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 Hybrid Artificial Intelligence Based on Evolutionary Approach in Optimizing Multiple Resources of Projects
Tác giả hoặc Nhóm tác giả: Thi Minh-Truc Huynh, Duc-Hoc Tran, Thi Thao-Nguyen Nguyen, Quynh Chau Truong
Nơi đăng: International organization of Scientific Research; ISSN (e): 2250-3021, ISSN (p): 2278-8719; Số: Vol. 06, Issue 06 (June. 2016);Từ->đến trang: PP 46-53;Năm: 2016
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Abstract: - Resource leveling problems are attractive and important part of construction project planning. Resource leveling is the process used within project scheduling to reduce fluctuations in resource usage over the period of project implementation. This study presents a new hybrid intelligence model, named as Artificial Bee Colony with Differential Evolution, to handle the multiple resources leveling in multiple projects problems (ABCDE-MRLMP). The proposed algorithm integrates crossover operation from differential evolution (DE) with original artificial bee colony (ABC) to balance exploration and exploitation phase of the optimization process. The ABCDE-MRLMP algorithm is compared with benchmark algorithms considered and previous findings using two construction case studies. The experimental results demonstrate the efficiency and effectiveness of the proposed model. The ABCDE-MRLMP is a promising alternative approach to handling resource leveling project scheduling problems. Keywords: - Multiple resources levelling, Scheduling, Artificial Bee Colony, Optimization, Differential Evolution, Construction management
ABSTRACT
Abstract: - Resource leveling problems are attractive and important part of construction project planning. Resource leveling is the process used within project scheduling to reduce fluctuations in resource usage over the period of project implementation. This study presents a new hybrid intelligence model, named as Artificial Bee Colony with Differential Evolution, to handle the multiple resources leveling in multiple projects problems (ABCDE-MRLMP). The proposed algorithm integrates crossover operation from differential evolution (DE) with original artificial bee colony (ABC) to balance exploration and exploitation phase of the optimization process. The ABCDE-MRLMP algorithm is compared with benchmark algorithms considered and previous findings using two construction case studies. The experimental results demonstrate the efficiency and effectiveness of the proposed model. The ABCDE-MRLMP is a promising alternative approach to handling resource leveling project scheduling problems. Keywords: -Multiple resources levelling, Scheduling, Artificial Bee Colony, Optimization, Differential Evolution, Construction management
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