Flow shop scheduling of hybrid make-to-stock and make-to-order in a distributed precast concrete production system

Prefabrication is not only economically optimal but also environmentally sustainable; hence, it is the future of construction. Furthermore, mass customization is the future of prefabrication. In the mass customization era, the production paradigm in precast concrete (PC) factories will inevitably sh...

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Bibliographic Details
Main Authors: Chen, Chen, Han, Jinchi, Liu, Ziwen, Tiong, Robert Lee Kong
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/170020
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Institution: Nanyang Technological University
Language: English
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Summary:Prefabrication is not only economically optimal but also environmentally sustainable; hence, it is the future of construction. Furthermore, mass customization is the future of prefabrication. In the mass customization era, the production paradigm in precast concrete (PC) factories will inevitably shift toward a blend of make-to-order (MTO) and make-to-stock (MTS) from the current MTO dominant model. Because PC factories have more motivation to fulfill MTO orders to secure profit, a hierarchical scheduling mechanism of MTO first and MTS second seems reasonable. Meanwhile, an emerging trend of expanding factories overseas is observed in the PC industry, especially in land-scarce countries like Singapore. Given additional resources, the new multiple-factory production network leads to relatively relaxed MTO due dates, which makes the proposed hierarchical scheduling mechanism more sensible. However, finding an optimal production plan for hybrid MTO and MTS on multiple production lines is not easy. There is an assignment problem in addition to the scheduling problem. Both the assignment problem and the scheduling problem are in the class of combinatorial optimization problems (COPs). Given that the use of meta-heuristics for solving complex COPs is a rapidly growing research topic, this paper employs meta-heuristic methods to solve the two problems simultaneously. Comparisons between the genetic algorithm and the whale optimization algorithm are made. Through the computational evaluation of a test case, the performance and effectiveness of the proposed methods are verified.