Coupled human balancing tasks are investigated on the basis of both pseudo-neural controllers characterized by time-delayed feedback with random gain and natural human balancing tasks. It is numerically shown that in comparison to single balancing tasks, balancing tasks coupled by mechanical structures increase the stability of balancing errors in terms of both amplitude and velocity and also i...

Structured peer-to-peer (P2P) overlay networks like Distributed Hash Tables (DHTs) map data items to the network based on a consistent hashing function. Such mapping for data distribution has an inherent load balance problem. Thus, a load balancing mechanism is an indispensable part of a structured P2P overlay network for high performance. The rapid development of P2P systems has posed challeng...

While developing the inventory model with shortages under permissible delay in payments, it has been observed in the literature that the researchers have not considered the fact that the retailer can earn interest on the revenue generated after fulfilling the outstanding demand as soon as he receives the new consignment at the start of the cycle. Owing to this fact, the present study investigat...

To have an efficient control of a huge amount of inventory items, traditional approach is to classify the inventory into different groups. Different inventory control policies can then applied to different groups. The well-known ABC classification is simple-to-understand and easy-to-use. However, ABC analysis is based on only single measurement such as annual dollar usage. It has been recognize...

We consider a stochastic inventory control problem with Markovian capacity and the option of order rejection. We show its optimal policy to be the combination of a capacity-dependent modified base-stock production policy and a capacity-dependent critical-point order acceptance policy. When capacity is stochastically monotone, we show that the policy parameters change over the current capacity l...

In this paper a deterministic inventory model is developed when the deterioration is constant. Demand rate is a function of selling price and holding cost is taken as time dependent. The model is solved allowing shortage in inventory. The results are solved with the help of numerical example by using Mathematica 5.1. The sensitivity analysis of the solution has done with the changes of the valu...

Reinforcement Learning (RL) can solve practical sequential decision problems, even when structures of the problems are less understood. However, some sequential decision problems intrinsically have structural parts that are easily to formulate and distinguish from less understood parts. Exploiting this knowledge may help improve performance of RL. This study proposed and investigated an approac...