Operations & Supply Chain Management

Due to the increase in diversity of wireless devices, streaming media systems must be capable of serving multiple types of users. Scalable coding allows for adaptations without re-encoding. To account for various viewing capabilities of each user, such as different spatial resolutions, multiple distortion measures are used. In this paper, we examine the question of how to broadcast media packets with multiple distortion measures to multiple users. We cast the problem as a stochastic shortest path problem and use Dynamic Programming to find the optimal policy.

Stock-outs convey information about the propensity of other consumers to purchase a product and this can increase the willingness of marginally interested consumers to buy. But in order to leverage stock-outs, firms must be able to capture the extra demand.

We consider a call center model with multiple customer classes and multiple server pools. Calls arrive randomly over time, and the instantaneous arrival rates are allowed to vary both temporally and stochastically in an arbitrary manner. The objective is to minimize the sum of personnel costs and expected abandonment penalties by selecting an appropriate staffing level for each server pool.

We establish the convergence of the min-sum message passing algorithm for minimization of a quadratic objective function given a convex decomposition. Our results also apply to the equivalent problem of the convergence of Gaussian belief propagation.

This paper presents a general class of dynamic stochastic optimization problems we refer to as Stochastic Depletion Problems. A number of challenging dynamic optimization problems of practical interest are stochastic depletion problems. Optimal solutions for such problems are difficult to obtain, both from a pragmatic computational perspective as also from a theoretical perspective. As such, simple heuristics are desirable.

We develop a model for the competitive interactions in service industries where firms cater to multiple customer classes or market segments with the help of shared service facilities or processes so as to exploit pooling benefits. Different customer classes typically have distinct sensitivities to the price of service as well as the delays encountered.

Cross-selling is becoming an increasingly prevalent practice in call centers, due, in part, to its unique capability to allow firms to dynamically segment their callers and customize their product offerings accordingly. This paper considers a call center with cross-selling capability that serves a pool of customers that are differentiated in terms of their revenue potential and delay sensitivity. It studies the operational decisions of staffing, call routing, and cross-selling under various forms of customer segmentation.

We develop a competitive pricing model which combines the complexity of time-varying demand and cost functions and that of scale economies arising from dynamic lot sizing costs. Each firm can replenish inventory in each of the T periods into which the planing horizon is partitioned. Fixed as well as variable procurement costs are incurred for each procurement order, along with inventory carrying costs. Each firm adopts, at the beginning of the planning horizon, a (single) price to be employed throughout the horizon.

We develop a competitive pricing model which combines the complexity of time-varying demand and cost functions and that of scale economies arising from dynamic lot sizing costs. Each firm can replenish inventory in each of the T periods into which the planning horizon is partitioned. Fixed as well as variable procurement costs are incurred for each procurement order, along with inventory carrying costs. Each firm adopts, at the beginning of the planning horizon, a (single) price to be employed throughout the horizon.