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 Hội nghị quốc tế: Opposition Based Multiple Objective Differential Evolution (OMODE) for Time-Cost-Environment Impact Tradeoff on Construction Operationcvs weekly sale cvs print prescription savings cards
Tác giả hoặc Nhóm tác giả: Duc-Hoc Tran, Min-Yuan Chengcvs weekly sale shauneutsey.com prescription savings cardswalgreens pharmacy coupon site promo codes walgreens
Nơi đăng: International Symposium on Reliability Engineering and Risk Management 2014; Số: 0;Từ->đến trang: 1-5;Năm: 2014
Lĩnh vực: Kỹ thuật; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
Current competitive conditions in the construction market require that construction companies satisfy customer needs using increasingly tight project budgets. The key indicators of success currently used on most construction projects include meeting project duration, cost, and quality targets. Project decision makers seldom consider customer expectations related to project-related environmental impact. Tradeoff optimization among project duration (time), project cost, and project environmental impact is necessary to enhance overall construction project benefit. This study develops a novel optimization algorithm, the Opposition-based Multiple Objective Differential Evolution (OMODE), to solve the time-cost-environmental impact tradeoff (TCET) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. An application example is analyzed to demonstrate the ability of OMODE-generated non-dominated solutions and verify the efficiency and effectiveness of the proposed algorithm.cvs weekly sale shauneutsey.com prescription savings cards
ABSTRACT
Current competitive conditions in the construction market require that construction companies satisfy customer needs using increasingly tight project budgets. The key indicators of success currently used on most construction projects include meeting project duration, cost, and quality targets. Project decision makers seldom consider customer expectations related to project-related environmental impact. Tradeoff optimization among project duration (time), project cost, and project environmental impact is necessary to enhance overall construction project benefit. This study develops a novel optimization algorithm, the Opposition-based Multiple Objective Differential Evolution (OMODE), to solve the time-cost-environmental impact tradeoff (TCET) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. An application example is analyzed to demonstrate the ability of OMODE-generated non-dominated solutions and verify the efficiency and effectiveness of the proposed algorithm.
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