inEARTHMOVING PRODUCTIVITY ESTIMATION USING GENETIC ALGORITHM
Shawki, K., Abd EL-Razek, M., Abdulla, N. (2009). inEARTHMOVING PRODUCTIVITY ESTIMATION USING GENETIC ALGORITHM. EKB Journal Management System, 37(No 3), 593-604. doi: 10.21608/jesaun.2009.126488
K. M. Shawki; M. E. Abd EL-Razek; N. Abdulla. "inEARTHMOVING PRODUCTIVITY ESTIMATION USING GENETIC ALGORITHM". EKB Journal Management System, 37, No 3, 2009, 593-604. doi: 10.21608/jesaun.2009.126488
Shawki, K., Abd EL-Razek, M., Abdulla, N. (2009). 'inEARTHMOVING PRODUCTIVITY ESTIMATION USING GENETIC ALGORITHM', EKB Journal Management System, 37(No 3), pp. 593-604. doi: 10.21608/jesaun.2009.126488
Shawki, K., Abd EL-Razek, M., Abdulla, N. inEARTHMOVING PRODUCTIVITY ESTIMATION USING GENETIC ALGORITHM. EKB Journal Management System, 2009; 37(No 3): 593-604. doi: 10.21608/jesaun.2009.126488

inEARTHMOVING PRODUCTIVITY ESTIMATION USING GENETIC ALGORITHM

JES. Journal of Engineering Sciences
Article 6, Volume 37, No 3, May and June 2009, Page 593-604  XML PDF (574.93 K)
Document Type: Research Paper
DOI: 10.21608/jesaun.2009.126488
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Authors
K. M. Shawki1; M. E. Abd EL-Razek2; N. Abdulla3
1Assistant Professor , construction and building engineering Dept. Collage of Engineering and Technology Arab Academy for Science , Technology and Martine Transport -Alexandria –Egypt
2Head of construction and building engineering Dept. Collage of Engineering and Technology Arab Academy for Science, Technology and Martine Transport Cairo Egypt
3Post graduate student construction and building engineering Dept. Collage of Engineering and Technology Arab Academy for Science, Technology and Martine Transport Alexandria- Egypt.
Abstract
This paper presents a framework for optimizing earth moving operations using computer simulation and genetic algorithms (GA) as an optimizer. The optimization aims at maximizing production of an earth moving fleet consists of an excavator and trucks as hauling units. The objective function considers the variables that influence the production of earth moving operations such as rolling resistance, grade resistance, vehicle weight, payload, horse power..etc. The constraints of the objective function are considered such as speed limits, payload capacity, etc. A sizing problem is considered to tune the GA parameters such as selection, crossover, population size, mutation, etc. Numerical examples are presented using developed software called "FLEET PRODUCTION" to illustrate the practical features of the proposed software and to demonstrate its capacities in selecting optimum fleet configurations. "FLEET PRODUCTION" is designed to assist engineers and contractors to select the best fleet combination of hoe and haulers that can complete an earth moving operation with maximum production.
Keywords
Genetic Algorithms; Construction equipment; Productivity
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