On the use of Simheuristics to Optimize Safety-Stock Levels in Material Requirements Planning with Random Demands

Barry B. Barrios, Angel A. Juan, Javier Panadero, Klaus Altendorfer, Andreas J. Peirleitner, Alejandro Estrada-Moreno

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

2 Zitate (Scopus)

Abstract

Material requirements planning (MRP) integrates the planning of production, scheduling, and inventory activities in a manufacturing process. Many approaches to MRP management focus either on the simulation of the system (without considering optimization aspects) or in its optimization (without considering stochastic aspects). This paper analyzes a MRP version in which the demand of final products in each period is a random variable. The goal is then to find the optimal safety-stock configuration of both the product and the parts, i.e.: the configuration that minimizes the expected total cost. This total cost is given by: (i) the inventory cost; and (ii) a penalty cost generated by the occurrence of stock outs. To solve this stochastic optimization problem, a spreadsheet simulation model is proposed and a heuristic procedure is employed over it. A numerical example illustrates the main concepts of the proposed approach as well as its potential.

OriginalspracheEnglisch
TitelProceedings of the 2020 Winter Simulation Conference, WSC 2020
Redakteure/-innenK.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, R. Thiesing
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten1539-1550
Seitenumfang12
ISBN (elektronisch)9781728194998
DOIs
PublikationsstatusVeröffentlicht - 14 Dez. 2020
Veranstaltung2020 Winter Simulation Conference, WSC 2020 - Orlando, USA/Vereinigte Staaten
Dauer: 14 Dez. 202018 Dez. 2020

Publikationsreihe

NameProceedings - Winter Simulation Conference
Band2020-December
ISSN (Print)0891-7736

Konferenz

Konferenz2020 Winter Simulation Conference, WSC 2020
Land/GebietUSA/Vereinigte Staaten
OrtOrlando
Zeitraum14.12.202018.12.2020

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