Consideration of the relevance of standard quality techniques in Mass Customisation

The business philosophy of Mass Customisation (MC) implies rapid response to customer requests, high efficiency and limited cost overheads of customisation. Furthermore, it also implies the quality benefits of the mass production paradigm are guaranteed. However, traditional quality science in manufacturing is premised on volume production of uniform products rather than of differentiated products associated with MC. This creates quality challenges and raises questions over the suitability of standard quality engineering techniques. From an analysis of relevant MC and quality literature it is argued the aims of MC are aligned with contemporary thinking on quality and that quality concepts provide insights into MC. Quality issues are considered along three dimensions - product development, order fulfilment and customer interaction. The applicability and effectiveness of conventional quality engineering techniques are discussed and a framework is presented which identifies key issues with respect to quality for a spectrum of MC strategies.

A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the agents on solution quality are examined for two multiple-choice optimisation problems. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements.

A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the agents on solution quality are examined for two multiple-choice optimisation problems. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements.