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multi-objective genetic algorithm for berth allocation problem considering daytime preference

COMPUTERS & INDUSTRIAL ENGINEERING 2015年 89卷 2-14页

作者： Hu, Zhi-Hua Shanghai Maritime Univ Logist Res Ctr Shanghai 200135 Peoples R China

Maximization of operational efficiency and minimization of cost are pursued by terminal operators, whereas daytime preference is increasingly emphasized by governments, terminal operators and workers. Daytime preference in berth allocation schedule refers to schedule the workloads in nights as fewer as possible, which improves working comfort, safety, and green and energy-savings degrees, but may decrease the throughput and total operational efficiency. By extending existing dynamic discrete berth allocation model, a bi-objective model considering daytime preference is established to minimize the delayed workloads and the workloads in nights. Based on the well known NSGA-II algorithm, a multi-objective genetic algorithm (moGA) is developed for solving the bi-objective model by using a two-part representation scheme. The sensitivities of the algorithmic parameters and tradeoffs between daytime preference and delayed workloads are analyzed by numerical experiments. The algorithmic aspects of the proposed approach and the effects of daytime preference on solutions are all examined. Finally, the managerial implications are discussed. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Berth allocation problem Dynamic discrete berth allocation problem Shiftwork multi-objective genetic algorithm Maritime terminal

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multi-objective genetic algorithm-based sample selection for partial least squares model building with applications to near-infrared spectroscopic data

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APPLIED SPECTROSCOPY 2006年第6期60卷 631-640页

作者： Shinzawa, Hideyuki Li, Boyan Nakagawa, Takehiro Maruo, Katsuhiko Ozaki, Yukihiro Matsushita Elect Works Ltd NBT Project Kadoma Osaka 5718686 Japan

In this study, multi-objective genetic algorithms (GAs) are introduced to partial least squares (PLS) model building. This method aims to improve the performance and robustness of the PLS model by removing samples with systematic errors, including outliers, from the original data. multi-objective GA optimizes the combination of these samples to be removed. Training and validation sets were used to reduce the undesirable effects of over-fitting on the training set by multi-objective GA. The reduction of the over-fitting leads to accurate and robust PLS models. To clearly visualize the factors of the systematic errors, an index defined with the original PLS model and a specific Pareto-optimal solution is also introduced. This method is applied to three kinds of near-infrared (NIR) spectra to build PLS models. The results demonstrate that multi-objective GA significantly improves the performance of the PLS models. They also show that the sample selection by multi-objective GA enhances the ability of the PLS models to detect samples with systematic errors.

关键词： partial least squares PLS multi-objective genetic algorithm GA sample selection least median squares LMedS systematic error near-infrared spectroscopy NIR spectroscopy

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multi-objective genetic algorithm for Economic Statistical Design of the T² Control Chart with Variable Sample Size: The Updated Markov Chain Approach

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JOURNAL OF TESTING AND EVALUATION 2018年第3期46卷 1209-1219页

作者： Seif, Asghar Bu Ali Sina Univ Dept Stat Shahid Fahmideh Blvd Hamadan Iran

T-2 control charts are used to primarily monitor the mean vector of quality characteristics of a process. Recent studies have shown that using variable sample size (VSS) schemes results in charts with more statistical power for detecting small to moderate shifts in the process mean vector. In this study, we have presented a multiple-objective economic statistical design of VSS T-2 control chart with the adjusted average time to signal (AATS) as the statistical objective and the expected cost per hour as the economic objective. Then, a multi-objective genetic algorithm for economic statistical design is proposed for identifying the Pareto optimal solutions of control chart design. Through an illustrative example, the advantages of the proposed approach are shown by providing a list of viable optimal solutions and graphical representations, which indicate the advantage of flexibility and adaptability of our approach.

关键词： economic statistical design Hotelling's T-2 control chart multi-objective genetic algorithm variable sample size

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multi-objective genetic algorithm for voltage stability enhancement using rescheduling and FACTS devices

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AIN SHAMS ENGINEERING JOURNAL 2014年第3期5卷 789-801页

作者： Roselyn, J. Preetha Devaraj, D. Dash, Subhransu Sekhar SRM Univ Chennai Tamil Nadu India Kalasalingam Univ Srivilliputhur India

This paper presents the application of multi-objective genetic algorithm to solve the Voltage Stability Constrained Optimal Power Flow (VSCOPF) problem. Two different control strategies are proposed to improve voltage stability of the system under different operating conditions. The first approach is based on the corrective control in contingency state with minimization of voltage stability index and real power control variable adjustments as objectives. The second approach involves optimal placement and sizing of multi-type FACTS devices, Static VAR Compensator and Thyristor Controlled Series Capacitor along with generator rescheduling for minimization of voltage stability index and investment cost of FACTS devices. A fuzzy based approach is employed to get the best compromise solution from the trade off curve to aid the decision maker. The effectiveness of the proposed VSCOPF problem is demonstrated on two typical systems, IEEE 30-bus and IEEE 57 bus test systems. (C) 2014 Production and hosting by Elsevier B.V. on behalf of Ain Shams University.

关键词： Generation rescheduling Voltage stability multi-objective genetic algorithm Fuzzy decision making FACTS

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multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced battery/supercapacitor hybrid energy storage

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ENERGY 2018年 163卷 351-363页

作者： Abdelkader, Abbassi Rabeh, Abbassi Ali, Dami Mohamed Mohamed, Jemli Univ Tunis High Natl Engn Sch Tunis ENSIT Engn Lab Ind Syst & Renewable Energies LISIER 5 Ave Taha HusseinPOB 56 Tunis 1008 Tunisia Univ Tunis High Natl Engn Sch Tunis ENSIT LaTICE Lab 5 Ave Taha HusseinPOB 56 Tunis 1008 Tunisia Univ Hail Dept Elect Engn Coll Engn POB 2440 Hail Saudi Arabia

The present paper proposes a new approach to optimize the sizing of a multi-source PV/Wind with Hybrid Energy Storage System (HESS). Hence, a developed modeling of all sub-systems composing the integral system has been designed to establish the proposed optimization algorithm. Besides, a frequency management based on Discrete Fourier Transform (DFT) algorithm has been also used to distribute the power provided by the power supply system into different dynamics. Thus, many frequency channels have been obtained in order to divide the roles of each storage device and show the impact of integrating fast dynamics into renewable energies based applications. The reformulation of our optimization problem is considered by the minimization of the Total Cost of Electricity (TCE) and the Loss of Power Supply Probability (LPSP) of the load, simultaneously. In this respect, a multi-objective based genetic algorithm approach was used to size the developed system considering all storage dynamics. In order to achieve an optimal system configuration, different economic analysis cases were established. The obtained results show that the minimum of LPSP is achieved according to a very low TCE which introduces that the exploitation of renewable energy has a very important effect to promote the energy sector in Tunisia. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Renewable energy sources multi-objective genetic algorithm Optimization Sizing Hybrid energy storage system Economic analysis

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multi-objective genetic algorithm for hybrid electric vehicle parameter optimization

Multi-objective genetic algorithm for hybrid electric vehicl...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Huang, Bufu Wang, Zhancheng Xu, Yangsheng Chinese Univ Hong Kong Dept Automat & Comp Aided Engn Shatin Hong Kong Peoples R China

ISBN: (纸本)9781424402588

As a typical multi-objective optimization problem, parameter optimization of HEV power control strategy must deal with the conflict between objectives, as fuel consumption and emissions. Classical methods define the HEV parameter optimization as a single objective problem to minimize the fuel consumption. In this paper, the multi-objective genetic algorithm (MOGA) is generalized for parameter optimization of power control strategy of series hybrid electric vehicle. Using a single unified formulation, a number of design objectives can be simultaneously optimized through searching in the parameter space. Compared with two main strategies, as Thermostatic and single-objective genetic algorithm (SOGA), the computation procedures of MOGA are discussed. Simulation results based on the model of series hybrid electric vehicle illustrate the optimization validity of MOGA.

关键词： multi-objective genetic algorithm power control strategy series hybrid electric vehicle parameter optimization

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multi-objective genetic algorithm to mitigate the composite problem of total transfer capacity, voltage stability and transmission loss minimization

Multi-objective genetic algorithm to mitigate the composite ...

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39th North American Power Symposium

作者： Ramana, N. Venkata Chandrasekar, K. JNT Univ Coll Engn Dept Elect & Elect Engn Kakinada India JNT Univ Coll Engn Dept Elect & Elect Engn Hyderabad Andhra Pradesh India

ISBN: (纸本)9781424417254

Enhancement in total transfer capacity, Voltage stability and minimization of transmission tine losses are treated as an interrelated, coupled composite problem. A multi-objective genetic algorithm is presented to mitigate this multi-objective and multi-criteria type composite problem. Simulation test is carried on the standard EEEE WSCC 3-Generator, 9-Bus system. The results indicate significant improvement in the three problems as well as economical justification is achieved through the application of the algorithm. The algorithm suggests using both SVC and TCSC at their optimal locations has significant impact in dealing the problem.

关键词： total transfer capacity multi-objective genetic algorithm TCSC SVC repeated power flow

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multi-objective genetic algorithm for multistage-based job processing schedules in FMS environment

Multi-objective genetic algorithm for multistage-based job p...

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IEEE International Conference on Control and Automation

作者： Kim, KwanWoo Lee, DongJoo Jeong, In-Jae Hanyang Univ Dept Ind Engn Seoul 133791 South Korea

ISBN: (纸本)9781424408177

In this paper, we propose a multi-objective genetic algorithm for effectively solving multistage-based job processing schedules in FMS environment. The proposed method is random-weight approach to obtaining a variable search direction toward Pareto solution. The objectives are to minimize the makespan and the total flow time, simultaneously. The feasibility and adaptability of the proposed moGA are investigated through experimental results.

关键词： multistage-based job processing schedule multi-objective genetic algorithm FMS

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multi-objective genetic algorithm for the automated planning of a wireless sensor network to monitor a critical facility

Multi-objective genetic algorithm for the automated planning...

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Conference on Sensors and Command, Control, Communications and Intelligence (C31) Technologies for Homeland Security and Homeland Defense III

作者： Jourdan, DB de Weck, OL MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

ISBN: (纸本)0819453269

This paper examines the optimal placement of nodes for a Wireless Sensor Network (WSN) designed to monitor a critical facility in a hostile region. The sensors are dropped from an aircraft, and they must be connected (directly or via hops) to a High Energy Communication Node (HECN), which serves as a relay from the ground to a satellite or a high-altitude aircraft. The sensors are assumed to have fixed communication and sensing ranges. The facility is modeled as circular and served by two roads. This simple model is used to benchmark the performance of the optimizer (a multi-objective genetic algorithm, or MOGA) in creating WSN designs that provide clear assessments of movements in and out of the facility, while minimizing both the likelihood of sensors being discovered and the number of sensors to be dropped. The algorithm is also tested on two other scenarios;in the first one the WSN must detect movements in and out of a circular area, and in the second one it must cover uniformly a square region. The MOGA is shown again to perform well on those scenarios, which shows its flexibility and possible application to more complex mission scenarios with multiple and diverse targets of observation.

关键词： multi-objective genetic algorithm wireless sensor network network planning node placement facility monitor coverage

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multi-objective genetic algorithm for integrated process planning and scheduling with fuzzy processing time

Multi-objective genetic algorithm for integrated process pla...

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6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Wen, Xiaoyu Li, Xinyu Gao, Liang Wan, Liang Wang, Wenwen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9781467363433

Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. In real manufacturing environment, multi-objectives should be taken into consideration simultaneously during the machining process. Meanwhile, the processing time for each job is often imprecise in many real applications. Therefore, multi-objective integrated process planning and scheduling (IPPS) problem with fuzzy processing time is addressed in this paper. The processing time is described as triangular fuzzy number. A multi-objective genetic algorithm (MOGA) is designed to search for the Pareto solutions of multiobjective IPPS problem with fuzzy processing time. An instance has been designed to test the performance of proposed algorithm. The experiment result shows that the proposed MOGA could obtain satisfactory Pareto solutions for the multi-objective IPPS problem with fuzzy processing time.

关键词： Integrated process planning and scheduling multi-objective genetic algorithm fuzzy processing time

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