Solving General Dynamic Lot Sizing Problem by Means of Stochastic Heuristic Methods.

Solving General Dynamic Lot Sizing Problem by Means of Stochastic Heuristic Methods.

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This paper deals with a dynamic multi-level multi-item lot sizing problem in a general production-assembly structure represented by a directed acyclic network, where each node may have several predecessors and/or successors. We assume a finite planning horizon consisting of discrete time periods, known deterministic demands at each time period, dynamic lot sizes, and time-varying cost parameters. The objective is to minimize the sum of setup, production, and inventory holding costs over the planning horizon. We study the possibilities of solving this problem by means of genetic algorithm, simulated annealing, and tabu search.

This paper deals with a dynamic multi-level multi-item lot sizing problem in a general production-assembly structure represented by a directed acyclic network, where each node may have several predecessors and/or successors. We assume a finite planning horizon consisting of discrete time periods, known deterministic demands at each time period, dynamic lot sizes, and time-varying cost parameters. The objective is to minimize the sum of setup, production, and inventory holding costs over the planning horizon. We study the possibilities of solving this problem by means of genetic algorithm, simulated annealing, and tabu search.

Dynamic lot sizing, general product structure, stochastic heuristic methods.

DVOŘÁK, J., MARTINEK, V., PALÍNEK, K.

05.06.2001

Slovak University of Technology Bratislava

Bratislava

80-227-1542-5

Proceedings of the 13th International Conference on PROCESS CONTROL ‘01

86/1

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