Using simulation to improve the blood supply chain

This case study is concerned with analysing policies for managing the blood inventory system in a typical UK hospital supplied by a regional blood centre. The objective of the project is to improve procedures and outcomes by modelling the entire supply chain for that hospital, from donor to recipient. The supply chain of blood products is broken down into material flows and information flows. Discrete-event simulation is used to determine ordering policies leading to reductions in shortages and wastage, increased service levels, improved safety procedures and reduced costs, by employing better system coordination. In this paper we describe the model and present results for a representative medium-sized hospital. The model can be used by both the National Blood Service and by hospital managers as a decision support tool to investigate different procedures and policies.     

The infinite horizon non-stationary stochastic inventory problem: Near myopic policies and weak ergodicity

In this paper we consider a periodic review dynamic inventory problem with non-stationary demands. The purpose of this paper is to show that near myopic policies are sufficiently close to optimal decisions for the infinite horizon inventory problem. In order to show this we pay attention to the fact that inventory processes with base-stock policies are weakly ergodic, and we discuss how the weak ergodicity is related to near myopic policies. Then we derive the error bounds of near myopic policies for the optimal decisions and evaluate them with a number of numerical experiments.     

Optimal control of a dual service rate M/M/1 production-inventory model

We analyse a dual-source, production-inventory model in which the processing times at a primary manufacturing resource and a second, contingent resource are exponentially distributed. We interpret the contingent source to be a subcontractor, although it could also be overtime production. We treat the inventory and contingent sourcing policies as decision variables in an analytical study and, additionally, allow the primary manufacturing capacity to be a decision variable in a subsequent numerical study. Our goal is to gain insight into the use of subcontracting as a contingent source of goods and whether it can fulfill real-world managers' expectations for improved performance. We prove that a stationary, non-randomised inventory and subcontracting policy is optimal for our M/M/1 dual-source model and, moreover, that a dual base-stock policy is optimal. We then derive an exact closed-form expression for one of the optimal base stocks, which to our knowledge is the first closed-form solution for a dual-source model. We use that closed-form result to advantage in a numerical study from which we gain insight into how optimal capacity, subcontracting, and inventory policies are set, and how effectively a contingent source can reduce total cost, capacity cost, and inventory cost. We find that (i) the contingent source can reduce total cost effectively even when contingent sourcing is expensive and (ii) contingent sourcing reduces capacity cost more effectively than it does inventory cost.     

Optimal configuration of a decentralized, market-driven production/inventory system

Pull systems are inherently easier to implement on the shop-floor; however, they are quite difficult to plan and design for optimal operation, leaving little guidelines to system designers and practitioners. In this paper we use an effective and relatively fast numerical method to understand the optimal configuration of a multi-stage, multi-product, decentralized, market-driven production/inventory system that minimizes average inventory holding subject to a service level constraint through selection of various production and procurement control parameters. We have also conducted a number of numerical experiments to understand how the control policies respond to changes in the system parameters, such as the number of stages, system workload, demand arrival rates of products, and inventory holding costs.     

Network revenue management with inventory-sensitive bid prices and customer choice

We develop an approximate dynamic programming approach to network revenue management models with customer choice that approximates the value function of the Markov decision process with a non-linear function which is separable across resource inventory levels. This approximation can exhibit significantly improved accuracy compared to currently available methods. It further allows for arbitrary aggregation of inventory units and thereby reduction of computational workload, yields upper bounds on the optimal expected revenue that are provably at least as tight as those obtained from previous approaches. Computational experiments for the multinomial logit choice model with distinct consideration sets show that policies derived from our approach can outperform some recently proposed alternatives, and we demonstrate how aggregation can be used to balance solution quality and runtime.     

生产率、需求率、变质率及损失率均随时间变化的生产库存模型

建立了生产率、需求率、变质率及损失率均随时间变化的生产库存模型,在假定了(I)变质率线性依赖于时间及库存开始点;(II)损失率线性依赖于时间及短缺量拖后供货的结束点的前提下,给出了寻求最优策略的方法,指出了文[10]中不合理的假设,并以实际的例子与已有结果作比较,其结果为生产管理部门的决策提供了理论依据.     

A comparison of different dispatching policies in two-warehouse inventory systems for deteriorating items over a finite time horizon

This paper considers a two-warehouse inventory problem for deteriorating items with a constant demand rate over a finite time horizon. A modified first-in-first-out dispatching policy is first proposed, and a new two-warehouse inventory model based on this dispatching policy is developed. The results of this model are then compared with those of other models based on classical dispatching policies, such as the last-in-first-out, modified last-in-first-out and first-in-first-out dispatching policies. We also prove the existence and uniqueness of the optimal solutions for the models considered. Finally, a numerical example is presented to illustrate the results, and several key conditions are derived for comparing the general cases of these four models.     

The bullwhip effect in supply chain networks

This paper analyzes the propagation and amplification of order fluctuations (i.e., the bullwhip effect) in supply chain networks operated with linear and time-invariant inventory management policies. The supply chain network is allowed to include multiple customers (e.g., markets), any network structure, with or without sharing information. The paper characterizes the stream of orders placed by any supplier for any stationary customer demand processes, and gives exact formulas for the variance of the orders placed and the amplification of order fluctuations. The paper also derives robust analytical conditions, based only on inventory management policies, to predict the presence of the bullwhip effect for any network structure, any inventory replenishment policies, and arbitrary customer demand processes. Numerical examples show that the analytical results accurately quantify the bullwhip effect; managerial insights are drawn from the analysis. The methodology presented in this paper generalizes those in previous studies for serial supply chains.     

The two-echelon production-routing problem

This paper introduces the Two-Echelon Production-Routing Problem. This problem is motivated from the petrochemical industry, enlarging the supply chain integration by taking into account production, inventory, and routing decisions in a two-echelon vendor-managed inventory system. We describe, model, and design a branch-and-cut (B&C) to solve the problem under different inventory policies. We also propose a novel exact algorithm, by employing parallel computing techniques, in order to combine local search procedures within a traditional B&C scheme. We evaluate the performance of our methods through extensive computational experiments, both by comparing the algorithms, the effectiveness of the different inventory policies, and the impact of these policies on the partial costs. We derive many managerial insights based on the results. We also validate our new exact algorithm by solving similar problems from the literature, such as the two-echelon multi-depot inventory-routing (2E-MDIRP) and the classical multi-vehicle production-routing problem (MV-PRP). Computational experiments show that our method is very competitive. Based on 512 experiments for the 2E-MDIRP, our algorithm was able to find 111 new best known solutions (BKS), besides proving 412 optimal solutions, against 298 from the literature. For 336 experiments over small and medium size MV-PRP instances, we proved 242 optimal solutions, 11 more than the exact methods from the literature, besides providing 95 new BKS. Moreover, we were the first to tackle large MV-PRP instances exactly, and in this case, our algorithm provides all BKS for instances up to 50 customers, 20 periods and 5 vehicles, outperforming all meta/matheuristics procedures from the literature.     

Multicriteria Trade-Offs in a Warehouse/Retailer System

Multiple Criteria Decision Making (MCDM) methodology is applied to a two-echelon serial inventory/distribution system, consisting of a warehouse and retailer. Different situations, such as deterministic and probabilistic demand, and whether marginal inventory costs are known, are discussed. Three non-linear multiobjective programming models and corresponding solution approaches are presented to obtain non-dominated inventory policies achieving trade-offs among objectives such as customer service, inventory investment and transportation cost. Our results are MCDM generalizations of Brown's exchange curve, Starr and Miller's optimal policy curve and Gardner and Dannenbring's optimal policy surface.     

A note on “Two-warehouse inventory model with deterioration under FIFO dispatch policy”

A recently published paper by Lee [C.C. Lee, Two-warehouse inventory model with deterioration under FIFO dispatching policy, European Journal of Operational Research 174 (2006) 861–873] considers different dispatching models for the two-warehouse inventory system with deteriorating items, in which Pakkala and Achary’s LIFO (last-in–first-out) model [T.P.M. Pakkala, K.K. Achary, A deterministic inventory model for deteriorating items with two warehouses and finite replenishment rate, European Journal of Operational Research 57 (1992) 71–76] is first modified, and then the author concludes that the modified LIFO model always has a lower cost than Pakkala and Achary’s LIFO model under a particular condition specified by him. The present note points out that this conclusion is incorrect and misleading. Alternatively, we provide a new sufficient condition such that the modified LIFO model always has a lower cost than Pakkala and Achary’s model. Besides, we also compare Pakkala and Achary’s original LIFO model with Lee’s FIFO (first-in–first-out) model for the special case where the two warehouses have the same deteriorating rates. Finally, numerical examples are provided to investigate and examine the impact of corresponding parameters on policy choice. The results in this note give a much clearer picture than those at Lee’s paper about the relationships between the different dispatching policies for the two-warehouse inventory system with deterioration items.     

Inventory Models with Markovian Demands and Cost Functions of Polynomial Growth

This paper studies stochastic inventory problems with unbounded Markovian demands, ordering costs that are lower semicontinuous, and inventory/backlog (or surplus) costs that are lower semicontinuous with polynomial growth. Finite-horizon problems, stationary and nonstationary discounted-cost infinite-horizon problems, and stationary long-run average-cost problems are addressed. Existence of optimal Markov or feedback policies is established. Furthermore, optimality of (s, S)-type policies is proved when, in addition, the ordering cost consists of fixed and proportional cost components and the surplus cost is convex.     

On optimal batch sizing in a multi-stage production system with rework consideration

Recently, Sarker et al. [Sarker, B.R., Jamal, A.M.M., Mondal, S., 2008. Optimal batch sizing in a multi-stage production system with rework consideration. European Journal of Operational Research 184(3) 915–929] presented an EPQ inventory model for a multi-stage manufacturing system with rework process; basically they proposed two operational inventory policies. In the paper, there are some mathematical expressions which are to be corrected. At first, this paper presents the mathematical expressions corrected and the appropriate solution to the numerical example. We also established the closed forms for the optimal total inventory cost, the conditions for which there is an optimal solution, and the mathematical expressions for determining the total additional cost for working with a non optimal solution for both policies that were not given by Sarker et al. (2008).     

Stock redistribution in two-echelon logistics systems

Consider a two-echelon periodic-review network consisting of a warehouse W and retailers R₁,…,R _n . Between replenishments of the overall system, stocks at certain retailers may become unbalanced. Therefore we allow, at a cost, the possibility of a lateral supply between retailers. This constitutes a ‘redistribution’, horizontal shipments made one period before the next regular replenishment opportunity. A stochastic inventory model is presented for such a system whose multiple stock-keeping locations have nonidentical costs; this is a major difference between the research reported here and previous work on redistribution. We developed an algorithm for carrying out the redistribution, and prove that the result is near optimal. A combination of analytical and simulation results quantify the benefits (lower costs and decreased safety stock) to our redistribution model. The resulting cost and service are compared to those of the simple allocation model (no lateral transshipments). Our cost-minimisation model is also compared to the complete redistribution model of Jonsson and Silver,¹ who minimised backorders.     

IMPUTING MISSING NATURAL RESOURCE INVENTORY DATA AND THE BOOTSTRAP

Gathering information on natural resource inventories is expensive, but lack of data inhibits resource sector modeling and policy analysis. Most work has focused on drawing broader inventory estimates from small survey samples. Other studies have used simple forward forecasting equations to project missing values. This research develops a method to impute missing inventory and growth observations when annual survey observations are not available. A one-way error component model is estimated and missing inventory values are imputed using an optimally weighted combination of forward and backward projections. This method ensures conformity of imputed observations with beginning and ending inventories. Confidence intervals for imputed inventory estimates are formed using the bootstrap method. Empirical results for estimated softwood and hardwood inventories in Louisiana are presented.     

Integer programming approach to reactive scheduling in make-to-order manufacturing

New algorithms based on mixed integer programming formulations are proposed for reactive scheduling in a dynamic, make-to-order manufacturing environment. The problem objective is to update a long-term production schedule subject to service level and inventory constraints, whenever the customer orders are modified or new orders arrive. Different rescheduling policies are proposed, from a total reschedule of all remaining and unmodified customer orders to a non-reschedule of all such orders. In addition, a medium restrictive policy is considered for rescheduling only a subset of remaining customer orders awaiting material supplies. Numerical examples modeled after a real-world scheduling/rescheduling of customer orders in the electronics industry are presented and some results of computational experiments are reported.     

Analyzing the effect of the inventory policy on order and inventory variability with linear control theory

In this paper we apply linear control theory to study the effect of various inventory policies on order and inventory variability, which are key drivers of supply chain performance. In particular, we study a two-echelon supply chain with a stationary demand pattern under the influence of three inventory policies: an inventory-on-hand policy that bases orders on the visible inventory at an installation, an installation-stock policy that bases orders on the inventory position (on-hand plus on-order inventory) at an installation, and an echelon-stock policy that bases orders on the inventory position at that installation and all downstream installations. We prove analytically that the inventory-on-hand policy is unstable in practical settings, confirming analytically what has been observed in experimental settings and in practice. We also prove that the installation-stock and echelon-stock policies are stable and analyze their effect on order and inventory fluctuation. Specifically, we show the general superiority of the echelon-stock in our setting and demonstrate analytically the effect of forecasting parameters on order and inventory fluctuations, confirming the results in other research.     

Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model

During a mass casualty incident (MCI), to which one of several area hospitals should each victim be sent? These decisions depend on resource availability (both transport and care) and the survival probabilities of patients. This paper focuses on the critical time period immediately following the onset of an MCI and is concerned with how to effectively evacuate victims to the different area hospitals in order to provide the greatest good to the greatest number of patients while not overwhelming any single hospital. This resource-constrained triage problem is formulated as a mixed-integer program, which we call the Severity-Adjusted Victim Evacuation (SAVE) model. It is compared with a model in the extant literature and also against several current policies commonly used by the so-called incident commander. The experiments indicate that the SAVE model provides a marked improvement over the commonly used ad-hoc policies and an existing model. Two possible implementation strategies are discussed along with managerial conclusions.     

Perishable inventory theory: a review.

This paper reviews the relevant literature on the problem of determining suitable ordering policies for both fixed life perishable inventory, and inventory subject to continuous exponential decay. We consider both deterministic and stochastic demand for single and multiple products. Both optimal and suboptimal order policies are discussed. In addition, a brief review of the application of these models to blood bank management is included. The review concludes with a discussion of some of the interesting open research questions in the area.