Low-priority Demands Research Articles

Economic production quantity with inventory rationing for a decaying item

In this paper, we develop an economic production quantity (EPQ) model with inventory rationing for a decaying item under deterministic conditions. Two demand classes are considered, and inventory rationing is implemented by introducing a critical level: when stock is below the critical level, low-priority demand is rejected. The cost model is developed considering two situations, depending on whether the critical level is in the inventory build-up phase or in the inventory depletion phase. We then formulate the optimization problem and propose a solution procedure. Numerical experiments are finally carried out to demonstrate the benefits of implementing inventory rationing.

The design of the vaccine supply network under uncertain condition

Purpose This study aims to design a proper supply chain network for the vaccine industry in Iran, which considers several features such as uncertainties in demands and cost, perishability of vaccines, wastages in storage, limited capacity and different priorities for demands. Design/methodology/approach This study presents a mixed-integer linear programming (MILP) model and using a robust counterpart approach for coping with uncertainties of model. Findings The presented robust model in comparison with the deterministic model has a better performance and is more reliable for network design of vaccine supply chain. Originality/value This study considers uncertainty in the network design of vaccine supply chain for the first time in the vaccine context It presents an MILP model where strategic decisions for each echelon and tactical decisions among different echelons of supply chain are determined. Further, it models the difference between high- and low-priority demands for vaccine.

Markov Models of Systems with Demands of Two Types and Different Restocking Policies

Markov models of queuing-inventory systems with demands of two types are proposed. In these systems, two restocking policies are used: policies with fixed and variable demand size. High-priority demands have no access constraints while low-priority demands are accepted if the total number of demands in the system is less than a given threshold. Methods are developed to calculate performance measures of the systems under study and problems of their optimization are solved. Results of the numerical experiments are shown.

Reservation and allocation policies for influenza vaccines

This research investigates the impact of alternative allocation mechanisms that can be employed in the context of vaccine inventory rationing. Available vaccine inventory can be allocated to arrivals from high priority (target groups such as healthcare professionals) and low priority (non-target groups) demand classes using Partitioned Allocation (PA), Standard Nesting (SN), and Theft Nesting (TN). In any one of the mechanisms, a part of the available inventory is reserved for the exclusive use of the high priority demand class. They differ, however, in how the unreserved portion of the inventory is utilized: Under PA, demand from the high (low) priority class consumes only the reserved (unreserved) quantity. Under SN, demand from the high priority class first consumes the reserved quantity; once and if this quantity is exhausted, high priority demand competes with low priority demand for the remaining inventory. Under TN the sequence of allocation is reversed: both demand classes first compete for the unreserved inventory. Once this portion of inventory is exhausted, high priority demand is fulfilled from the reserved inventory and low priority demand is rejected. We develop service level (probability of fulfilling the entire demand) and fill rate (fraction of demand fulfilled) expressions for all three allocation mechanisms. Based on these expressions, numerical analyses are conducted to illustrate which allocation mechanism a health planner should choose depending on the availability of vaccines, and how the health planner should set the reserved quantity for the high priority class. We observe that (1) there exist certain conditions under which one of the allocation mechanisms outperforms the others and (2) this effect is determined by the decision maker’s choice of the performance measure.

Inventory rationing in an (s, Q) inventory model with lost sales and two demand classes

Whenever demand for a single item can be categorised into classes of different priority, an inventory rationing policy should be considered. In this paper we analyse a continuous review (s, Q) model with lost sales and two demand classes. A so-called critical level policy is applied to ration the inventory among the two demand classes. With this policy, low-priority demand is rejected in anticipation of future high-priority demand whenever the inventory level is at or below a prespecified critical level. For Poisson demand and deterministic lead times, we present an exact formulation of the average inventory cost. A simple optimisation procedure is presented, and in a numerical study we compare the optimal rationing policy with a policy where no distinction between the demand classes is made. The benefit of the rationing policy is investigated for various cases and the results show that significant cost reductions can be obtained.

Rationing policies for some inventory systems

This paper considers inventory systems which maintain stocks to meet various demand classes with different priorities. We use the concept of a support level control policy. That is rationing is accomplished by maintaining a support level, say K, such that when on hand stock reaches K, all low priority demands are backordered. We develop four analytical and simulation models to improve the existing models. Firstly, multiple support levels are used instead of using a single support level. Secondly, a simulation model with a more realistic assumption on the demand process has been provided. Thirdly, a single period deterministic cost minimisation model has been developed analytically. Finally, we address a continuous review (Q, r) model with a compound Poisson process.

Perishable inventory control with two types of customers and different selling prices under the warehouse capacity constraint

This paper discusses an inventory model for a single perishable product with two types of customers, high priority and low priority ones (the former buys only the newest commodity, while the latter buys either one, whether the newest or not), different selling prices reflecting the remaining lifetime of commodity and the constraint of the warehouse capacity (i.e., the capacity of own warehouse, and so the excess inventory are stored in the rental warehouse). Further demands in successive periods are assumed to be independent nonnegative random variables with known distribution functions and high priority and low priority demands are independent. Stock is depleted by high priority demand first and then low priority demand using FIFO issuing policy. Assuming two types of shortage costs, different selling prices, outdating cost purchasing cost, two types of holding costs, an optimal ordering policy to maximize the expected profit is derived.

A model for use in the rationing of inventory during lead time

While the traditional solution to the problem of meeting stochastically variable demands for inventory during procurement lead time is through the use of some level of safety stock, several authors have suggested that a decision be made to employ some form of rationing so as to protect certain classes of demands against stockout by restricting issues to other classes. Nahmias and Demmy [10] derived an approximate continuous review model of systems with two demand classes which would permit an inventory manager to calculate the expected fill rates per order cycle for high-priority, low-priority, and total system demands for a variety of parameters. The manager would then choose the rationing policy that most closely approximated his fill-rate objectives. This article describes a periodic review model that permits the manager to establish a discrete time rationing policy during lead time by prescribing a desired service level for high-priority demands. The reserve levels necessary to meet this level of service can then be calculated based upon the assumed probability distributions of high- and low-priority demands over lead time. The derived reserve levels vary with the amount of lead time remaining. Simulation tests of the model indicate they are more effective than the single reserve level policy studied by Nahmias and Demmy.

Operating Characteristics of an Inventory System with Rationing

This paper considers an inventory system which maintains stock to meet both high and low priority demands. This model is suggested by the operation of a spare parts pool in a military depot: high priority demands are those which might result in the grounding of an aircraft, for example, while low priority demands are those which arise from the routine restocking of base level inventories. We analyze the following type of control policy: there is a support level, say K > 0, such that when the level of on hand stock reaches K, all low priority demands are backordered while high priority demands continue to be filled. Both continuous review and periodic review systems are considered. The objective of the analysis is to develop methods for comparing fill rates when there is rationing and when there is no rationing for specified values of the reorder point, order quantity and support level.