Résumé
In this article, the author describes the results of a collaborative European project work with partners from Bouygues, Euro-Decision, ICL, IC-Parc, NTUA, and Renault. Abstract. The industrial and commercial worlds are increasingly competitive , requiring companies to be more productiveand more responsiveto market changes (e.g. globalisation and privatisation). As a consequence, there is a strong need for solutions to large scale optimization problems, in domains such as production scheduling, transport, nance and network management. This means that more experts in constraint programming and optimization technology are required to develop adequate software. Given the computational complexity of Large Scale Combinatorial Optimization problems, a key question is how to help/guide in the tackling of LSCO problems in industry. Optimization technology is certainly reaching a level of maturity. Having emerged in the 50s within the Operational Research community, it has evolved and comprises new paradigms such as constraint programming and stochastic search techniques. There is a practical need, i.e. eeciency, scalability and tractability, to integrate techniques from the diierent paradigms. This adds complexity to the design of LSCO models and solutions. Various forms of guidance are available in the literature in terms of 1) case studies that map powerful algorithms to problem instances, and 2) visualiza-tion and programming tools that ease the modelling and solving of LSCOs. However, there is little guidance to address the process of building applications for new LSCO problems (independently of any language). This article gives an overview of the CHIC-2 methodology which aims at lling a gap in this direction. In particular, we describe some management issues speciic to LSCOs such as risk management and team structures, and focus on the technical development guidance for scoping, designing and implementing LSCO applications. The design part in particular views the modelling of LSCOs from a multi-paradigm perspective.