A Genetic Local Search Algorithm for Supply Planning of Two Level
Transcription
A Genetic Local Search Algorithm for Supply Planning of Two Level
Industrial Engineering and Computer Sciences Division (G2I) A SUPPLY PLANNING FOR TWO-LEVEL ASSEMBLY SYSTEMS WITH RANDOM LEAD TIMES : GENETIC ALGORITHM F. HNAIEN, X. DELORME, A. DOLGUI Avril 2008 RESEARCH REPORT 2008-500-002 G2I research reports are available in PDF format on the site Web of ENSM-SE Les rapports de recherche du Centre G2I de l'ENSM-SE sont disponibles en format PDF sur le site Web de l'Ecole www.emse.fr Centre G2I Génie Industriel et Informatique Division for Industrial Engineering and Computer Sciences (G2I) Par courier : By mail: Ecole Nationale Supérieure des Mines de Saint-Etienne Centre G2I 158, Cours Fauriel 42023 SAINT-ETIENNE CEDEX 2 France A Supply Planning for Two-Level Assembly Systems with Random Lead Times: Genetic Algorithm Faicel Hnaien, Xavier Delorme, Alexandre Dolgui* Industrial Engineering and Computer Science Centre (G2I) Ecole des Mines de St Etienne, 158, cours Fauriel, 42023 Saint Etienne, France [email protected], [email protected], [email protected] Abstract This paper examines supply planning for two level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. If the demand for the finished product is not delivered at the due date, a tardiness cost is incurred. In the same manner, a holding cost at each level appears if some components to assembly the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the lead time at each level is a random discrete variable. The expected cost is composed of the tardiness cost for finished product and the holding costs of components at levels 1 and 2. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. For this new problem, a genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms of solution quality, convergence and computation time is also investigated. Keywords: Assembly Systems, Stochastic Lead Times, Supply Planning, Genetic algorithm *corresponding author: Prof. A. Dolgui, Ph.:+33(0)477420166 Fax:+33(0)477426666 1 Ecole Nationale Supérieure des Mines de Saint-Etienne Centre G2I 158, Cours Fauriel 42023 SAINT-ETIENNE CEDEX 2 www.emse.fr