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2023 | OriginalPaper | Chapter

9. An MILP Model for the Lot-Sizing/Scheduling of Automotive Plastic Components with Raw Materials and Packaging Availability

Authors : E. Guzmán, B. Andres, R. Poler

Published in: Industry 4.0: The Power of Data

Publisher: Springer International Publishing

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Abstract

This paper examines the lot-sizing /scheduling problem for plastic automotive components manufacturing. The scenario in which the problem is tackled refers to a second-tier supplier in the automotive supply chain. Here, the studied second-tier supplier is characterized by transforming plastic granules in injection machines using specific moulds that produce components or finished products. Each mould can be set up on distinct machines to inject one same automobile component, or even two different components or more in the same mould. The same mould is assembled on different injection machines and can have distinct production rates subject to the machine on which it is set up. Our research work puts forward a mixed integer linear programming (MILP) model to minimize setup, the inventory of raw materials and plastic components, stockout, backorder costs, and machine-mould assignation costs. We demonstrate the usability of this model with randomly generated instances. The results of the experiments show that our MILP converges toward optimal solutions in large instances by reaching efficient solutions in reference to both quality and execution times. The novelty of this model lies in it considering the arrival of materials as raw material for the injection of parts into moulds, the use of raw materials and the availability of containers for packaging finished products. Moulds can also be set up only during specific time periods in accordance with the quantity of available labor during each time period.

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Metadata
Title
An MILP Model for the Lot-Sizing/Scheduling of Automotive Plastic Components with Raw Materials and Packaging Availability
Authors
E. Guzmán
B. Andres
R. Poler
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-29382-5_9

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