TY  - CONF
TI  - An Application of Artificial Intelligence Planner for Bespoke Precast Concrete Production Planning: A Case Study
C1  - Sydney, Australia
C3  - 13th Annual Conference of the International Group for Lean Construction
SP  - 493
EP  - 499
PY  - 2005
AU  - Benjaoran, Vacharapoom
AU  - Dawood, Nashwan
AD  - Researcher, Centre for Construction Innovation and Research, University of Teesside, Middlesbrough, TS1 3BA, UK, +44 (0) 1642-342406, FAX +44 (0) 1642-342401, b. vacharapoom@tees.ac.uk
AD  - Professor, Centre for Construction Innovation and Research, University of Teesside, Middlesbrough, TS1 3BA, UK, +44 (0) 1642-342405, n.n.dawood@tees.ac.uk
AB  - Precast concrete manufacturers are highly involved in the construction industry through the supply of bespoke products. Their workload is a complex combination of different and unique designed products, which have various delivery dates. The production process from design to manufacturing is complicated and contains uncertainties due to many factors such as: multi-disciplinary design, progress on construction sites, and costly purpose-built moulds. Lean construction concepts aim to identify and reduce all forms of wastes in the construction process including its supply chains. An integrated, comprehensive planning system called Artificial Intelligence Planner (AIP) has been proposed to improve the efficiency of the process by targeting on the production planning as a significant impact to the success of the business. Artificial intelligent techniques are used in AIP to enhance data analyses and decision supports for production planning. A case study for the implementation was conducted on a real bespoke precast concrete manufacturer. The difference between AIP and this factory setting was attended. Data from the studied were reformatted and the AIP configuration was customized. Finally, the successful implementation has showed the adaptability and flexibility of AIP to the real production conditions, and it has given the improvement of the resulted production schedules. The anticipated outcomes are the shortened customer lead-time and the optimum factory’s resource utilization. These consequently make the construction process lean.
KW  - Bespoke precast concrete products
KW  - Production planning
KW  - Genetic algorithm
KW  - Neural network.
PB  - 
T2  - 13th Annual Conference of the International Group for Lean Construction
DA  - 2005/07/19
CY  - Sydney, Australia
L1  - http://iglc.net/Papers/Details/396/pdf
L2  - http://iglc.net/Papers/Details/396
N1  - Export Date: 20 April 2024
DB  - IGLC.net
DP  - IGLC
LA  - English
ER  -