Robust newsvendor problem with autoregressive demand
| Autor: | Alba V. Olivares-Nadal, Pepa Ramírez-Cobo, Emilio Carrizosa |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa, Universidad de Sevilla. FQM329: Optimizacion, Ministerio de Economía y Competitividad (MINECO). España, Junta de Andalucía |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
General Computer Science Computer science 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Newsvendor model Extended newsvendor model Robustness (computer science) 0502 economics and business Revenue Minimax 050207 economics 021103 operations research 05 social sciences Autocorrelation Robust optimization Demand forecasting Distribution-free newsboy problem Autoregressive model Autoregressive process Modeling and Simulation Convex optimization Uncertainty set Forecasting |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| Popis: | This paper explores the single-item newsvendor problem under a novel setting which combines temporal dependence and tractable robust optimization. First, the demand is modeled as a time series which follows an autoregressive process AR(p), p ? 1 . Second, a robust approach to maximize the worst-case revenue is proposed: a robust distribution-free autoregressive method for the newsvendor problem, which copes with non-stationary time series, is formulated. A closed-form expression for the optimal solution is found for p=1; for the remaining values of p, the problem is expressed as a nonlinear convex optimization program, to be solved numerically. The optimal solution under the robust method is compared with those obtained under three versions of the classic approach, in which either the demand distribution is unknown, and autocorrelation is neglected, or it is assumed to follow an AR(p) process with normal error terms. Numerical experiments show that our proposal usually outperforms the previous benchmarks, not only with regard to robustness, but also in terms of the average revenue. Extensions to multiperiod and multiproduct models are also discussed. HighlightsThe single period problem with uncertain and correlated demand values is explored.The demand forecast is estimated by a robust optimization method based on uncertainty sets.The proposed approach usually outperforms the competing methods.A model for the multi-product case with demands correlated along time and between products is proposed.An approach to deal with the multi-period case is outlined. |
| Databáze: | OpenAIRE |
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