Ready Mixed Concrete Production and Truck Dispatching Planning When RMC Mixer Is Breakdown Unexpectedly under Stochastic Travel Times

• Main Authors: Kung-Wen Chung, 張恭文
• Other Authors: Shang-Yao Yan
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/63912073161242063709

碩士 === 國立中央大學 === 土木工程研究所 === 97 === The production of ready mixed concrete (RMC) has been automatic and computerized, but the RMC production scheduling and truck dispatching are still manually determined with staff experience. Consequently, the resulting solution, though feasible, could possibly be inferior. Yan and Lai (2007) and Yan et al. (2008) have respectively developed a deterministic model that combines RMC production scheduling and truck dispatching in the same framework, for general and overtime conditions. However, in actual operations many stochastic incidents may occur to disturb the original plan. For example, if an RMC plant is broken down, then both the RMC production scheduling and truck dispatching would be affected. Moreover, the travel time between an RMC plant and a construction site is usually stochastic (particularly with a certain distribution) in practice, which could affect the RMC production scheduling and truck dispatching. Therefore, in this research we focus on the RMC production and truck schedule adjustment problem under stochastic travel time when an RMC mixer is broken down unexpectedly to develop a suitable model from the system optimization perspective. The model is expected to be useful planning tool for carriers to decide on their optimal RMC production scheduling and truck dispatching in their operations. We employ time-space network flow techniques to formulate the RMC truck flows and the RMC production in the dimension of time and space, coupled with suitable side constaints comply real operating requirements, to develop the model. The model is formulated as an integer network flow problem with side constraints, which is characterized as NP-hard and is difficult be be optimally solved in a reasonable time for large-scale problems. In order to efficiently solve large-scale problems occurring in real world, we develop a solution algorithm. To evaluate the model and solution algorithms, we perform a case s`tudy using real operating data from a Taiwan RMC firm. The test results show that the model and the solution algorithm are good and could be useful references for carriers to handle RMC productin and truck schedule perturbations under stochastic travel times.