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The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with multi-objective and multi-constraint

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Journal of Donghua University(English Edition) 2007年第4期24卷 455-459,466页

作者：李蓓智周亚勤兰世海杨建国 College of Mechanical Engineering Donghua University

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

关键词： modern logistics vehicle scheduling routing optimization multi-objective multi-constraint biologic immunity information modeling

Research on multi-objective Real-Time Optimization of Automatic Train Operation(ATO) in Urban Rail Transit

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Journal of Shanghai Jiaotong university(Science) 2018年第2期23卷 327-335页

作者： HE Tong XIONG Ruiqi School of Traffic and Transportation Lanzhou Jiaotong University Lanzhou 730070 China

The determination and optimization of Automatic Train Operation(ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II(NSGA-II) is used to solve the model, and the optimal speed curve of train running is ***, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.

关键词： urban rail transit multi-objective Automatic Train Operation(ATO) Non-dominated Sorting Genetic Algorithm II(NSGA-II) fuzzy predictive controller

Downlink Resource Allocation with multi-objective Optimization in OFDMA Systems

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Journal of Harbin Institute of Technology(New Series) 2015年第1期22卷 68-72页

作者： Zhengyu Song Shujuan Hou Siliang Wu School of Information and Electronics Beijing Institute of Technology

A downlink radio resource allocation algorithm is proposed for orthogonal frequency division multiple access( OFDMA) systems. The resource allocation problem about system throughput and user fairness is formulated based on the multi-objective optimization theory. Then the optimality conditions are derived,according to which a joint subcarrier and power allocation algorithm is proposed. The simulation results show that the proposed algorithm can dynamically achieve arbitrary levels of compromise between throughput and fairness by adjusting the weighting coefficient,outperforming some static algorithms. In comparison,the classic maximum rate algorithm( MRA),max-min algorithm and proportional fairness( PF) algorithm can only achieve tradeoff in a certain level and are all special cases of the proposed algorithm.

关键词： OFDMA resource allocation multi-objective optimiza

multi-objective Fuzzy Optimization Algorithm for Separation-Recycle System

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Chinese Journal of Chemical Engineering 2004年第2期12卷 221-226页

作者：孙力樊希山姚平经 School of Chemical Engineering Dalian University of Technology Dalian 116012 China

Separation-recycle system is an important part in chemical process, and its optimization is a multiobjective problem. In this paper the process optimization procedure is proposed. The fuzzy optimization algorithm with the concept of relative importance degree (RID) is utilized to transfer multi-objective optimization (MO-O) model into a single-objective optimization (SO-O) framework. The treatment of process condensate in synthesisa mmonia plant is taken as example to illustrate the optimization procedure, and the satisfactory result demonstrates feasibility and effectiveness of the suggested method.

关键词： multi-objective fuzzy optimization relative importance degree

Solving Service Selection Problem Based on a Novel multi-objective Artificial Bees Colony Algorithm

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Journal of Shanghai Jiaotong university(Science) 2017年第4期22卷 474-480页

作者：黄利萍张斌苑勋张长胜高岩 Software College Northeastern University School of Computer Science and Engineering Northeastern University Institute of Information Science & Engineering Shenyang Ligong University

Service computing is a new paradigm and has been widely used in many fields. The multi-objective service selection is a basic problem in service computing and it is non-deterministic polynomial (NP)-hard. This paper proposes a novel multi-objective artificial bees colony (n-MOABC) algorithm to solve service selection problem. A composite service instance is a food source in the algorithm. The fitness of a food source is related to the quality of service (QoS) attributes of a composite service instance. The search strategy of the bees are based on dominance. If a food source has not been updated in successive maximum trial (Max Trial) times, it will be abandoned. In experiment phase, a parallel approach is used based on map-reduce framework for n-MOABC algorithm. The performance of the algorithm has been tested on a variety of data sets. The computational results demonstrate the effectiveness of our approach in comparison to a novel bi-ant colony optimization (NBACO) algorithm and co-evolution algorithm. © 2017, Shanghai Jiaotong University and Springer-Verlag GmbH Germany.

关键词： novel multi-objective artificial bees colony(n-MOABC) multi-objective service selection search strategy

Emission-Reductive and multi-objective Coordinative Optimization of Binary Feed for Atmospheric and Vacuum Distillation Unit

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China Petroleum Processing & Petrochemical Technology 2017年第4期19卷 101-112页

作者： Huang Xiaoqiao Zhao Tianlong Li Na Ma Zhanhua Song Lijuan Li Jun State Key Laboratory of Heavy Oil Processing China University of Petroleum Qingdao 266580 Petro China Fuel Oil Company Limited Research Institute Beijing 100195 Liaoning Shihua University Fushun 113001

A genetic algorithm based multi-objective coordinative optimization strategy is developed to optimize the operation of a binary feed atmospheric and vacuum distillation system, in which the objective functions cover the economic benefit, the furnace energy consumption and the CO_2 emissions, and meanwhile the simultaneous effect of binary feed composition is also investigated. A cross-call integration of software is developed to implement the optimization algorithm,and once the maximum economic benefit, the minimum furnace energy consumption and the minimum CO_2 emissions are obtained, the Pareto-optimal solution set is worked out, with the practical problems of the refinery being solved. The optimization result shows that under the same furnace energy consumption and the CO_2 emissions as the existing working condition, the economic benefit still allows for a considerable potential of increment by adjusting the heavy oil proportion of the binary feed crude oil.

关键词： multi-objective optimization atmospheric and vacuum distillation system genetic algorithm CO2 emissions binary feed composition

Biogeography-based multi-objective optimization algorithm with hybrid migration and applying in ethylene cracking process 36

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Biogeography-based multi-objective optimization algorithm wi...

Non-dominated sorting culture differential evolution algorithm for multi-objective optimal operation of Wind-Solar-Hydro complementary power generation system

第36届中国控制会议

作者： Zheming Huang Rongbin Qi East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical ProcessMinistry of Education

ISBN: (纸本)9781538629185

The Ethylene cracking is highly nonlinear, complex process with many constraints because of the complex running state of cracking furnace groups. For the solutions to multi-objective problems of yield, cost and benefits, this paper puts forward a biogeography-based multi-objective optimization algorithm with hybrid migration（BBMOHM）. The hybrid migration strategy combines the self-adaptive migration with the simplex operator and the replication migration, which strengthens the convergence and the search ability, to avoid falling into local optimum. For the goal of utilizing the dominance information between individuals, this algorithm adopts the migration model of dominance degree to assist migration strategies improving the performance of the algorithm. Finally, experiments about the ZDT test function and the multi-objective optimization problem of the ethylene cracking process is verified the BBMOHM achieves good performance on convergence and distribution.

关键词： biogeography-based optimization multi-objective self-adaptive migration ethylene cracking optimization

来源： cnki会议评论

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Global Energy Interconnection 2019年第4期2卷 368-374页

作者： Guanjun Liu Hui Qin Rui Tian Lingyun Tang Jie Li School of Hydropower and Information Engineering Huazhong University of Science and Technology

Due to the intermittency and instability of Wind-Solar energy and easy compensation of hydropower, this study proposes a Wind-Solar-Hydro power optimal scheduling model. This model is aimed at maximizing the total system power generation and the minimum ten-day joint output. To effectively optimize the multi-objective model, a new algorithm named non-dominated sorting culture differential evolution algorithm(NSCDE) is proposed. The feasibility of NSCDE was verified through several well-known benchmark problems. It was then applied to the Jinping Wind-Solar-Hydro complementary power generation system. The results demonstrate that NSCDE can provide decision makers a series of optimized scheduling schemes.

关键词： Wind-Solar-Hydro complementary power generation system Scheduling strategy multi-objective optimization Culture algorithm

Uncertain and multi-objective programming models for crop planting structure optimization

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Frontiers of Agricultural Science and Engineering 2016年第1期3卷 34-45页

作者： Mo LI Ping GUO Liudong ZHANG Chenglong ZHANG Centre for Agricultural Water Research in China China Agricultural University College of Water Conservancy and Hydropower and Architecture Engineering Yunnan Agricultural University

Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro fits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study,three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming(IFCCP) model and an inexact fuzzy linear programming(IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimizationtheory-based fuzzy linear multi-objective programming model was developed, which is capable of re flecting both uncertainties and multi-objective. In addition, a multiobjective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic bene fits and the denominator representing minimum crop planting area allocation. These models better re flect actual situations,considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in MinqinCounty, north-west China. The advantages, the applicable conditions and the solution methods of each model are exp

关键词： crop planting structure optimization model uncertainty multi-objective