Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 323
Lotsizing and scheduling comprises activities which have to be done repeatedly within MRP-systems. We consider the proportional (multi-item, capacitated, dynamic) lotsizing and scheduling problem which is more general than the discrete lotsizing and scheduling problem as well as the continuous setup lotsizing problem. A simple stochastic backward algorithm with biased sampling via randomized regrets is presented. We partition the parameter space of the stochastic algorithm and choose subspaces via sequential analysis based on hypothesis testing. The new ideas provided in this paper, i.e. the randomized-regrets-based backward algorithm as well as the controlled search via sequential analysis, have three important properties: They are simple, effective, and rather general. Computational results are presented as well.
Lotsizing and scheduling backward algorithm / local search biased sampling via randomized regrets / Monte Carlo Simulation hypothesis testing / sequential analysis