Tava Lennon Olsen

Coordinating Production and Delivery Under a (z, Z)-Type Vendor-Managed Inventory Contract

This paper models a type of vendor-managed inventory (VMI) agreement that occurs in practice called a (z, Z) contract. We investigate the savings due to better coordination of production and delivery facilitated by such an agreement. The optimal behavior of both the supplier and the retailer are characterized. The optimal replenishment and production policies for a supplier are found to be up-to policies, which are shown to be easily computed by decoupling the periods when the supplier outsources from those when the supplier does not outsource. A simple application of the newsvendor relation is used to define the retailers optimal policy. Numerical analysis is conducted to compare the performance of a single supplier and a single retailer operating under a (z, Z) VMI contract with the performance of those operating under traditional retailer-managed inventory (RMI) with information sharing. Our results verify some observations made in industry about VMI and show that the (z, Z) type of VMI agreement performs significantly better than RMI in many settings, but can perform worse in others.

Review of modeling approaches for emergency department patient flow and crowding research.

Emergency department (ED) crowding is an international phenomenon that continues to challenge operational efficiency. Many statistical modeling approaches have been offered to describe, and at times predict, ED patient load and crowding. A number of formula-based equations, regression models, time-series analyses, queuing theory-based models, and discrete-event (or process) simulation (DES) models have been proposed. In this review, we compare and contrast these modeling methodologies, describe the fundamental assumptions each makes, and outline the potential applications and limitations for each with regard to usability in ED operations and in ED operations and crowding research.

Distributed power control and spreading gain allocation in CDMA data networks

We study the radio resource allocation problem of distributed joint transmission power control and spreading gain allocation in a DS-CDMA mobile data network. The network consists of K base stations and M wireless data users. The data streams generated by the users are treated as best-effort traffic, in the sense that there are no pre-specified constraints on the quality of the radio channels. We are interested in designing a distributed algorithm that achieves maximal (or near-maximal in some reasonable sense) aggregate throughput, subject to peak power constraints. We provide an algorithm where base stations coordinate in a distributed fashion to control the powers and spreading gains of the users, and show that it converges to a Nash equilibrium point. In general, there may be multiple equilibrium points; however, certain structural properties of the throughput expression can be exploited to significantly trim the search space and induce an ordering on the users in each cell. The numerical results indicate that with these modifications, the algorithm frequently converges in just a few iterations to the throughput maximizing (globally optimal) power and spreading gain allocation.

Supply Chain Management with Guaranteed Delivery

We consider a two-stage supply chain under centralized control. The downstream facility faces discrete stochastic demand and passes supply requests to the upstream facility. The upstream facilityalways meets the supply requests from downstream. If the upstream facility cannot meet the supply requests from inventory on hand, the shortage must be filled by expediting, which will incur per unit and setup costs. Such expediting may take the form of overtime production, which occurs at the end of the period and incurs relatively high production costs, or premium freight shipments, which involves building products at the beginning of the period they are needed and shipping them very quickly with relatively high shipping costs. We consider the case where one method of filling shortages is available and determine novel optimal inventory policies under centralized control. At both stages, threshold policies that depend only on the current inventory in the system are optimal; for the total inventory in the system, a base-stock policy is optimal. Numerical analysis provides insight into the optimal policies and allows us to compare the supply chains under centralized and decentralized control.

Control of a Single-Server Tandem Queueing System with Setups

This paper considers the control of a single-server tandem queueing system with setups. Jobs arrive to the system according to a Poisson process and are produced to order. A single server must perform a number of different operations on each job. There is a setup time for the server to switch between different operations. We assume that there is a holding cost at each operation, which is nondecreasing in operation number (i.e., as value is added to a job, it becomes more expensive to hold). The cont rol problem is to decide which job the server should process at each point in time. We formulate this control problem as a Markov-Decision Process. We partially characterize the optimal policy, develop an exact analysis of exhaustive and gated polling policies, and develop an effective heuristic policy. The results of a simulation study, which tests the performance of the policies considered, are reported. These computational results indicate that our heuristic is effective for a wide variety of cases.

An approach to scalability and line balancing for reconfigurable manufacturing systems

Line balancing has been an important technique for manufacturing system design, because a completely balanced system can provide maximum resource utilization at the designed capacity. However, even if a system is completely balanced, it still has capacity waste when the entire product life cycle is considered, because real production is often significantly less than capacity. Avoiding this mismatch requires scalable systems such as reconfigurable manufacturing systems (RMSs) to meet changing product demand. Stage paralleling is suggested as an approach to scalability for RMSs. By comparing the economic feasibility of such manufacturing systems with completely balanced transfer line systems with respect to station cost, it is shown that line balancing is not necessarily desirable with this approach. The effect of station cost differences for unbalanced systems is also considered.

A descriptive multi-attribute model for reconfigurable machining system selection that examines buyer-supplier relationships

Presents a model of the machining system selection process that is focused on capital intensive, complex machining systems that are intended to provide service over a long time horizon. This model was developed based on interviews with both machine tool suppliers and buyers. The systems considered here increasingly face potentially conflicting demands such as: the ability to be quickly and inexpensively upgraded and reconfigured in order to have quick new product change‐over and ramp‐up time; and high product variety at close to mass production costs. This new “reconfigurability” capability increases the importance of the supplier‐buyer relationship after the machining system has been selected. We also remark that the selection process can serve as the basis for internal consensus and team building within the buyer firm and for enhancing supplier base quality.

An Emergency Department Patient Flow Model Based on Queueing Theory Principles

OBJECTIVES The objective was to derive and validate a novel queuing theory-based model that predicts the effect of various patient crowding scenarios on patient left without being seen (LWBS) rates. METHODS Retrospective data were collected from all patient presentations to triage at an urban, academic, adult-only emergency department (ED) with 87,705 visits in calendar year 2008. Data from specific time windows during the day were divided into derivation and validation sets based on odd or even days. Patient records with incomplete time data were excluded. With an established call center queueing model, input variables were modified to adapt this model to the ED setting, while satisfying the underlying assumptions of queueing theory. The primary aim was the derivation and validation of an ED flow model. Chi-square and Students t-tests were used for model derivation and validation. The secondary aim was estimating the effect of varying ED patient arrival and boarding scenarios on LWBS rates using this model. RESULTS The assumption of stationarity of the model was validated for three time periods (peak arrival rate = 10:00 a.m. to 12:00 p.m.; a moderate arrival rate = 8:00 a.m. to 10:00 a.m.; and lowest arrival rate = 4:00 a.m. to 6:00 a.m.) and for different days of the week and month. Between 10:00 a.m. and 12:00 p.m., defined as the primary study period representing peak arrivals, 3.9% (n = 4,038) of patients LWBS. Using the derived model, the predicted LWBS rate was 4%. LWBS rates increased as the rate of ED patient arrivals, treatment times, and ED boarding times increased. A 10% increase in hourly ED patient arrivals from the observed average arrival rate increased the predicted LWBS rate to 10.8%; a 10% decrease in hourly ED patient arrivals from the observed average arrival rate predicted a 1.6% LWBS rate. A 30-minute decrease in treatment time from the observed average treatment time predicted a 1.4% LWBS. A 1% increase in patient arrivals has the same effect on LWBS rates as a 1% increase in treatment time. Reducing boarding times by 10% is expected to reduce LWBS rates by approximately 0.8%. CONCLUSIONS This novel queuing theory-based model predicts the effect of patient arrivals, treatment time, and ED boarding on the rate of patients who LWBS at one institution. More studies are needed to validate this model across other institutions.

Setting basestock levels in multi-product systems with setups and random yield

This paper provides procedures for setting optimal, or near-optimal, basestock levels in a multi-product system with setups and random yield. The procedures are derived using a novel polling system model of the system that contains both queues for production orders and queues for temporary storage of rework orders with routing occurring between these two types of queues. Both systems with backlogging and lost sales are analyzed using existing work on polling models with routing and possibly finite buffers. For a system with backlogging, we provide a cost function that is minimized by solving a set of single-item newsvendor problems. In systems with lost sales, each queue is given a finite buffer equal to the basestock level and excess demand is lost. We provide a cost function and show that finding optimal solutions for large problems is not tractable; thus, we provide a heuristic for finding the basestock levels and demonstrate the effectiveness of the heuristic and accuracy of the cost approximation through numerical tests.

ANALYTIC MODELS FOR WHEN AND HOW TO EXPEDITE IN MAKE-TO-ORDER SYSTEMS

Expediting is defined as using overtime or subcontracting to supplement regular production. This is usually done when the number of backorders has grown to be unacceptably large. In this paper, we consider analytic models for deciding when and how to expedite in a single-product make-to-order environment. We derive the structure of the optimal expediting policy in both continuous- and discrete-time cases. The continuous-time model corresponds best to subcontracting and the discrete-time model corresponds to either overtime or subcontracting. Models for performance analysis of the continuous-time case are also given.