McLeod Institute of Simulation Sciences
Academic Certificate

Engineering Degree (five years plus thesis) given by the

University of Genoa

To:

Enrico Briano

Supervisor:

Agostino Bruzzone

No:

4

Thesis Title

Advanced M&S applied to Analysis Techniques for Supporting Decision Makers in Multi-Job Management in an Aeronautical Industry

Summary
The productive processes of an executive airplane are quite complex due to the high number of components and supplies, strict standards & regulations, long duration of jobs and being highly tailored; in fact, even if the basic structure of the plane is always the same, every customer, both private and institutional (national departments, military forces, police, fire departments, etc), fix in detail the configuration, including special optional kits for future utilization; for these reasons the cost of the production for every single airplane is very high.
This research underlines the importance of M&S (Modeling and Simulation) as a powerful tool for the Decision Support and is devoted to investigate new solutions for reducing W.I.P. (Work In Process) and the production process lead time, as well as their related impact on costs: the Test Case was provided by an Italian aircraft Company that is one of the most important on executive plane national market.
To reach this goal a stochastic discrete event driven simulation model has been developed: it is a C++ built-model called M.A.C.A.C.O. (Modeling Air Craft Analysis of Construction process and Organization) that takes into account all the production processes and stochastic components; the simulator allows to investigate the impact of different organizational solutions: job duration, job working hours per year, extra shifts, workers for every single specialization, minimum and maximum number of resources to assign to each job, expediting procedures, etc.
The model allows simulating the concurrent plane production in the shop floor; each plane production is defined by a virtual PERT including all the operations of the whole assembling with its priorities, resources and current status.
This research includes the development of a neural model devoted to forecast the delivery date of a specific airplane, which is affected by a huge range of variability, based on its status in an intermediate moment of its production; the artificial neural network (ANN) was designed based on experimental results and available biography as a Back Propagation feed-forward full connected; the ANN was implemented by using a software package (Neuralware Pro); by this approach it was possible to demonstrate the potential of this technique that provided pretty good predictive capabilities: at the 50% from completion the real delivery date was estimated by the ANN with an average error of 3% (maximum error 8%).
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