Self-Adaptation for Performance Optimisation in an Agent-Based Information System

Author(s):  
C. Gerber
Author(s):  
Eda Ustaoglu ◽  
Arif Çagdaş Aydinoglu

Land-use change models are tools to support analyses, assessments, and policy decisions concerning the causes and consequences of land-use dynamics, by providing a framework for the analysis of land-use change processes and making projections for the future land-use/cover patterns. There is a variety of modelling approaches that were developed from different disciplinary backgrounds. Following the reviews in the literature, this chapter focuses on various modelling tools and practices that range from pattern-based methods such as machine learning and GIS (Geographic Information System)-based approaches, to process-based methods such as structural economic or agent-based models. For each of these methods, an overview is given for the advances that have been progressed by geographers, natural and economy scientists in developing these models of spatial land-use change. It is noted that further progress is needed in terms of model development, and integration of models operating at various scales that better address the multi-scale characteristics of the land-use system.


Author(s):  
George T.S. Ho ◽  
Henry C.W. Lau ◽  
C.K.M. Lee ◽  
Andrew W.H. Ip ◽  
William Ho

Author(s):  
M. Ghiassi ◽  
C. Spera

This chapter presents a Web-enabled, agent-based information system model to support mass-customized markets. We present a distributed, real-time, Java-based, mobile intelligent 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 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 mass customization process. 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%). Similarly, the adoption by a leading automotive manufacturer resulted in a 51% total inventory reduction while increasing plant utilization by 30%. These results verify that the successful adoption of this system can reduce inventory and logistic costs, improve delivery performance, increase manufacturing facilities utilization, and provide a higher overall profitability.


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