| Περίληψη: | The growing need towards higher product variety greatly affects the design and operation of manufacturing systems. The emerging mass customisation paradigm can act as a solution to these needs. Yet, there is a lack of proper tools to support industrial implementations of mass customisation. One of the inadequately supported activities is the design and planning of dynamic manufacturing network configurations and their reconfiguration to adapt to changing market demands. This problem is challenging and planning teams can no longer rely on experience to yield accurate and effective solutions. Efficient decision-support systems are needed to achieve variety in cost- and time-effective ways.
These challenges are tackled in this thesis through the development of a method for supporting the design and operation of manufacturing networks using an intelligent multi-objective simulation-based decision-making approach. The method is developed into the web-based “Design and Planning of Manufacturing Networks” - DPMN© platform that consists of:
- the “User Adaptive Design System (UADS)”, which provides user-friendly design tools to allow online constrained component customisation and visualisation
- the “Decentralised Manufacturing Platform (DEMAP)” that generates, evaluates, and proposes manufacturing and transportation network alternatives for the customised products designed and ordered by the customers
which focus on supporting companies that offer mass customised or personalised products and services in two ways respectively: 1) by augmenting the engagement of the customers in the design phase of customised products and 2) by enabling the Original Equipment Manufacturers (OEMs) to efficiently design and reconfigure manufacturing networks through the exploitation of a decentralised manufacturing concept.
The decision-making process is enhanced with the developed Intelligent Search Algorithm (ISA). The ISA, during the evaluation and selection of the manufacturing network alternatives, considers multiple conflicting and user-defined criteria, including production and transportation cost and time, flexibility, quality, reliability, dynamic complexity, flexibility, annual production volume, and environmental impact. The performance of the ISA is investigated through robust engineering techniques and through a probabilistic study. Moreover, the impact of demand volatility to the operational performance of networks is examined by simulation. Discrete event simulation models are developed to assess the performance of different network topologies under diversified product demand.
The industrial applicability of the method and platform is validated in two distinct pilot cases developed in collaboration with two EU OEMs: a car-maker and a producer of CNC robotic laser cutting machine.
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