104624C127413603 @default.
- W4285104624 hasConceptScore W4285104624C144133560 @default.
- W4285104624 hasConceptScore W4285104624C162324750 @default.
- W4285104624 hasConceptScore W4285104624C162853370 @default.
- W4285104624 hasConceptScore W4285104624C163258240 @default.
- W4285104624 hasConceptScore W4285104624C187736073 @default.
- W4285104624 hasConceptScore W4285104624C194648553 @default.
- W4285104624 hasConceptScore W4285104624C200601418 @default.
- W4285104624 hasConceptScore W4285104624C21547014 @default.
- W4285104624 hasConceptScore W4285104624C2776613951 @default.
- W4285104624 hasConceptScore W4285104624C2778449969 @default.