检索结果-内蒙古大学图书馆

A hybrid solution method for the bi-objective traffic counting location problem using previous origin-destination trip tables

引用

OPTIMIZATION AND ENGINEERING 2023年第4期24卷 2693-2725页

作者： Camara, Marcus Vinicius Oliveira Vieira, Bruno Salezze Ferrari, Thayse Quadros, Saul Germano Rabello Mattos Ribeiro, Glaydston Fed Univ Rio de Janeiro UFRJ Alberto Luiz Coimbra Inst Grad Sch & Res Engn Transportat Engn Program Rio De Janeiro Brazil Fed Univ Sao Paulo UNIFESP Postgrad Program Operat Res Aeronaut Technol Inst Sao Paulo Brazil Vertex Univ Ctr Univertix Civil Engn Program Tres Rios Brazil

Highway monitoring with traffic counting stations can provide data for the transporta-tion planning such as the origin-destination (O-D) trip tables. These O-D trip tables are important in the process of estimating traffic flow on the highways, indicating where new investments are required. This paper presents a hybrid solution method for the Bi-objective Traffic Couting Location Problem (BTCLP) considering previous trip tables. The BTCLP minimizes the number of counting stations located and maxi-mizes the coverage of the O-D trips. The concept of coverage of trips between an O-D pair considers that a user can use different paths given a maximum deviation of the shortest path. The hybrid solution combines strategies from the e-Constraint method with an existing Partial Set Covering Framework and can be used as exact or heuris-tic approach. We explore scenarios considering different limits for deviations from shortest path for 26 real instances based on the Brazilian transportation road network. Our computational experiments show that the hybrid solution method provides good solutions for large-sized instances.

关键词： multiple objective programming Traffic Counting Location Problem (TCLP) Bi-objective optimization Real-world problem O-D trip table

来源：评论

学校读者我要写书评

暂无评论

An exact penalty on bilevel programs with linear vector optimization lower level

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2009年第1期197卷 36-41页

作者： Ankhili, Z. Mansouri, A. Univ Cadi Ayyad Dept Math Fac Sci Semlalia MAD Marrakech Morocco

We are interested in a class of linear bilevel programs where the upper level is a linear scalar optimization problem and the lower level is a linear multi-objective optimization problem. We approach this problem via an exact penalty method. Then, we propose an algorithm illustrated by numerical examples. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Bilevel programming Linear programming multiple objective programming Penalty methods

来源：评论

学校读者我要写书评

暂无评论

The self regulation problem as an inexact steepest descent method for multicriteria optimization

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第3期235卷 494-502页

作者： Bento, G. C. Cruz Neto, J. X. Oliveira, P. R. Soubeyran, A. Univ Fed Goias IME BR-74001970 Goiania Go Brazil Fed Piaui Teresina DM BR-64049500 Teresina PI Brazil Univ Fed Rio de Janeiro COPPE Sistemas BR-21945970 Rio De Janeiro Brazil Aix Marseille Univ GREQAM AMSE Marseille France

In this paper we study an inexact steepest descent method for multicriteria optimization whose step-size comes with Armijo's rule. We show that this method is well-defined. Moreover, by assuming the quasi-convexity of the multicriteria function, we prove full convergence of any generated sequence to a Pareto critical point. As an application, we offer a model for the Psychology's self regulation problem, using a recent variational rationality approach. (C) 2014 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Steepest descent Self regulation Quasi-convexity

来源：评论

学校读者我要写书评

暂无评论

On simulation and optimization of one natural gas industry system under the rough environment

引用

EXPERT SYSTEMS WITH APPLICATIONS 2010年第3期37卷 1854-1862页

作者： Xu, Jiuping Dong, Rentao Wu, Desheng Dash Reykjavik Univ IS-103 Reykjavik Iceland Univ Toronto RiskLab Toronto ON M5S 3G3 Canada Sichuan Univ Uncertainty Decis Making Lab Chengdu 610064 Peoples R China Sichuan Univ Sch Publ & Adm Chengdu 610064 Peoples R China

Industry planning is a complex decision making problem involving various criteria under dynamic situations. This paper studies the natural gas industry planning where natural gas is used to produce chemical products in the upper course of the Yangtze River in China. We identify the main uncertainty factors that affecting planning, and then model and analyze them using rough set, dynamic system and multiple objective programming theories. Thus, we develop system dynamic-rough multiple objective programming (SD-RMOP) models to plan and develop natural gas industry operations in this region. We then carry out a simulation experiment using optimized parameters from SD-RMOP. We compare performance from different models and show how decision-making is improved by the insight the model provides. (C) 2009 Published by Elsevier Ltd.

关键词： Natural gas industry planning Rough set theory System dynamics multiple objective programming Simulation

来源：评论

学校读者我要写书评

暂无评论

Multi-objective reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm

引用

COMPUTERS & OPERATIONS RESEARCH 2009年第5期36卷 1562-1571页

作者： Azaron, Amir Perkgoz, Cahit Katagiri, Hideki Kato, Kosuke Sakawa, Masatoshi Dalhousie Univ Dept Ind Engn Halifax NS Canada Hiroshima Univ Grad Sch Engn Dept Artificial Complex Syst Engn Hiroshima Japan

A genetic algorithm approach is used to solve a multi-objective discrete reliability optimization problem in a k dissimilar-unit non-repairable cold-standby redundant system. Each unit is composed of a number of independent components with generalized Erlang distributions arranged in a series-parallel configuration. There are multiple component choices with different distribution parameters available for being replaced with each component of the system. The objective of the reliability optimization problem is to select the best components, from the set of available components, to be placed in the standby system in order to minimize the initial purchase cost of the system, maximize the system MTTF (mean time to failure), minimize the system VTTF (variance of time to failure) and also maximize the system reliability at the mission time. Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed GA approach. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： Reliability optimization Genetic algorithm multiple objective programming Markov processes Graph theory

来源：评论

学校读者我要写书评

暂无评论

On operators and search space topology in multi-objective flow shop scheduling

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第1期181卷 195-206页

作者： Geiger, Martin Josef Univ Hohenheim Lehrstuhl Industriebetriebslehre D-70593 Stuttgart Germany

Multi-objective optimization using evolutionary algorithms identifies Pareto-optimal alternatives or their close approximation by means of a sequence of successive local improvement moves. While several successful applications to combinatorial optimization problems are known, studies of underlying problem structures are still scarce. The paper presents a study of the problem structure of multi-objective permutation flow shop scheduling problems and investigates the effectiveness of local search neighborhoods within an evolutionary search framework. First, small problem instances with up to six objective functions for which the optimal alternatives are known are studied. Second, benchmark instances taken from literature are investigated. It turns out for the investigated data sets that the Pareto-optimal alternatives are found relatively concentrated in alternative space. Also, it can be shown that no single neighborhood operator is able to equally identify all Pareto-optimal alternatives. Taking this into consideration, significant improvements have been obtained by combining different neighborhood structures into a multi-operator search framework. (c) 2006 Elsevier B.V. All rights reserved.

关键词： multiple objective programming flow shop scheduling local search evolutionary computations

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimization for stochastic computer networks using NSGA-II and TOPSIS

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第3期218卷 735-746页

作者： Lin, Yi-Kuei Yeh, Cheng-Ta Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan

Network reliability is a performance indicator of computer/communication networks to measure the quality level. However, it is costly to improve or maximize network reliability. This study attempts to maximize network reliability with minimal cost by finding the optimal transmission line assignment. These two conflicting objectives frustrate decision makers. In this study, a set of transmission lines is ready to be assigned to the computer network, and the computer network associated with any transmission line assignment is regarded as a stochastic computer network (SCN) because of the multistate transmission lines. Therefore, network reliability means the probability to transmit a specified amount of data successfully through the SCN. To solve this multiple objectives programming problem, this study proposes an approach integrating Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). NSGA-II searches for the Pareto set where network reliability is evaluated in terms of minimal paths and Recursive Sum of Disjoint Products (RSDP). Subsequently, TOPSIS determines the best compromise solution. Several real computer networks serve to demonstrate the proposed approach. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Reliability multiple objective programming Stochastic computer network Non-dominated sorting genetic algorithm II Technique for order preference by similarity to ideal solution

来源：评论

学校读者我要写书评

暂无评论

A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第3期261卷 1028-1051页

作者： Liu, Ruochen Li, Jianxia Fan, Jing Mu, Caihong Jiao, Licheng Xidian Univ Int Collaborat Joint Lab Intelligent Percept & Co Minist EducInt Res Ctr Intelligent Percept & Com Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China

In real-world applications, there are many fields involving dynamic multi-objective optimization problems (DMOPs), in which objectives are in conflict with each other and change over time or environments. In this paper, a modified coevolutionary multi-swarm particle swarm optimizer is proposed to solve DMOPs in the rapidly changing environments (denoted as CMPSODMO). A frame of multi-swarm based particle swarm optimization is adopted to optimize the problem in dynamic environments. In CMPSODMO, the number of swarms (PSO) is determined by the number of the objective functions, and all of these swarms utilize an information sharing strategy to evolve cooperatively. Moreover, a new velocity update equation and an effective boundary constraint technique are developed during evolution of each swarm. Then, a similarity detection operator is used to detect whether a change has occurred, followed by a memory based dynamic mechanism to response to the change. The proposed CMPSODMO has been extensively compared with five state-of-the-art algorithms over a test suit of benchmark problems. Experimental results indicate that the proposed algorithm is promising for dealing with the DMOPs in the rapidly changing environments. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Dynamic multi-objective optimization Coevolution Multi-swarm Particle swarm optimization Similarity detective

来源：评论

学校读者我要写书评

暂无评论

Solving a bi-objective Transportation Location Routing Problem by metaheuristic algorithms

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第1期234卷 25-36页

作者： Abril Martinez-Salazar, Iris Molina, Julian Angel-Bello, Francisco Gomez, Trinidad Caballero, Rafael Dept Ind & Syst Engn Nuevo Leon Mexico Univ Malaga Fac Econ Dept Appl Econ Math E-29071 Malaga Spain

In this work we consider a Transportation Location Routing Problem (TLRP) that can be seen as an extension of the two stage Location Routing Problem, in which the first stage corresponds to a transportation problem with truck capacity. Two objectives are considered in this research, reduction of distribution cost and balance of workloads for drivers in the routing stage. Here, we present a mathematical formulation for the bi-objective TLRP and propose a new representation for the TLRP based on priorities. This representation lets us manage the problem easily and reduces the computational effort, plus, it is suitable to be used with both local search based and evolutionary approaches. In order to demonstrate its efficiency, it was implemented in two metaheuristic solution algorithms based on the Scatter Tabu Search Procedure for Non-Linear Multiobjective Optimization (SSPMO) and on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) strategies. Computational experiments showed efficient results in solution quality and computing time. (C) 2013 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Transportation Location Routing Problem Logistics Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

Multiobjective nondominated neighbor coevolutionary algorithm with elite population

引用

SOFT COMPUTING 2015年第5期19卷 1329-1349页

作者： Mu, Caihong Jiao, Licheng Liu, Yi Li, Yangyang Xidian Univ Int Res Ctr Intelligent Percept & Computat Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shannxi Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

A nondominated neighbor coevolutionary algorithm (NNCA) with a novel coevolutionary mechanism is proposed for multiobjective optimization, where elite individuals are used to guide the search. All the nondominated individuals are divided into two subpopulations, namely, the elite population and the common population according to their crowding-distance values. The elite individual located in less-crowded region will have more chances to select more team members for its own team and thus this region can be explored more sufficiently. Therefore, the elite population will guide the search to the more promising and less-crowded region. Secondly, to avoid the 'search stagnation' situation which means that algorithms fail to find enough nondominated solutions, a size guarantee mechanism (SGM) is proposed for elite population by emigrating some dominated individuals to the elite population when necessary. The SGM can prevent the algorithm from searching around limited nondominated individuals and being trapped into the 'search stagnation' situation. In addition, several different kinds of crossover and mutation operator are used to generate offspring, which are benefits for the diversity property. Tests on 20 multiobjective optimization benchmark problems including five ZDT problems, five DTLZ problems and ten unconstrained CEC09 test problems show that NNCA is very competitive compared with seven the state-of-the-art multiobjective optimization algorithms.

关键词： multiple objective programming Evolutionary computations Coevolutionary algorithm Elite population

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：