OATAO is an open access repository that collects the work of some Toulouse researchers and makes it freely available over the web where possible. This is an author's version published in : http://oatao.univ-toulouse.fr/9722Official URL : https://dx.Hydrogen supply chain MILP 3 -Constraint Lexicographic optimization M-TOPSIS a b s t r a c t 3 This work considers the potential future use of hydrogen in fuel cell electrical vehicles to face problems such as global warming, air pollution, energy security and competitiveness.The lack of current infrastructure has been identified as one of the main barriers to develop the hydrogen economy. This work is focused on the design of a hydrogen supply chain through mixed integer linear programming used to find the best solutions for a multiobjective optimization problem in which three objectives are involved, i.e., cost, global warming potential and safety risk. This problem is solved by implementing an -constraint method. The solution consists of a Pareto front, corresponding to different design strategies in the associated variable space. Multiple choice decision making is then recommended to find the best solution through an M-TOPSIS analysis. The model is applied to the Great Britain case study previously treated in the dedicated literature. Mono and multicriteria optimizations exhibit some differences concerning the degree of centralization of the network and the selection of the production technology type. (C. Azzaro-Pantel).Please cite this article in press as: De-Leó n Almaraz S, et al., Assessment of mono and multi-objective optimization to design a hydrogen supply chain, International Journal of Hydrogen Energy (2013), http://dx.

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Scheduling of semiconductor wafer fabrication system is identified as a complex problem, involving multiple objectives to be satisfied simultaneously (maximization of workstation utilization and minimization of waiting time and storage, for instance). In this study, we propose a methodology based on an artificial neural network technique, for computing the various objective functions, embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment. A discrete event simulator, developed and validated in our previous works, serves here to feed the neural network. Six criteria related to both equipment (facility average utilization) and products (average cycle time (ACT), standard deviation of ACT, average waiting time, Work In Process and total storage) are chosen as significant performance indexes of the workshop. The optimization variables are the time between campaigns and the release time of batches into the plant. An industrial size example is taken as a test bench to validate the approach.