Home
Giới thiệu
Tài khoản
Đăng nhập
Quên mật khẩu
Đổi mật khẩu
Đăng ký tạo tài khoản
Liệt kê
Công trình khoa học
Bài báo trong nước
Bài báo quốc tế
Sách và giáo trình
Thống kê
Công trình khoa học
Bài báo khoa học
Sách và giáo trình
Giáo sư
Phó giáo sư
Tiến sĩ
Thạc sĩ
Lĩnh vực nghiên cứu
Tìm kiếm
Cá nhân
Nội dung
Góp ý
Hiệu chỉnh lý lịch
Thông tin chung
English
Đề tài NC khoa học
Bài báo, báo cáo khoa học
Hướng dẫn Sau đại học
Sách và giáo trình
Các học phần và môn giảng dạy
Giải thưởng khoa học, Phát minh, sáng chế
Khen thưởng
Thông tin khác
Tài liệu tham khảo
Hiệu chỉnh
Số người truy cập: 109,897,342
Integrating Chaotic Initialized Opposition Multiple-Objective Differential Evolution and Stochastic Simulation to Optimize Ready-Mixed Concrete Truck Dispatch Schedule
Tác giả hoặc Nhóm tác giả:
Min-Yuan Cheng
,
Duc-Hoc Tran
walgreens pharmacy coupon
site
promo codes walgreens
Nơi đăng:
Journal of Management in Engineering;
S
ố:
0;
Từ->đến trang
: 1-11;
Năm:
2015
Lĩnh vực:
Kỹ thuật;
Loại:
Bài báo khoa học;
Thể loại:
Quốc tế
TÓM TẮT
Delivering ready-mixed concrete (RMC) efficiently to construction sites is a practical concern and one of the most challenging tasks for RMC batch managers. Batch plant managers must consider both time and order factors in order to set an RMC truck dispatch schedule that successfully balances batch plant (supplier) and construction site (customer) priorities. This paper develops an optimization framework that integrates discrete event simulation (DES) and multiobjective differential evolution (MODE) to determine the solutions for RMC truck dispatch scheduling. The model takes into consideration uncertainties as well as unexpected situations such as truck breakdowns during delivery. In addition, the chaotic initialized opposition multiobjective differential evolution (COMODE) algorithm is used to optimize the dispatching schedule, which minimizes the total waiting duration both of RMC trucks at construction sites and of construction sites for trucks. A batch plant case study is used to illustrate the capability of the new DES-COMODE algorithm, with results showing that DES-COMODE-generated nondominated solutions can assist batch plant managers to set efficient truck dispatch schedules in a timely manner, a task both difficult and time-consuming using current methods. Results demonstrate that DES-COMODE is superior to four currently used algorithms, including the nondominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC) in terms of efficiency and effectiveness.
walgreens prints coupons
rx coupons printable
free printable coupons
ABSTRACT
Delivering ready-mixed concrete (RMC) efficiently to construction sites is a practical concern and one of the most challenging tasks for RMC batch managers. Batch plant managers must consider both time and order factors in order to set an RMC truck dispatch schedule that successfully balances batch plant (supplier) and construction site (customer) priorities. This paper develops an optimization framework that integrates discrete event simulation (DES) and multiobjective differential evolution (MODE) to determine the solutions for RMC truck dispatch scheduling. The model takes into consideration uncertainties as well as unexpected situations such as truck breakdowns during delivery. In addition, the chaotic initialized opposition multiobjective differential evolution (COMODE) algorithm is used to optimize the dispatching schedule, which minimizes the total waiting duration both of RMC trucks at construction sites and of construction sites for trucks. A batch plant case study is used to illustrate the capability of the new DES-COMODE algorithm, with results showing that DES-COMODE-generated nondominated solutions can assist batch plant managers to set efficient truck dispatch schedules in a timely manner, a task both difficult and time-consuming using current methods. Results demonstrate that DES-COMODE is superior to four currently used algorithms, including the nondominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC) in terms of efficiency and effectiveness.
© Đại học Đà Nẵng
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0236 3822 041 ; Email: dhdn@ac.udn.vn