Computing protection level policies for dynamic capacity allocation problems by using stochastic approximation methods

Computing Protection Level Policies for Dynamic Capacity Allocation Problems by Using Stochastic Approximation Methods

In this paper, we consider a dynamic capacity allocation problem. We have a fixed amount of daily processing capacity. Jobs of different priorities arrive randomly over time and we need to decide which jobs should be scheduled on which days. The jobs that are waiting to be processed incur a holding cost depending on their priority levels. We want to minimize the total expected cost over a plann...

Dynamic Stochastic Capacity Pricing for Resource Allocation

AIRLINE STOCHASTIC CAPACITY ALLOCATION BY APPLYING REVENUE MANAGEMENT

To formulate a single-leg seat inventory control problem in an airline ticket sales system, the concept and techniques of revenue management are applied in this research. In this model, it is assumed the cabin capacity is stochastic and hence its exact size cannot be forecasted in advance, at the time of planning. There are two groups of early-reserving and late-purchasing customers demanding t...

An Approximation Algorithm for Solving Multistage, Stochastic, Dynamic Resource Allocation Problems with Single Period Travel Times

We consider a stochastic version of a dynamic resource allocation problem. In this setting, reusable resources must be assigned to demands that arise randomly over time. Both resources and demands are heterogeneous, and satisfying a demand generally will change the attributes of a resource. These problems arise in eet management, personnel training and assignment, and the remnant inventory prob...

Adaptive Greedy Rules for Dynamic and Stochastic Resource Capacity Allocation Problems

In this paper we briefly present a novel dynamic and stochastic model of resource allocation that generalizes a variety of problems addressed in the literature and we outline a unified methodology for designing adaptive greedy rules. Such rules are important in practice, since they may provide an easy-to-interpret and easy-to-implement solution to problems that are intractable for optimal solut...