In this paper, a Back Propagation-Artificial Neural Network (BPANN) has been adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. The conventional approach to determine the parts requirements is the Economic Order Quantity (EOQ) method. The ability of neural models to learn, particularly their capability of handling large amounts of data simult...

In this paper we consider a single item, discrete time, lot sizing situation where demand is random and its parameters (e.g., mean and standard deviation) can change with time. For the appealing criterion of minimizing expected total relevant costs per unit time until the moment of the next replenishment we develop two heuristic ways of selecting an appropriate augmentation quantity beyond the ...

    inventory model,   backorder   buyer ,   vendor,   lot for lot policy In this paper, an inventory model for two-stage supply chain is investigated. A supply chain with single vendor and single buyer is considered. We assume that shortage as a backorder is allowed for the buyer and the vendor makes the production set up every time the buyer places an order and supplies on a lot for lot basis...

Problem definition: In theory, all coordinating contracts are equivalent; however, the minimum order quantity (MOQ) contract is observed to be more popular in practice. We seek understand whether decision makers as suppliers can perform better with MOQ and, if so, why. also study indeed preferred when subjects allowed choose among contracts. Academic/practical relevance: The behavioral operatio...

Supplier selection and order allocation decisions are the main parties of a supply chain network which has high impact on economic performance this network. This study using an Economic Order Quantity (EOQ) concept proposes optimization model for integrated supplier problem where lot sizing, discounts, disruptions contributed among first studies in research area. To address uncertainty, scenari...