Stochastische Lagerpolitiken

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A multi-level inventory system with a make-to-order supplier

We study a supply network comprising a factory, a central warehouse using a discrete-time reorder point-reorder quantity policy and multiple regional distribution centres applying discrete-time base-stock policies under consideration of a fill rate constraint. The warehouse is supplied from the factory, which has a limited capacity and which follows a make-to-order strategy. The complete system is analysed by decomposition into three layers that are linked through layer-specific random replenishment lead times. An overall optimisation model including as decision variables the processing time in the factory and the parameters of the inventory policies applied in the warehouse and the distribution centres is formulated and solved

A column generation heuristic for dynamic capacitated lot sizing with random demand under a fill rate constraint

This paper deals with the dynamic multi-item capacitated lot-sizing problem under random period demands (SCLSP). Unfilled demands are backordered and a fill rate constraint is in effect. It is assumed that, according to the static-uncertainty strategy of Bookbinder and Tan [1], all decisions concerning the time and the production quantities are made in advance for the entire planning horizon regardless of the realization of the demands. The problem is approximated with the set partitioning model and a heuristic solution procedure that combines column generation and the recently developed ABC[beta] heuristic is proposed.Production planning and control Heuristics Inventory control

Capacitated lot sizing with parallel machines, sequence-dependent setups, and a common setup operator

In this paper, we consider a special variant of a capacitated dynamic lot sizing problem which, despite its practical relevance, has not been treated sufficiently in the literature. In addition to the significant complexity introduced by the sequence dependency of setup times, the problem is further complicated by the fact that there is only one single setup operator which is responsible for all setups on all machines. Hence, the multi-machine problem cannot be decomposed into multiple single machine problems, as the setup operations must be coordinated among the different machines. In addition, lots are produced in batches whereby the processing time of a lot is a step-wise function of the number of batches per lot. Due to perishability and quarantine issues, time windows for the production are given. We formulate a big-bucket lot sizing model and apply MIP-based heuristics to two industrial data sets