Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
System identification
Information distortion in a supply chain: the bullwhip effect
Management Science - Special issue on frontier research in manufacturing and logistics
Optimization: algorithms and consistent approximations
Optimization: algorithms and consistent approximations
Trust-region methods
An introduction to general systems thinking (silver anniversary ed.)
An introduction to general systems thinking (silver anniversary ed.)
Parallel Implementation of a Central Decomposition Method for Solving Large-Scale Planning Problems
Computational Optimization and Applications
A Network Design Problem for a Distribution System with Uncertain Demands
SIAM Journal on Optimization
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Dynamics of global supply chain supernetworks
Mathematical and Computer Modelling: An International Journal
Editorial: Topics in Real-time Supply Chain Management
Computers and Operations Research
Self-organized supply chain networks: theory in practice and an analog simulation based approach
Autonomics '08 Proceedings of the 2nd International Conference on Autonomic Computing and Communication Systems
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Supply Chain Management (SCM) is an important activity in all producing facilities and in many organizations to enable vendors, manufacturers and suppliers to interact gainfully and plan optimally their flow of goods and services. To realize this, a dynamic modelling approach for characterizing supply chain activities is opportune, so as to plan efficiently the set of activities over a distributed network in a formal and scientific way. The dynamical system will result so complex that it is not generally possible to specify the functional forms and the parameters of interest, relating outputs to inputs, states and stochastic terms by experiential specification methods. Thus the algorithm that will presented is Data Driven, determining simultaneously the functional forms, the parameters and the optimal control policy from the data available for the supply chain. The aim of this paper is to present this methodology, by considering dynamical aspects of the system, the presence of nonlinear relationships and unbiased estimation procedures to quantify these relations, leading to a nonlinear and stochastic dynamical system representation of the SCM problem. Moreover, the convergence of the algorithm will be proved and the satisfaction of the required statistical conditions demonstrated. Thus SCM problems may be formulated as formal scientific procedures, with well defined algorithms and a precise calculation sequence to determine the best alternative to enact. A "Certainty equivalent principle" will be indicated to ensure that the effects of the inevitable uncertainties will not lead to indeterminate results, allowing the formulation of demonstrably asymptotically optimal management plans.