Investigation of Ready Mixed Concrete Transportation Problem Using Linear Programming and Genetic Algorithm
Abstract
Ready-mixed concrete (RMC) is one of the most common building material for construction industry for nearly all developed and developing countries. Generally, because of the technical requirements, concrete must be mixed in a batch plant and transported to the construction site. There are two important factors affected the cost of RMC: raw material cost and transportation cost. Additionally, transportation cost is also included when determining the unit price of RMC. However, profitability affects adversely in the case of long distance between the plant and construction site. For these reason, distribution of RMC from supply to the demand points with minimum cost is aimed in this study. This work contributes to both modelling and dispatching of RMC as an optimization problem by applying linear and heuristic methods. For this purpose, as an example, an urban area which divided into 7 districts and contained 4 concrete batch plants is discussed. Linear programming and genetic algorithm were applied to solve this problem and compared each other under the same conditions. The result shows linear programming is more efficient for this application because of the limited constraints and variables.
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References
Feng, C. W., Cheng, T. M. and Wu, H. T., Optimizing the schedule of dispatching RMC trucks through genetic algorithms, Automation in Construction, 13 (3). (2004), 327–340.
Lu, M. and Lam, H. C., Optimized concrete delivery scheduling using combined simulation and genetic algorithms, Proceedings of the 37th conference on Winter Simulation Conference. Orlando. USA, 2005.
Feng, C. W. and Wu, H. T., Integrating fmGA and CY-CLONE to optimize the schedule of dispatching RMC trucks, Automation in Construction, 15 (2), (2006), 186–199.
Graham, L. D., Forbes, D. R. and Smith, S. D., Modeling the ready mixed concrete delivery system with neural networks, Automation in Construction, 15 (5), (2006), 656–663.
Naso, D., Surico, M., Turchiano, B. and Kaymak, U., Genetic algorithms for supply-chain scheduling: a case study in the distribution of ready-mixed concrete, European Journal of Operational Research, 177 (3), (2007), 2069–2099.
Yan, S., Lai, W. and Chen, M., Production scheduling and truck dispatching of ready mixed concrete, Transportation Research. 44 (1). (2008), 164–179.
Lin, P. C., Wang, J., Huang, S. H. and Wang, Y. T., Dispatching ready mixed concrete trucks under demand postponement and weight limit regulation, Automation in Construction, 19, (2010), 798–807.
Maghrebi, M. and Waller, S. T., Assessing the accuracy of expert-based decisions in the dispatching of ready mixed concrete, Journal of Construction Engineering and Management, 140(6), (2014), 06014004-1 - 06014004-7.
Aditya, S., Nideesh, K. T. and Guruprasad, N., Solving the transportation problem for warehouses and dealers in Bangalore City, World Academy of Science, Engineering and Technology Mathematical and Computational Sciences, 2(1), (2015).
Chandwani, V., Agrawal, V. and Nagar, R., Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks, Expert Systems with Applications, 42(2), (2015), 885-893.
Holland, J., Adoption in natural and artificial systems, Ann Arbor, MI: The University of Michigan Press, 1975.
Albayrak, G. and Albayrak, U., Optimization of the ready-mixed concrete delivery system using transportation algorithm, CRC Press, Engineering Optimization IV, The Book Chapter of Numerical Optimization Techniques, 2015, 159-165.
DOI: 10.28991/cej-2016-00000052
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Copyright (c) 2016 Gulcag Albayrak, Ugur Albayrak
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