In this paper we consider a two-level inventory system with one warehouse and one retailer with information exchange. Transportation times are constant and retailer faces independent Poisson demand. The retailer applies continuous review (R,Q)-policy. The supplier starts with m initial batches (of size Q), and places an order to an outside source immediately after the retailer’s inventory posit...

In this work, the backward Bayesian multiple linear regression (BBMLR) as a new approach is presented to predict the adsorption efficiency (AE) of Cd(II) ions by ostrich bone char (OBC) in the fixed bed adsorption with four operational variables consisting of pH (2-9), inlet Cd(II) concentration (Co= 25-100 mg/L), bed depth (h= 3-9 cm) and feed flow rate (Q= 0.5-30 mL/min). The performance of t...

An active perception learning system based on reinforcement learning is proposed. A novel reinforcement architecture, called Actor-Q, is employed in which Qlearning and Actor-Critic are combined. The system decides its actions according to Q-values. One of the actions is to move its sensor, and the others are to make an answer of its recognition result, each of which corresponds to each pattern...

The key issues of Information Technology (IT) managers of Brazilian banking, a highly advanced industry, were assessed. The resulting priorities are comparable to those found in developed countries. As well as the usual key issues method of rating, two other methods were used: Q-sort and interpretive structured modeling (ISM). Respondents Q-sorted issues, and a factor analysis on these data ide...

Developing information technology led to raise the intricacy of information systems intensive, hence techniques with effectiveness and efficiency are required. These techniques are used to support users in using the information for rapid and correct decision-making. Conventional text mining and managing systems mainly use the presence or absence of keywords to discover and analyze useful inform...

Q-Iearning is a reinforcement learning alg()rithm that learns expected utilities for stateaction transitions through successive interactions with the environment The algorithm '5 simplicity as well as its convergence properties have made it a popular algorithm for study However; its non-parametric representation of utilities limits its effectiveness in environments with large amounts of percept...

We present a framework to model the dynamics of Multiagent Q-learning with -greedy exploration. The applicability of the framework is tested through experiments in typical games selected from the literature.