fixed interval inventory model
A multiecheloninventory modelwithfixedreplenishmentintervals, Graves, S. C. (1996). Management Science,42(1), 1-18. A new model for the study of multi-echelon inventory with stochastic demand was the aim of developing this paper. The model assumes that each site in the system functions based on an order-up-to policy and that time of delivery is deterministic and also that the demand for processes is both stochastic and independent. A set of test problems was used for this model for two-echelon systems, and the purpose was to understand the structure of good policy. Primarily, the findings are both the central warehouse upper echelon and retail sites – lower echelon.
Deterministicinventory modelwith two levels of storage, a linear trend in demand and afixedtime horizon, Kar, S., Bhunia, A. K., & Maiti, M. (2001). Computers & Operations Research,28(13), 1315-1331. An inventory model is developed for one item with two separate facilities for storage, both owned and a rented warehouse. Due to linearly dependent demand increasing, over a fixed finite time horizon, and hence the limited capacity of the existing storage the owned warehouse. The formulation of this model is based on the assumption that the rate of replenishment is infinite. In countries where state control is lower, it is normal to observe the demand for food grain. This demand for food grain been lowest at the time of harvest and increases just before the next harvest. This phenomenon is common in developing countries. This methodology of model development applies to inventory models of any product with a periodic production and whose demand increases linearly with time.
Operations planning in a supply chain system withfixed–intervaldeliveries of finished goods to multiple customers, Parija, G. R., & Sarker, B. R. (1999). IIE transactions,31(11), 1075-1082. This paper determines an economic batch size for a product at a manufacturing center which supplies finished products to several customers; this is with a fixed-quantity at a fixed time-interval to each customer. To economically manage the supply of chain logistics, the ordering of raw materials and the manufacturing of batch size for deliveries plays an important role. Also, manufacturing with fixed intervals of finished goods to several customers plays a key role. An optimal multi-ordering policy for procuring raw materials is developed. This is aimed at minimizing the total cost incurred due to raw materials and finished goods.
Aninventory modelfor arbitraryintervaland quantity distributions of demand, Beckmann, M. (1961). Management Science,8(1), 35-57. The following are assumptions under the problem of continuous inventory time which involves the demand process: (1) A distribution of interval length between successful demands; (2) an arbitrary distribution of the quantity demanded independent of any prior event and quantity demanded last. This is dependent on the time elapsed from the previous demand; (3) backlog of unfilled orders. Delivery time is fixed and considered cost are fixed costs proportional cost of purchase, shortage, and storage. Poisson, stuttering Poisson, geometric, Gamma, negative binomial and compound distributions are formulae calculated.
Optimal batch size and raw material ordering policy for a production system with afixed–interval, lumpy demand delivery system, Sarker, B. R., & Parija, G. R. (1996). European Journal of Operational Research,89(3), 593-608. An ordering policy for raw materials is developed by this paper to meet the supplies of a production facility. This facility supplies fixed quantities of already finished products at a fixed interval of time to external buyers. This model also develops an optimum multi-order policy to procure raw materials for a single manufacturing batch. To refine the optimal solution, an integer approximation is used. This model also runs a worst-case scenario. This scenario is analyzed to show the effect of the cost of set upon the total cost of inventory.
Integratedinventory modelfor deteriorating items under a multi-echelon supply chain environment, Rau, H., Wu, M. Y., & Wee, H. M. (2003). International journal of production economics,86(2), 155-168. The importance of supply chain management increases as the competition in the industrial environment becomes stiffened. This researchs objective is to create a multi-echelon inventory model for a decreasing item. The objective of this research is also to get a total cost from an integrated perspective within the communities of suppliers, manufacturer, and buyer. A numerical analysis is given to illustrate the model and a computer code developed as well. This paper finally illustrates the integrated approach strategy which results in the lowest point total cost in comparison independent decision approaches.
Mixtureinventory modelwith backorders and lost sales for variable lead time, Ouyang, L. Y., Yeh, N. C., & Wu, K. S. (1996). Journal of the Operational Research Society,47(6), 829-832. Recently, in their work, Ben-Daya and Raouf brought forth a continuous review inventory model. In this model, they made order quantity and lead time as decision variables where the shortages where ignored. Presumptuously, the shortages are allowed and also add the stockout cost thus extending the Ben-Daya and Raouf model. The effects of parameters were also included.
An optimal batch size for a production system operating under afixed-quantity, periodic delivery policy, Sarker, B. R., & Parija, G. R. (1994). Journal of the Operational Research Society,45(8), 891-900. In this paper, a manufacturing system that procures raw materials from suppliers processes them, and convert to finished materials is considered. This paper also creates a policy for ordering raw materials to meet the requirement of a production facility. This production facility must also produce finished products in demand by buyers at fixed interval points in time. A general cost model is first developed, putting into consideration both supplier of the raw materials and the purchase of finished products. This model determines an interval that contains the optimal solution.
An approximate periodicmodelforfixed-life perishable products in a two-echeloninventorydistribution system, Kanchanasuntorn, K., & Techanitisawad, A. (2006).International Journal of Production Economics,100(1), 101-115. This paper researches the effect of the perishability of product and retailers stockout on the total cost of the system, net profit, average inventory level in a two-echelon inventory distribution system and an average inventory. This paper also seeks to develop an approximate inventory model for these system performance measures. The perishability of the product is a major challenge for several industrial sectors, especially the agro-based industry. A significant improvement on the total cost of the system and net profit can be achieved simply modifying an existing model to incorporate fixed lifetime perishable and retailers lost sales policy.
A stochasticinventory modelwith trade credit, Gupta, D., & Wang, L. (2009). Manufacturing & Service Operations Management,11(1), 4-18. By routine, retailers get goods from suppliers on credit. Almost all stochastic models assume either charges that is time variant or time-invariant finance charges but not with the duration of the credit. This paper presents a discrete time model of the retailers operations with random demand. This is used to verify that there is no effect by the credit terms on the structure of the optimal policy. This model is followed by a continuous time mode that leads to an algorithm. The algorithm is used to find an optimal stock level. The suppliers problem is also modeled and the calculation for optimal credit parameters in numerical experiments done.
An economic order quantitymodelfor items with two levels of storage for a linear trend in demand, Goswami, A., & Chaudhuri, K. S. (1992). Journal of the Operational Research Society,43(2), 157-167. This paper studies a deterministic inventory that has two storages; own warehouse and rented warehouse. A linear-positive trend is considered in demand. The model is first formulated without allowing backlogging and then solved. Secondly, the reformulation of the model is done and solutions provided by the assumption that backlog and inventory shortages are allowed. Numerical examples further illustrate the results.