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Production Planning under Uncertainties and Forecasts Updates

Zied Jemai (), Maxime Claisse () and Chengbin Chu
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Zied Jemai: LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec
Maxime Claisse: LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec
Chengbin Chu: LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec

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Abstract: —In this paper, we consider a single item single level production planning problem with uncertain and dynamic demand. The uncertainties are modelled through the probability density function of the demand. The system is studied in a rolling-horizon framework, in which data is updated at each iteration and makes the demand non-stationary. In this context, we model a cost minimization problem using a stochastic dynamic programming approach, which allows to integrate the update of the demand density into the optimization process. We calculate the optimal production quantities in a specific case where linear regression is the forecasting method used and the production lead-time is equal to one period. We finally run a simulation to illustrate the optimality of the solution, and to show its efficiency compared to others classical production planning procedures.

Date: 2016-04-06
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01672409
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Published in 3rd International Conference on Control, Decision and Information Technologies (CoDIT'16) , Apr 2016, Saint Julian's, Malta

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