https://doi.org/10.24928/2023/0186
Predictive Simulation for Automated Decision-Support in Production Planning and Control
Timson Yeung1, Jhonattan Guillermo Martinez Ribón2, Li-Or Sharoni3, Rafael Sacks4 & Tomi Pitkäranta5
1 Doctoral Candidate, Seskin Virtual Construction Laboratory, Technion – IIT, Haifa, Israel, [email protected], https://orcid.org/0000-0002-2195-08012 Postdoctoral Researcher, Seskin Virtual Construction Laboratory, Technion – IIT, Haifa, Israel, [email protected], https://orcid.org/0000-0001-6715-4440
3M.Sc. Student, Seskin Virtual Construction Laboratory, Technion – IIT, Haifa, Israel, [email protected], https://orcid.org/0000-0002-2831-3290
4Professor, Head of National Building Research Institute, Seskin Virtual Construction Laboratory, Technion – IIT, Haifa, Israel, [email protected], https://orcid.org/0000-0001-9427-5053
5Head of Concept and Partnerships, Sitedrive Oy, Helsinki, Finland, [email protected], https://orcid.org/0000-0003-1100-0578
Abstract
Production system design, planning and control are limited both by the incomplete situational awareness of planners and by their inability to predict the range of possible outcomes of their planning and control decisions. With the development of information technologies for monitoring products and processes on construction sites, it is increasingly possible to provide detailed status information describing the as-built products ‘as-built’ and processes ‘as-performed’. This opens the door to applying predictive analytics to provide decision-makers with frequent predictions of the outcomes for a range of changes they might contemplate to the production system design, even during construction. Within the BIM2TWIN project, we are designing and implementing an agent-based simulation engine that is a core component of an Automated Decision Support System. Currently, the simulation can be calibrated to accurately predict the range of likely project durations for a residential construction project. However, certain aspects of the trade crews’ performance, particularly with respect to the completion of tasks, appear to differ from the behaviours described by industry experts and encapsulated in the crew agent behaviour tree in the simulation.
Keywords
Production system design, production planning and control, agent-based simulation, decisionsupport.
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Reference
Yeung, T. , Ribón, J. G. M. , Sharoni, L. , Sacks, R. & Pitkäranta, T. 2023. Predictive Simulation for Automated Decision-Support in Production Planning and Control, Proceedings of the 31st Annual Conference of the International Group for Lean Construction (IGLC31) , 1279-1290. doi.org/10.24928/2023/0186
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