Operations research applications in the plywood industry

There are several areas in the plywood industry where Operations Research techniques have greatly assisted in better decision-making. These have resulted in improved profits, reduction of wood losses and better utilization of resources. Realizing these, some of the plywood manufacturing firms in the developed countries have established separate Operations Research departments or divisions. In the face of limited raw-material resources, raising costs and a competitive environment, the benefits attributable to the use of these techniques are becoming more and more significant.

Set theoretic operations on polygons using the scan-grid approach

This paper describes an algorithm to compute the union, intersection and difference of two polygons using a scan-grid approach. Basically, in this method, the screen is divided into cells and the algorithm is applied to each cell in turn. The output from all the cells is integrated to yield a representation of the output polygon. In most cells, no computation is required and thus the algorithm is a fast one. The algorithm has been implemented for polygons but can be extended to polyhedra as well. The algorithm is shown to take O(N) time in the average case where N is the total number of edges of the two input polygons.

Self-image (autoidolon) techniques for the realisation of optical computing type operations

Abstract is not available.

Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.

Reinforcement learning applications in dynamic pricing of retail markets

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

Multi-port network modeling of stub loaded Microstrip ring antenna for dual-band operations

A square ring microstrip antenna can be modified for dual-band operations by appropriately attaching an open ended stub. The input impedance of this antenna is analyzed here using multi-port network modeling (MNM) approach. The coupled feed is included by defining additional terms in the model. A prototype antenna is fabricated and tested to validate these computations.