Optimal Planning for the Logistic System of Modular Construction through Multi-Stage Stochastic Programming

Conference paper : The 11th International Conference on City Logistics (CITY LOGISTICS 2019)

Abstract

The modular construction method has been adopted extensively by the construction sector for pursuing higher building quality and better project efficiency. However, the employment of this new construction method has not only altered the definition of construction supply chain, but also poses new challenges to the logistics system which has conventionally focused on raw material transportation. This challenge is exacerbated in the transport and inventory aspects when the project is executed in the urban settings, owing to the frequent traffic congestion, crowded environment, as well as the bulkiness and delicacy of finished modules. This study develops a multi-stage stochastic programming model for identifying the optimal supply chain configuration for the modular construction method. Site demand is considered to be stochastic, forcing the project managers to make several operational decisions at multiple time points during project execution. The developed model can provide the best production, transportation and inventory plans, as well as the most favourable initial inventory preparation schemes. Furthermore, we have proven that the implementation of multi-stage stochastic programming model can yield more economical and risk-averse solutions than the two-stage stochastic programming approach.

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Leo Hsu
Post-Doctoral Researcher

Recently obtained PhD from the Dyson School of Design Engineering. Currently focusing on the Demand Responsive Transport Systems.

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Panagiotis Angeloudis
Associate Professor

Associate Professor in Transport Systems and Logistics, with a passion for CS, OR and their role in transforming transportation.