Operations & Supply Chain Management

We review queueing-theory methods for setting staffing requirements in service systems where customer demand varies in a predictable pattern over the day. Analyzing these systems is not straightforward, because standard queueing theory focuses on the long-run steady-state behavior of stationary models. We show how to adapt stationary queueing models for use in nonstationary environments so that time-dependent performance is captured and staffing requirements can be set.

In a decentralized supply chain, with long-term competition between independent retailers facing random demands and buying from a common supplier, how should wholesale and retail prices be specified in an attempt to maximize supply-chain-wide profits? We show what types of coordination mechanisms allow the decentralized supply chain to generate aggregate expected profits equal to the optimal profits in a centralized system, and how the parameters of these (perfect) coordination schemes can be determined.

We consider a family of N items that are produced in, or obtained from, the same production facility. Demands are deterministic for each item and each period within a given horizon of T periods. If in a given period an order is placed, setup costs are incurred. The aggregate order size is constrained by a capacity limit. The objective is to find a lot-sizing strategy that satisfies the demands for all items over the entire horizon without backlogging, and that minimizes the sum of inventory-carrying costs, fixed-order costs, and variable-order costs.

We characterize supply chain settings in which perfect coordination can be achieved with simple wholesale pricing schemes: either retailer-specific constant unit wholesale prices or retailer-specific volume discount schemes. We confine ourselves to two-echelon supply chains with a single supplier servicing a network of retailers who compete with each other by selecting sales quantities.

Consider a make-to-order manufacturer that offers multiple products to a market of price and delay sensitive users.

We consider the problem of estimating convex boundaries from blurred and noisy observations. In our model, the convolution of an intensity function f is observed with additive Gaussian white noise. The function f is assumed to have convex support G whose boundary is to be recovered.

Hospital diagnostic facilities, such as magentic resonance imaging centers, typically provide service to several diverse patient groups: outpatients, who are scheduled in advance; inpatients, whose demands are generated randomly during the day; and emergency patients, who must be served as soon as posssible. Our analysis focuses on two inter-related tasks: designing the outpatient appoitnment schedule, and establishing dynamic priority rules for admitting patients into service.

We study nonparametric change-point estimation from indirect noisy observations. Focusing on the white noise convolution model, we consider two classes of functions that are smooth apart from the change-point. We establish lower bounds on the minimax risk in estimating the change-point and develop rate optimal estimation procedures. The results demonstrate that the best achievable rates of convergence are determined both by smoothness of the function away from the change-point and by the degree of ill-posedness of the convolution operator.

We consider a single class open queueing network, also known as a generalized Jackson network (GJN). A classical result in heavy-traffic theory asserts that the sequence of normalized queue length processes of the GJN converge weakly to a reflected Brownian motion (RBM) in the orthant, as the traffic intensity approaches unity. However, barring simple instances, it is still not known whether the stationary distribution of RBM provides a valid approximation for the steady-state of the original network.