Ready configuration of machines into an existing manufacturing system

This paper focuses on simplifying and easing the integration of a new machine into an existing conventional hierarchical manufacturing system. Based on a distributed manufacturing paradigm, it proposes the functions and interfaces that a new machine and an existing manufacturing system should possess so that ready and simple configuration of additional machines can be achieved. The configuration process is intended to include, not only mechanical and electrical interfaces, but also decision system interfaces (such as planning, scheduling, and shop-floor control). Preliminary laboratory experiments to compare the reconfigurability resulting from a conventional integration method and the proposed distributed method are presented and discussed. © 2007 ISAM.

Technology management tools: Generalization, integration and configuration

Managers in technology-intensive businesses need to make decisions in complex and dynamic environments. Many tools, frameworks and processes have been developed to support managers in these situations, leading to a proliferation of such approaches, with little consistency in terminology or theoretical foundation, and a lack of understanding of how such tools can be linked together to tackle management challenges in an integrated way. As a step towards addressing these issues, this paper proposes the concept of an integrated 'toolkit', incorporating generalized forms of three core technology management tools that support strategic planning (roadmapping, portfolio analysis and linked analysis grids). © 2006 World Scientific Publishing Company.

Simulation-based optimal sensor scheduling with application to observer trajectory planning

The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions. © 2007 Elsevier Ltd. All rights reserved.