This paper studies pricing strategies for networks with stochastic capacity variation. Call-level simulation is used to compare the profit generated with four different pricing models, and with different policies for the management of call dropping episodes in the network. Both user-oriented and network-oriented performance metrics are considered. The simulation results show that the choice of ...

A firm tends to associate capacity planning with economic scale size and demand fulfillment for profit maximization. However, it is troublesome capacity dilemmas to achieve both of them simultaneously in stochastic environment. We propose a multi-objective stochastic programming with data envelopment analysis (DEA) constraints to find a compromise efficient benchmark.

The addition of noise to the deterministic Hopfield network, trained with one shot Hebbian learning, is known to bring benefits in the elimination of spurious attractors. This paper extends the analysis to learning rules that have a much higher capacity. The relative energy of desired and spurious attractors is reported and the affect of adding noise to the dynamics is empirically investigated....

We develop a general framework for determining the optimal resource capacity for each station comprising a stochastic network, motivated by applications arising in computer capacity planning and business process management. The problem is mathematically intractable in general and therefore one typically resorts to either overly simplistic analytical approximations or very time-consuming simulat...