供應(yīng)鏈下的多級(jí)存貨管理外文文獻(xiàn)-在線瀏覽
2024-11-10 15:54本頁(yè)面
【正文】 entory control policy for multiechelon system with stochastic demand has been a widely researched area. More recent papers have been covered by Silver and Pyke. The advantage of centralized planning, available in periodic review policies, can be obtained in continuous review policies, by defining the reorder levels of different stages, in terms of echelon stock rather than installation stock. Rau et al. , Diks and de Kok , Dong and Lee ,Mitra and Chatterjee , Hariga , Chen ,Axsater and Zhang , Nozick and Turnquist ,and So and Zheng use a mathematic modeling technique in their studies to manage multiechelon inventory in SCs. Diks and de Kok’s study considers a divergent multiechelon inventory system, such as a distribution system or a production system, and assumes that the order arrives after a fixed lead time. Hariga, presents a stochastic model for a singleperiod production system posed of several assembly/processing and storage facilities in series. Chen, Axsater and Zhang, and Nozick and Turnquist consider a twostage inventory system in their papers. Axsater and Zhang and Nozickand Turnquist assume that the retailers face stationary and independent Poisson demand. Mitra and Chatterjee examine De Bodt and Graves’ model (1985), which they developed in their paper’ Continuousreview policies for a multiechelon inventory problem with stochastic demand’, for fastmoving items from the implementation point of view. The proposed modification of the model can be extended to multistage serial and two echelon assembly systems. In Rau et al.’s model, shortage is not allowed, lead time is assumed to be negligible, and demand rate and production rate is deterministic and constant. So and Zheng used an analytical model to analyze two important factors that can contribute to the high degree of orderquantity variability experienced by semiconductor manufacturers: supplier’s lead time and forecast demand updating. They assume that the external demands faced by there tailor are correlated between two successive time periods and that the retailer uses the latest demand information to update its future demand forecasts. Furthermore, they assume that the supplier’s delivery lead times are variable and are affected by the retailer’s order quantities. Dong and Lee’s paper revisits the serial multiechelon inventory system of Clark and Scarf and develops three key results. First, they provide a simple lowerbound approximation to the optimal echelon inventory levels and an upper bound to the total system cost for the basic model of Clark and Scarf. Second, they show that the structure of the optimal stocking policy of Clark and Scarf holds under timecorrelated demand processing using a Martingale model of forecast evolution. Third, they extend the approximation to the timecorrelated demand process and study, in particular for an autoregressive demand model, the impact of lead times, and autocorrelation on the performance of the serial inventory system. After reviewing the literature about multiechelon inventory management in SCs using mathematic modeling technique, it can be said that, in summary, these papers consider two, three, or Nechelon systems with stochastic or deterministic demand. They assume lead
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