Integrated scheduling of ready-mixed concrete production and delivery |
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| Affiliation: | 1. School of Automation, Huazhong University of Science and Technology, Wuhan, China;2. Key Laboratory of Education Ministry for Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan, China;3. CISDI Chongqing Information Technology Co., Ltd., Chongqing, China;4. Department of Industrial & Systems Engineering, Lehigh University, PA, USA;1. Computing in Engineering, Ruhr-Universität Bochum, Universitätstraße 150, 44801 Bochum, Germany;2. IT in Mechanical Engineering, Ruhr-Universität Bochum, Universitätstraße 150, 44801 Bochum, Germany;1. Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada;2. Department of Systems Design Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada;1. School of Civil and Environmental Engineering, University of New South Wales (UNSW), Sydney, Australia and Department Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Iran;2. School of Civil and Environmental Engineering, University of New South Wales (UNSW), Sydney, Australia;3. School of Computer Science Engineering, University of New South Wales (UNSW), Sydney, Australia |
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| Abstract: | This study presents an approach for improving the operations of production and delivery in ready-mixed concrete (RMC) plants. A network flow method is applied to formulate the integrated scheduling problem of ready-mixed concrete production and delivery with trucks and pumps, where the demands of construction sites are in certain time windows. A method is developed that applies a genetic algorithm in which the chromosome consists of three sequences (construction sites, delivery order and vehicle IDs); operators work on the sequences of construction sites. The approach is evaluated by simulation of real cases. Comparison with combinations of other priority rules for scheduling production and vehicles demonstrates the effectiveness of the genetic algorithm. Sensitivity analysis reveals the effects of the fleet size of an available vehicle, the cost rates and the time windows of construction sites. The model and algorithm may be helpful for practical integrated operations for operation management at RMC plants. |
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