The construction and use of a general purpose synthetic program for an interactive benchmark on demand paged systems

1977 ◽  
Author(s):  
Joel N. Williams
Keyword(s):  
General Purpose ◽  
On Demand

A Web-Enabled, Mobile Intelligent Information Technology Architecture for On-Demand and Mass Customized Markets

2010 ◽  
pp. 83-123
Author(s):  
M. Ghiassi ◽  
C. Spera
Keyword(s):  
Information System ◽  
Supply Chain ◽  
Intelligent Agents ◽  
Simulated Data ◽  
General Purpose ◽  
Cost Overruns ◽  
On Demand ◽  
Intelligent Information ◽  
Improving Accuracy ◽  
Event Based

This chapter presents a web-enabled, intelligent agent-based information system model to support on-demand and mass customized markets. The authors present a distributed, real-time, Java-based, mobile information system that interfaces with firms’ existing IT infrastructures, follows a build-to-order production strategy, and integrates order-entry with supply chain, manufacturing, and product delivery systems. The model provides end-to-end visibility across the entire operation and supply chain, allows for a collaborative and synchronized production system, and supports an event-based manufacturing environment. The system introduces four general purpose intelligent agents to support the entire on-demand and mass customization processes. The adoption of this approach by a semiconductor manufacturing firm resulted in reductions in product lead time (by half), buffer inventory (from five to two weeks), and manual transactions (by 80%). Application of this approach to a leading automotive manufacturer, using simulated data, resulted in a 51% total inventory reduction while increasing plant utilization by 30%. Adoption of this architecture by a pharmaceutical firm resulted in improving accuracy of trial completion estimates from 74% to 82% for clinical trials resulting in reduced trial cost overruns. These results verify that the successful adoption of this system can reduce inventory and logistics costs, improve delivery performance, increase manufacturing facilities utilization, and provide a higher overall profitability.

scholarly journals Ezycart: Multivendor E-Commerce Website

2021 ◽  
Vol 9 (8) ◽  
pp. 2867-2869
Author(s):  
Dushyant Goyal
Keyword(s):  
General Purpose ◽  
Physical Work ◽  
The Internet ◽  
Digital Resources ◽  
On Demand ◽  
Online Platforms ◽  
Is Security ◽  
The World ◽  
User Friendly ◽  
Made In

Abstract: Due to the Pandemic, we saw a rise in the use of online or digital resources. Since the lockdown was imposed, thus day to day commute was affected severely. The vendors and markets that relied on customers this commute were severely affected. Thus, it saw the necessity to shift the business to online platforms like Amazon, Snapdeal, etc. From these experiences, the idea of Ezycart came into play, with a motive to boost the online infrastructure of E-Commerce specific to the needs and demands of the population of our country (India), easy-to-use, secure, and user-friendly Multi-vendor Website helping connect the local vendors and shops with the consumer and providing on-demand good and services. The objective of this project is to develop a general-purpose e-commerce store where any product can be bought from the comfort of home through the Internet. Unlike traditional commerce that is carried out physically with the effort of a person to go & get products, eCommerce has made it easier for humans to reduce physical work and to save time. E-Commerce which was started in the early 1990s has taken a great leap in the world of computers, but the fact that has hindered the growth of ecommerce is security. Security is the challenge facing e-commerce today & there is still a lot of advancement made in the field of security.

A Web-Enabled, Mobile Intelligent Information Technology Architecture for On-Demand and Mass Customized Markets

2011 ◽  
pp. 263-294
Author(s):  
M. Ghiassi ◽  
C. Spera
Keyword(s):  
Information System ◽  
Supply Chain ◽  
Intelligent Agents ◽  
Simulated Data ◽  
General Purpose ◽  
Cost Overruns ◽  
On Demand ◽  
Intelligent Information ◽  
Improving Accuracy ◽  
Event Based

This chapter presents a web-enabled, intelligent agent-based information system model to support on-demand and mass customized markets. The authors present a distributed, real-time, Java-based, mobile information system that interfaces with firms’ existing IT infrastructures, follows a build-to-order production strategy, and integrates order-entry with supply chain, manufacturing, and product delivery systems. The model provides end-to-end visibility across the entire operation and supply chain, allows for a collaborative and synchronized production system, and supports an event-based manufacturing environment. The system introduces four general purpose intelligent agents to support the entire on-demand and mass customization processes. The adoption of this approach by a semiconductor manufacturing firm resulted in reductions in product lead time (by half), buffer inventory (from five to two weeks), and manual transactions (by 80%). Application of this approach to a leading automotive manufacturer, using simulated data, resulted in a 51% total inventory reduction while increasing plant utilization by 30%. Adoption of this architecture by a pharmaceutical firm resulted in improving accuracy of trial completion estimates from 74% to 82% for clinical trials resulting in reduced trial cost overruns. These results verify that the successful adoption of this system can reduce inventory and logistics costs, improve delivery performance, increase manufacturing facilities utilization, and provide a higher overall profitability.

scholarly journals Gappy Pattern Matching on GPUs for On-Demand Extraction of Hierarchical Translation Grammars

2015 ◽  
Vol 3 ◽  
pp. 87-100
Author(s):  
Hua He ◽  
Jimmy Lin ◽  
Adam Lopez
Keyword(s):  
Hierarchical Models ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Practical Applications ◽  
On Demand ◽  
Mt Evaluation ◽  
Order Of Magnitude ◽  
Evaluation Dataset ◽  
The Cost ◽  
Graphics Processing

Grammars for machine translation can be materialized on demand by finding source phrases in an indexed parallel corpus and extracting their translations. This approach is limited in practical applications by the computational expense of online lookup and extraction. For phrase-based models, recent work has shown that on-demand grammar extraction can be greatly accelerated by parallelization on general purpose graphics processing units (GPUs), but these algorithms do not work for hierarchical models, which require matching patterns that contain gaps. We address this limitation by presenting a novel GPU algorithm for on-demand hierarchical grammar extraction that is at least an order of magnitude faster than a comparable CPU algorithm when processing large batches of sentences. In terms of end-to-end translation, with decoding on the CPU, we increase throughput by roughly two thirds on a standard MT evaluation dataset. The GPU necessary to achieve these improvements increases the cost of a server by about a third. We believe that GPU-based extraction of hierarchical grammars is an attractive proposition, particularly for MT applications that demand high throughput.

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