Expressions for item fill rates in periodic inventory systems

We examine the problem of estimating the item fill rate in a periodic inventory system. We show that the traditional expressions for line item fill rate, found in many operations management textbooks, perform well for high fill rates (above 90%), but they consistently underestimate the true fill rate. The problem of underestimation becomes significant as the fill rate falls below 90% and is greatly amplified in cases with very low fill rates (below 50%). We review other more accurate expressions for fill rate, discussing their relative merits. We then develop an exact fill rate expression that is robust for both high and low fill rates. We compare the new expression to others found in the literature via an extensive set of simulation experiments using data that reflect actual inventory systems found at Hewlett-Packard. We also examine the robustness of the expressions to violations in the underlying assumptions. Finally, we develop an alternative fill rate expression that is robust for cases of high demand variability where product returns are allowed. © 1995 John Wiley & Sons, Inc.     

Criteria and approximate methods for path-constrained moving-target search problems

A search is conducted for a target moving in discrete time among a finite number of cells according to a known Markov process. The searcher must choose one cell in which to search in each time period. The set of cells available for search depends upon the cell chosen in the last time period. The problem is to find a search path, i.e., a sequence of search cells, that either maximizes the probability of detection or minimizes the mean number of time periods required for detection. The search problem is modelled as a partially observable Markov decision process and several approximate solutions procedures are proposed. © 1995 John Wiley & Sons, Inc.     

Discount pricing policies for inventories subject to declining demand

Until only recently, the mechanism behind determining item price has been ignored and the discount price taken as a given in quantity-discount inventory decision problems. Inventory subject to declining demand further complicates both pricing and replenishment decisions. This article provides the vendor with the means for optimally determining both the discount price and replenishment order frequency for all buyers in the system in an environment of declining demand. In the multiple-buyer case, we provide an efficient algorithm for classifying buyers into homogeneous subgroups to further enhance joint cost savings among all system participants.     

Should we be using cost of capital in calculating holding costs of consumable inventory?

The DOD directs the usage of 10% of item cost as the cost of capital in the calculation of inventory holding costs. This 10% cost is not totally justified and a complete review must be accomplished to bring this factor to a meaningful and more useful value. The current logic supporting a 10% cost of capital results in a continuing perturbation which forces the Air Force to operate in a less than efficient mode when using the economic order quantity for consumable purchases.     

Algorithms for the design of finite-capacity service units

Finite-capacity queues arise naturally in many practical situations, notably in communications and manufacturing engineering. In this article, the matrix formalism of probability distributions of phase type is used to develop fast algorithms to compute various steady-state distributions for the finite-capacity PH/PH/1 queue. This algorithm is an important ingredient in solving nontraditional but significant design problems. Some of these are described as illustrative examples.     

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.     

A survey of preventive maintenance models for stochastically deteriorating single-unit systems

A survey of the research done on preventive maintenance is presented. The scope of the present survey is on the research published after the 1976 paper by Pierskalla and Voelker [98]. This article includes optimization models for repair, replacement, and inspection of systems subject to stochastic deterioration. A classification scheme is used that categorizes recent research into inspection models, minimal repair models, shock models, or miscellaneous replacement models.     

The inventory packing problem

We study the problem of finding the minimum number of identical storage areas required to hold n items for which demand is known and constant. The replenishments of the items within a single storage area may be time phased so as to minimize the maximum total storage capacity required at any time. This is the inventory-packing problem, which can be considered as a variant of the well-known bin-packing problem, where one constraint is nonlinear. We study the worst-case performance of six heuristics used for that earlier problem since the recognition version of the inventory-packing problem is shown to be NP complete. In addition, we describe several new heuristics developed specifically for the inventory-packing problem, and also study their worst-case performance. Any heuristic which only opens a bin when an item will not fit in any (respectively, the last) open bin needs, asymptotically, no more than 25/12 (resp., 9/4) times the optimal number of bins. Improved performance bounds are obtainable if the range from which item sizes are taken is known to be restricted. Extensive computational testing indicates that the solutions delivered by these heuristics are, for most problems, very close to optimal in value.     

Identifying and ranking a most preferred subset of alternatives in the presence of multiple criteria

In this article an interactive method is developed to identify and rank a most preferred subset, T, of alternatives assuming that the decision maker has an implicit quasiconcave nondecreasing utility function. The method requires the decision maker to compare pairs of selected alternatives. Based on the responses of the decision maker, convex cones are constructed to eliminate alternatives that are proved to be inferior to alternatives in set T. The method aims at keeping the number of pairwise comparisons small. Computational experience with the method indicates that the required number of pairwise comparisons to form set T is usually small. However, the number of pairwise comparisons needed to confirm that this set is best may be large.