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In dynamic lot sizing problems, proper inventory control and production decisions are crucial to achieve a balance between customer demand satisfaction and cost management. In the stochastic lot sizing where the planner needs to ensure that a service level is satisfied, the objective is to minimize the total expected cost while the decisions are subject to specific demand fulfillment criteria. In this talk, we propose a more general setting where, in addition to the individual service level constraints, an aggregate service level over several products is also imposed. In addition, we present a receding horizon implementation of the proposed formulations which can be effectively used in a dynamic environment to overcome some of the inherent limitations of static models. Computational experiments are conducted to analyze the impact of aggregate service levels and the receding horizon implementation of the proposed formulations as opposed to standard service levels imposed on individual items and static models.