Eylem Tekin

Age-based vs. stock level control policies for a perishable inventory system

Abstract In this study, we investigate the impact of modified lotsize-reorder control policy for perishables which bases replenishment decisions on both the inventory level and the remaining lifetimes of items in stock. We derive the expressions for the key operating characteristics of a lost sales perishable inventory model, operating under the proposed age-based policy, and examine the sensitivity of the optimal policy parameters with respect to various system parameters. We compare the performance of the suggested policy to that of the classical ( Q , r ) type policy through a numerical study over a wide range of system parameters. Our findings indicate that the age-based policy is superior to the stock level policy for slow moving perishable inventory systems with high service levels.

A Stochastic Model for Joint Inventory and Outbound Shipment Decisions

In this paper, we consider a vendor realizing a sequence of random order arrivals in random sizes. The vendor has the autonomy to hold/consolidate small orders until an economical dispatch quantity accumulates. Consequently, the actual inventory requirements at the vendor are in part determined by the parameters of the shipment release policy in use. In this context, we investigate the impact of shipment consolidation on the expected long-run average cost by simultaneously computing the optimal order quantity for inventory replenishment at the vendor and the optimal dispatch quantity for outbound shipments. Since we consider the case where demand follows a general stochastic bulk arrival process, obtaining exact analytical expressions for some key operating characteristics of the cost function is intractable. Hence, we provide easy-to-compute approximations which enable efficient numerical solutions for the problem. We also investigate: (i) the cases where consolidated shipments are preferred over immediate deliveries; (ii) the sensitivity of optimal integrated policy variables to demand/cost parameters; (iii) the potential savings that can be obtained by shipment consolidation; and (iv) the tradeoffs between the waiting time induced by shipment consolidation and costs saved. Our results provide insights into the impact of outbound transportation operations on inventory replenishment decisions and outbound distribution system design. Moreover, numerical testing suggests that significant cost savings (up to 57%) are possible with shipment consolidation.

Dynamic maintenance scheduling for power systems incorporating hurricane effects

An effective preventive maintenance program plays a critical role in improving the reliability of electric power systems. In this paper, the impact of potential damage due to hurricane is incorporated in the power system maintenance scheduling problem. The proposed model considers component deterioration, as well as two competing and independent failure modes, i.e., failure due to loss of reliability and failure due to hurricane damages. Furthermore, the interrelationship between the component, the grid, and the associated downtime cost dynamics are incorporated in the problem. The downtime cost of the power system due to component outage is formulated using mixed-integer programming. The obtained downtime cost is used in a stochastic dynamic programming model to derive the optimal maintenance policy for the component.

On the comparison of risk-neutral and risk-averse newsvendor problems

The objective of this paper is to investigate and compare the relationship between risk-neutral and risk-averse newsvendor problems under three different decision criteria: expected utility (EU) maximization, mean-variance (MV) analysis, and conditional value-at-risk (CVaR) minimization. Several models in the literature have shown that for special cases of the newsvendor problem (eg, no salvage value, no shortage penalty, and with recourse option), a risk-averse newsvendor orders less than a risk-neutral newsvendor. First, we present an observation about the EU maximization models with such special cases where a risk-averse newsvendor orders less than a risk-neutral one. We note that this observation does not extend to the newsvendor problem with positive shortage penalty. Using several counterexamples, we demonstrate that the common wisdom that a risk-averse newsvendor orders less than a risk-neutral newsvendor is not true in general. Second, we demonstrate, analytically where possible and numerically if not, that the comparison of the optimal order quantities of risk-neutral and risk-averse newsvendors depends on the key assumptions regarding the model inputs, namely, the decision criterion, the demand distribution and the cost parameters such as shortage penalty and unit ordering cost. Third, we show that EU and the MV criteria yield consistent results while EU and CVaR criteria may yield consistent or conflicting results depending on the loss function used for the CVaR criterion.

System Hardening and Condition-Based Maintenance for Electric Power Infrastructure Under Hurricane Effects

The devastating impact of hurricanes on electric power systems calls for development of sophisticated asset management strategies for utility infrastructure. El Niño/La Niña are shown to have seasonal effects on hurricane arrivals in the long-term climatological horizon. The periodic effects of such natural phenomena need to be analyzed and incorporated into decision making processes for strategic asset management of power systems. In this paper, an integrated infrastructure hardening and condition-based maintenance scheduling model for critical components of the power systems is proposed. Partially observable Markov decision processes are used to formulate the problem. The survival function against hurricanes is derived as a dynamic stress-strength model, and is incorporated in the proposed framework. A generalized formulation for two hardening strategies, i.e., with temporary and permanent hardening effects, is proposed. Case studies are conducted to analyze the model and further illustrate the effectiveness of the proposed strategy.

Cyclic Consumption and Replenishment Decisions for Vendor-Managed Inventory of Multisourced Parts in Dell’s Supply Chain

Multisourcing and vendor-managed inventory (VMI) programs with percentage supply allocations (PSAs) are two common practices in the electronics industry. A PSA is a prenegotiated percentage of a multisourced part’s total demand, which should be provided by a supplier. For parts within Dell’s supply chain for which these practices are applicable, we provide a framework to implement pull-based replenishments, verify its feasibility, and demonstrate the potential benefits via simulation. Specifically, we investigate two decisions to meet customer demand and PSAs with minimal inventory: (1) how to allocate actual customer demand among multiple suppliers, and (2) what guidelines Dell should specify to suppliers for VMI hub replenishments. For demand allocations, we propose a consumption cycle based on the PSAs. We consider a heuristic replenishment policy and develop an algorithm to compute the policy parameters, which can be used as supplier guidelines. The proposed approach presents a significant opportunity for cost savings. Dell used the results of this project (1) to address skepticism toward a pull-based approach in managing VMI replenishments, (2) to gain stakeholder support to implement a pull-based approach, and (3) to emphasize the need for a change in Dell’s philosophy on managing demand allocations to suppliers and VMI hub replenishments.