Research on Lean Planning and Optimization for Precast Component Production Based on Discrete Event Simulation

Abstract

Component factories are experiencing the problems associated with lean production, especially the accuracy of production time prediction and the unnecessary waste in terms of time and resource utilization. In order to solve these problems, a discrete event simulation- (DES-) based lean planning and optimization method for precast component production is proposed by integrating the complexity assessment (CS), discrete event simulation (DES), and lean management (LM). The method includes three submodels: improved production planning, DES, and lean analysis and optimization. In the submodel of improved production planning, a complexity evaluation index system for precast components is established through investigating five component factories, consulting seven domain experts and analysing relevant literature. In the submodel of DES, the DES technique is adopted to simulate and analyse the production activities of precast components. The submodel of lean analysis and optimization provides multidimensional analysis, comparative analysis, and suggestions. Finally, a detailed production case is selected to simulate and test the proposed method. The important findings are as follows: (1) this method can minimize the difference between the processing time of each workstation to avoid bottleneck stations as much as possible; (2) this method can capture the uncertainty during precast component production, and the most likely production time calculated by the method is 12.05 hours instead of the 11.50 hours originally estimated by the component factory; (3) this method can identify some unnecessary waste in the production process of precast components, including less than 50% utilization of workstations and unnecessary equipment purchases; (4) this method also provides some suggestions regarding production optimization. Due to the particularity of precast component production, it further expands the boundary of lean production methodology from the perspective of the construction industry rather than the manufacturing industry. The proposed method assists component factories in planning and optimizing the precast component production when they make detailed production plans.

Lean-offsite-simulation nexus for housing construction: a state-of-the-art review of the existing knowledge

“…The review of the concept lean, offsite and simulation shows their potential in improving production process in a project. In practice, the lean approach exposes the non-value-adding activities from the production process (Heravi and Firoozi, 2017;Yuan et al, 2020), while the offsite approach support standardisation of the process with improved quality (Hermes, 2015) and the simulation approach support visualisation of the production system (Arashpour et al, 2016;Goh and Goh, 2019) All of these show that the three techniques support process improvement. However, there is limited evidence on how the three approaches are applied concurrently to deliver housing projects.…”

Integration of discrete event simulation with other modeling techniques to simulate construction engineering and management: an overview

“…Applying discrete event simulation only Fabrication of products (Bohács et al, 2016;Brodetskaia et al, 2013;Cheng and Tran, 2016;Golzarpoor et al, 2017;Heravi and Firoozi, 2017;Hu et al, 2018;Hu and Mohamed, 2014;Khan et al, 2017;Lau et al, 2014;Sneha and Tezeswi, 2016; Planning and scheduling (Al-Emran et al, 2010;Alvanchi et al, 2012a;Alvanchi et al, 2012c;Beiert et al, 2010;Dallasega et al, 2019;Gonzalez and Echaveguren, 2012;Hassan et al, 2016;Jung et al, 2016;Lee et al, 2019;Lindhard et al, 2019;Osman et al, 2017;Sadeghi et al, 2015;Shin et al, 2011;Yuan et al, 2020;Zhang et al, 2012) Productivity (Afifi et al, 2020;Arashpour and Arashpour 2015;Arashpour et al, 2014;Aziz et al, 2017;Baniassad et al, 2018;Khanh and Kim, 2020;Kisi et al, 2017;Montaser and Moselhi, 2014) Construction processes (Akhavian and Behzadan, 2018;Akhavian and Behzadan, 2014;Athigakunagorn and Limsawasd 2020;Chen et al, 2020a;Corona-Suarez et al, 2014;Fayed and Azeldin, 2018;Feng et al, 2018a;Feng e...…”

Reduction of the Cycle Time in the Biopsies Diagnosis Through a Simulation Based on the Box Müller Algorithm

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