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Numrering	Referens	Omslagsbild	Hitta
1.	Fu, Jiali, et al. (författare) Optimizing fleet selection for earthmoving operations 2013 Ingår i: ISEC 2013 - 7th International Structural Engineering and Construction Conference. - : Research Publishing Services. - 9810753551 - 9789810753559 ; , s. 1261-1266 Konferensbidrag (refereegranskat)abstract Earthmoving operations often involve a large number of specially designed equipment with significant purchasing/leasing prices, high operating and maintenance costs. Hence, choosing the right fleet is a major concern from the construction planners' point of view. This paper presents a methodology that combines discrete-event simulation and optimization to solve the optimal fleet selection problem for earthmoving operations. Two optimization objectives are formulated and solved using the proposed framework and a genetic algorithm: minimization of Total Cost of Ownership (TCO) and maximization of productivity. Further, a two-stage rating scheme is introduced to arrange the fleet configurations so that the optimization algorithm converges to a fleet with better second-stage performance while the first-stage performance remains at the same level. The case study shows that the proposed mechanism can effectively allocate a local optimal equipment combination for earthmoving operations and hence serve as an efficient tool for construction management.

Fu, Jiali, et al. (författare)
Optimizing fleet selection for earthmoving operations
2013
Ingår i: ISEC 2013 - 7th International Structural Engineering and Construction Conference. - : Research Publishing Services. - 9810753551 - 9789810753559 ; , s. 1261-1266
Konferensbidrag (refereegranskat)abstract
- Earthmoving operations often involve a large number of specially designed equipment with significant purchasing/leasing prices, high operating and maintenance costs. Hence, choosing the right fleet is a major concern from the construction planners' point of view. This paper presents a methodology that combines discrete-event simulation and optimization to solve the optimal fleet selection problem for earthmoving operations. Two optimization objectives are formulated and solved using the proposed framework and a genetic algorithm: minimization of Total Cost of Ownership (TCO) and maximization of productivity. Further, a two-stage rating scheme is introduced to arrange the fleet configurations so that the optimization algorithm converges to a fleet with better second-stage performance while the first-stage performance remains at the same level. The case study shows that the proposed mechanism can effectively allocate a local optimal equipment combination for earthmoving operations and hence serve as an efficient tool for construction management.

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