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A mathematical model is employed in this study for optimizing the order quantity, lead time, ordering cost and number of deliveries in one production cycle.
The objective of this study is to minimize the total relevant cost by simultaneously optimizing the order quantity, lost sales rate and order processing cost.
Moreover, it is general, and can be easily extended for optimizing the order in which software components should be tested during component integration testing of a software system.
The principal goal of the research is to maximize the total profit of the chain by optimizing the order quantity of the supplier and the selling prices of the manufacturer and the wholesaler.
Moon and Choi (1998) and Hariga and Ben-Daya (1999) improved the model of Ouyang et al. (1996) by simultaneously optimizing the order quantity, the reorder point and lead-time.
Figure 4 provides an example of the methodology where a given timetable along a corridor with quadruple tracks (Scenario a) is first modified by compressing the timetable (Scenario b) and then further improved by optimizing the order of trains (Scenario c).
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Their objective is to simultaneously optimize the order quantity, ordering cost, back-order discount and lead time.
They analyze different setups regarding the supplier-retailer relationship and optimize the order quantity, reorder point and number of facings.
The objective is to simultaneously optimize the order quantity, ordering cost, back-order discount and lead time.
In Section 3, different model order selection criteria are comparatively discussed, and ARMA(2n,2n – 1) modeling procedures are adopted to optimize the order selection path.
The performance of the signal tracking loop depends on the dynamics of the received signal, and the goal of the filter design is to optimize the order and bandwidth to eliminate error signal effectively under a certain dynamic stress.
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