A Sequential Algorithm for an Inventory Selection Problem

Assume that we have m finished products in an inventory. Eachfinished product is characterized by two measurements P andQ. A customer specifies a purchase order by the requirementsof characteristics P and Q. A product is qualified to satisfya purchase order if and only if it possesses better measurementsof both P and Q than the customer requires. For a given batchof n purchase orders, the inventory selection problem is tochoose n finished products from the inventory to satisfy allpurchase orders with a minimum cost. This problem can be formulatedas a large-scale transportation problem. When the cost functionof selecting a product to satisfy an order exhibits certainstructure, we develop a fast sequential algorithm to solve thisproblem. Possible extensions and related problems are also discussedin this paper.     

Newsvendor equations for optimal reorder levels of serial inventory systems with fixed batch sizes

We consider a stochastic serial inventory system with a given fixed batch size per stage and linear inventory holding and penalty costs. For this system, echelon stock (R,nQ) policies are known to be optimal. On the basis of new average costs formulas, we obtain newsvendor equations for the optimal reorder levels.     

Optimal Solutions in the Multi-location Inventory System with Transshipments

We consider a single-period multi-location inventory system where inventory choices at each location are centrally coordinated. Transshipments are allowed as recourse actions in order to reduce the cost of shortage or surplus inventory after demands are realized. This problem has not been solved to optimality before for more than two locations with general cost parameters. In this paper we present a simple and intuitive model that enables us to characterize optimal inventory and transshipment policies for three and four locations as well. The insight gained from these analytical results leads us to examine the optimality conditions of a greedy transshipment policy. We show that this policy will be optimal for two and three locations. For the n location model we characterize the necessary and sufficient conditions on the cost structure for which the greedy transshipment policy will be optimal.      

Exact evaluation of (R,Q)-policies for two-level inventory systems with Poisson demand

In this paper we show how to exactly evaluate holding and shortage costs for a two-level inventory system with one warehouse and N different retailers. Lead-times (transportation times) are constant, and the retailers face different Poisson demand processes. All facilities apply continuous review (R, Q)-policies. We express the policy costs as a weighted mean of costs for one-for-one ordering policies.     

Optimality conditions for regenerative inventory systems under batch demands

We consider a single-item, continuous-review, (s, S) inventory system, under complete backlogging and a constant procurement lead time. Demands occur in independent, identically distributed batches, separated by independent identically distributed intervals. The model also includes a class of periodic review systems as a special case. Of interest are the optimal control policies with respect to a stationary cost rate function, constructed to include ordering, holding and shortage costs. We study the structural properties of the cost rate function and report some new bounds and optimality conditions. An application of these to the computation and approximation of optimal policies is also discussed.     

Optimal dynamic pricing and inventory control with stock deterioration and partial backordering

This paper studies the optimal dynamic pricing and inventory control policies in a periodic-review inventory system with fixed ordering cost and additive demand. The inventory may deteriorate over time and the unmet demand may be partially backlogged. We identify two sufficient conditions under which (s,S,p) policies are optimal.     

Optimal and near-optimal policies for lost sales inventory models with at most one replenishment order outstanding

In this paper we use policy-iteration to explore the behaviour of optimal control policies for lost sales inventory models with the constraint that not more than one replenishment order may be outstanding at any time. Continuous and periodic review, fixed and variable lead times, replenishment order sizes which are constrained to be an integral multiple of some fixed unit of transfer and service level constraint models are all considered. Demand is discrete and, for continuous review, assumed to derive from a compound Poisson process. It is demonstrated that, in general, neither the best (s, S) nor the best (r, Q) policy is optimal but that the best policy from within those classes will have a cost which is generally close to that of the optimal policy obtained by policy iteration. Finally, near-optimal computationally-efficient control procedures for finding (s, S) and (r, Q) policies are proposed and their performance illustrated.     

An economic production quantity model with a positive resetup point under random demand

In this paper, an extended economic production quantity (EPQ) model is investigated, where demand follows a random process. This study is motivated by an industrial case for precision machine assembly in the machinery industry. Both a positive resetup point s and a fixed lot size Q are implemented in this production control policy. To cope with random demand, a resetup point, i.e., the lowest inventory level to start the production, is adapted to minimize stock shortage during the replenishment cycle. The considered cost includes setup cost, inventory carrying cost, and shortage cost, where shortage may occur at the production stage and/or at the end of one replenishment cycle. Under some mild conditions, the expected cost per unit time can be shown to be convex with respect to decision parameters s and Q. Further computational study has demonstrated that the proposed model outperforms the classical EPQ when demand is random. In particular, a positive resetup point contributes to a significant portion of this cost savings when compared with that in the classical lot sizing policy.     

Computing a Stationary Base-Stock Policy for a Finite Horizon Stochastic Inventory Problem with Non-linear Shortage Costs

Abstract

We consider a periodic-review stochastic inventory problem in which demands for a single product in each of a finite number of periods are independent and identically distributed random variables. We analyze the case where shortages (stockouts) are penalized via fixed and proportional costs simultaneously. For this problem, due to the finiteness of the planning horizon and non-linearity of the shortage costs, computing the optimal inventory policy requires a substantial effort as noted in the previous literature. Hence, our paper is aimed at reducing this computational burden. As a resolution, we propose to compute “the best stationary policy.” To this end, we restrict our attention to the class of stationary base-stock policies, and show that the multi-period, stochastic, dynamic problem at hand can be reduced to a deterministic, static equivalent. Using this important result, we introduce a model for computing an optimal stationary base-stock policy for the finite horizon problem under consideration. Fundamental analytic conclusions, some numerical examples, and related research findings are also discussed.     

Ranking of generalised multi-stage KANBAN policies

We consider KANBAN type batch-ordering policies in a multi-stage production–inventory system. KANBAN policies and various modifications of such policies may be interpreted as installation stock (Q, r)-policies with constraints on the number of outstanding orders. If the same constraints are applied also to MRP and echelon stock (Q, r)-policies, the ranking lists established by Axsäter and Rosling (Axsäter, S., Rosling, K., 1993. Management Science 39, 1274–1280; Axsäter, S., Rosling, K., 1994. European Journal of Operational Research 75, 405–412) for such policies remain valid. The result is based on new definitions of the inventory positions, and suggests new classes of generalised KANBAN type batch-ordering policies with potentially improved performance.     

Modeling and analysis for determining optimal suppliers under stochastic lead times

The policy of simultaneously splitting replenishment orders among several suppliers has received considerable attention in the last few years and continues to attract the attention of researchers. In this paper, we develop a mathematical model which considers multiple-supplier single-item inventory systems. The item acquisition lead times of suppliers are random variables. Backorder is allowed and shortage cost is charged based on not only per unit in shortage but also per time unit. Continuous review (s,Q)$(s\text{,}Q)$ policy has been assumed. When the inventory level depletes to a reorder level, the total order is split among n suppliers. Since the suppliers have different characteristics, the quantity ordered to different suppliers may be different. The problem is to determine the reorder level and quantity ordered to each supplier so that the expected total cost per time unit, including ordering cost, procurement cost, inventory holding cost, and shortage cost, is minimized. We also conduct extensive numerical experiments to show the advantages of our model compared with the models in the literature. According to our extensive experiments, the model developed in this paper is the best model in the literature which considers order splitting for n-supplier inventory systems since it is the nearest model to the real inventory system.     

On the value of customer information for an independent supplier in a continuous review inventory system

We consider the inventory control problem of an independent supplier in a continuous review system. The supplier faces demand from a single customer who in turn faces Poisson demand and follows a continuous review (R, Q) policy. If no information about the inventory levels at the customer is available, reviews and ordering are usually carried out by the supplier only at points in time when a customer demand occurs. It is common to apply an installation stock reorder point policy. However, as the demand faced by the supplier is not Markovian, this policy can be improved by allowing placement of orders at any point in time. We develop a time delay policy for the supplier, wherein the supplier waits until time t after occurrence of the customer demand to place his next order. If the next customer demand occurs before this time delay, then the supplier places an order immediately. We develop an algorithm to determine the optimal time delay policy. We then evaluate the value of information about the customer’s inventory level. Our numerical study shows that if the supplier were to use the optimal time delay policy instead of the installation stock policy then the value of the customer’s inventory information is not very significant.     

Inventory models with minimal service level constraints

This paper investigates inventory models in which the stockout cost is replaced by a minimal service level constraint (SLC) that requires a certain level of service to be met in every period. The minimal service level approach has the virtue of simplifying the computation of an optimal ordering policy, because the optimal reorder level is solely determined by the minimal SLC and demand distributions. It is found that above a certain “critical” service level, the optimal (s,S) policy “collapses” to a simple base-stock or order-up-to level policy, which is independent on the cost parameters. This shows the minimal SLC models to be qualitatively different from their shortage cost counterparts. We also demonstrate that the “imputed shortage cost” transforming a minimal SLC model to a shortage cost model does not generally exist. The minimal SLC approach is extended to models with negligible set-up costs. The optimality of myopic base-stock policies is established under mild conditions.     

A generalized discrete-time order replacement model

In this article, we consider a discrete-time order replacementproblem. More precisely, we treat a generalized model with morecomplex cost structure than Kaio & Osaki (1979, IEEE Trans.Reliab., R-29, 405–406) and with two decision variables:allowable inventory time and ordering time. Based on the discreteprobabilistic argument, we derive the optimal ordering policyto deliver a spare unit preventively by a regular order, soas to minimize the expected cost per unit time in the steadystate. Numerical examples are devoted to carrying out the sensitivityanalysis of model parameters on the optimal ordering policyand its associated expected cost value.     

Simple heuristics for push and pull remanufacturing policies

Inventory policies for joint remanufacturing and manufacturing have recently received much attention. Most efforts, though, were related to (optimal) policy structures and numerical optimization, rather than closed form expressions for calculating near optimal policy parameters. The focus of this paper is on the latter. We analyze an inventory system with unit product returns and demands where remanufacturing is the cheaper alternative for manufacturing. Manufacturing is also needed, however, since there are less returns than demands. The cost structure consists of setup costs, holding costs, and backorder costs. Manufacturing and remanufacturing orders have non-zero lead times. To control the system we use certain extensions of the familiar (s, Q) policy, called push and pull remanufacturing policies. For all policies we present simple, closed form formulae for approximating the optimal policy parameters under a cost minimization objective. In an extensive numerical study we show that the proposed formulae lead to near-optimal policy parameters.     

Optimal pricing and production in an inventory model

This paper deals with the problem of simultaneously determining the optimal price policy and production rate over a given planning horizon. For nonlinear demand functions and convex inventory and shortage cost functions the optimal solution paths are derived by using optimal control theory. The treatment of linear nonsmooth cost functions requires the use of a generalized maximum principle. The solution method is a phase portrait analysis providing insight into the optimal pricing and production policies as well as the resulting inventory paths. Moreover, it is shown that in the case of nonsmooth piecewise linear cost functions the equilibrium is approached within finite time although the model is nonlinear in the control variables. Finally it is illustrated that exogenous fluctuations in the demand rate (seasonal demand pattern) amount to cyclical optimal solutions.     

Optimal ordering policies for periodic-review systems with a refined intra-cycle time scale

Chiang [C. Chiang, Optimal ordering policies for periodic-review systems with replenishment cycles, European Journal of Operational Research 170 (2006) 44–56] recently proposed a dynamic programming model for periodic-review systems in which a replenishment cycle consists of a number of small periods (each of identical but arbitrary length) and holding and shortage costs are charged based on the ending inventory of small periods. The current paper presents an alternative (and concise) dynamic programming model. Moreover, we allow the possibility of a positive fixed cost of ordering. The optimal policy is of the familiar (s, S) type because of the convexity of the one-cycle cost function. As in the periodic-review inventory literature, we extend this result to the lost-sales periodic problem with zero lead-time. Computation shows that the long-run average cost is rather insensitive to the choice of the period length. In addition, we show how the proposed model is modified to handle the backorder problem where shortage is charged on a per-unit basis irrespective of its duration. Finally, we also investigate the lost-sales problem with positive lead-time, and provide some computational results.     

The Finite Horizon Trended Inventory Replenishment Problem With Shortages

A new type of replenishment policy is suggested for an inventory item having a finite shortage cost and linear trend in demand over a finite time horizon. The optimal solution of the suggested replenishment policy has a lower total cost as compared with the optimal solution for the traditional replenishment policies.     

Two Models that show the Interpolation Theorem Fails in all L1(Q{alpha}) and L1, 1 (Q{alpha}, {alpha} = 0, 1, 2,...

The logics L¹(Q), L^1,1(Q) and L²(Q) are formed by adding quantifiersQ, Q^1,1 and Q² respectively to the first-order logic. In thispaper, for each ordinal (including = 0), we construct twoQ models to prove that the Interpolation Theorem fails in L(Q)and L^1,1 (Q).