Open AccessPlanning semi-automated precast production using GA
Author(s) -
Chen Chen,
Thomas C. H. Phang,
Lee Kong Tiong
Publication year - 2020
Publication title -
international journal of industrialized construction
Language(s) - English
Resource type - Journals
ISSN - 2563-5034
DOI - 10.29173/ijic215
Subject(s) - precast concrete , tardiness , production planning , production line , production (economics) , scheduling (production processes) , schedule , computer science , job shop scheduling , industrial engineering , real time computing , engineering , operations research , operations management , civil engineering , operating system , mechanical engineering , economics , macroeconomics
Although fully automated production systems have been developed and used in some industry leaders, most of the precast factories have yet to be developed to that stage. Semi-automated production lines are still popularly used. As production productivity can be maximally improved within the physical constraints by applying a sound production plan, this paper tends to propose a production planning method for the semi-automated precast production line using genetic algorithm (GA). The production planning problem is formulated into a flexible job shop scheduling problem (FJSSP) model and solved using an integrated approach. Thanks to the development of new technologies such as building information modeling (BIM) platform and radio frequency identification (RFID), implementation of a just-in-time (JIT) schedule in the semi-automated precast production line becomes practicable on the grounds of risk mitigation and enhanced demand forecast capability. In this regard, the optimization objectives are minimum makespan, station idle time, and earliness and tardiness penalty. An example was applied to validate the integrated GA approach. The experimental results show that the developed GA approach is a useful and effective method for solving the problem that it can return high-quality solutions. This paper thus contributes to the body of knowledge new precast production planning method for practical usage.