Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization

Problem statement: This study proposed a systematic model of delivering Ready Mixed Concrete (RMC) that optimizes the schedule of dispatching RMC trucks. Approach: Firstly, the factors that impact the RMC delivery process are analyzed. Secondly, a model based on Bee Colony Optimization (BCO) was developed in order to find the best dispatching schedule that minimize the total waiting time of RMC trucks at construction sites. Results: To demonstrate its efficiency, the BCO algorithm was applied to solve two dispatching RMC problems. The results obtained from the BCO are compared to those achieved from the conventional approaches i.e., Genetic Algorithm (GA) and Tabu Search (TS) algorithm. Conclusion/Recommendations: The experimental results showed that the BCO approach can quickly generate efficient and flexible solutions to dispatch RMC trucks. Furthermore, the obtained results had higher quality solution efficiently and faster computational time than those obtained from the conventional approaches.

[1]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[2]  Uzay Kaymak,et al.  Genetic Algorithms in Supply Chain Scheduling of Ready-Mixed Concrete , 2004 .

[3]  Tarek Zayed,et al.  Resource Allocation for Concrete Batch Plant Operation: Case Study , 2004 .

[4]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Emile H. L. Aarts,et al.  Simulated annealing and Boltzmann machines - a stochastic approach to combinatorial optimization and neural computing , 1990, Wiley-Interscience series in discrete mathematics and optimization.

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[9]  Daniel W. Halpin,et al.  SIMULATION OF CONCRETE BATCH PLANT PRODUCTION , 2001 .

[10]  Chung-Wei Feng,et al.  An effective simulation mechanism for construction operations , 2003 .

[11]  Chung-Wei Feng,et al.  Optimizing the schedule of dispatching RMC trucks through genetic algorithms , 2004 .

[12]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[13]  J. A. Bland,et al.  Tabu search and design optimization , 1991, Comput. Aided Des..

[14]  Shangyao Yan,et al.  Production scheduling and truck dispatching of ready mixed concrete , 2008 .

[15]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[16]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[17]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[18]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  Ming Lu,et al.  HKCONSIM : A Practical Simulation Solution to Planning Concrete Plant Operations in Hong Kong , 2003 .