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

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

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Bicriteria train scheduling for high-speed passenger railroad planning applications

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2005年第3期167卷 752-771页

作者： Zhou, XS Zhong, M Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA Univ Maryland Maryland Transportat Initiat College Pk MD 20742 USA Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA

This paper is concerned with a double-track train scheduling problem for planning applications with multiple objectives. Focusing on a high-speed passenger rail line in an existing network, the problem is to minimize both (1) the expected waiting times for high-speed trains and (2) the total travel times of high-speed and medium-speed trains. By applying two practical priority rules, the problem with the second criterion is decomposed and formulated as a series of multi-mode resource constrained project scheduling problems in order to explicitly model acceleration and deceleration times. A branch-and-bound algorithm with effective dominance rules is developed to generate Pareto solutions for the bicriteria scheduling problem, and a beam search algorithm with utility evaluation rules is used to construct a representative set of non-dominated solutions. A case study based on Beijing-Shanghai high-speed railroad in China illustrates the methodology and compares the performance of the proposed algorithms. (C) 2004 Elsevier B.V. All rights reserved.

关键词： scheduling multiple objective programming transportation railway

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

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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

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Constrained multi-objective optimization of compact microwave circuits by design triangulation and pareto front interpolation

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期299卷 302-312页

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Reykjavik Univ Engn Optimizat & Modeling Ctr IS-102 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

Development of microwave components is an inherently multi-objective task. This is especially pertinent to the design closure stage, i.e., final adjustment of geometry and/or material parameters carried out to improve the electrical performance of the system. The design goals are often conflicting so that the improvement of one normally leads to a degradation of others. Compact microwave passives constitute a representative case: reduction of the circuit footprint area is detrimental to electrical figures of merit (e.g., the operating bandwidth). Identification of the best available trade-off designs requires multiobjective optimization (MO). This is a computationally expensive task, especially when executed at the level of full-wave electromagnetic (EM) simulation. The computational complexity issue can be mitigated through the employment of surrogate modeling methods, yet their application is limited by a typically high nonlinearity of system responses, and the curse of dimensionality. In this paper, a novel technique for fast MO of compact microwave components is proposed, which allows for sequential rendition of the trade-off designs using triangulation of the already available Pareto front as well as rapid refinement algorithms. Our methodology is purely deterministic;in particular, it does not rely on population-based nature-inspired procedures. The three major benefits are low computational cost, possibility of handling explicit design constraints, and a capability of producing a visually uniform representation of the Pareto front. The algorithm is demonstrated using a compact branch-line coupler and a three-section impedance matching transformer. In both cases, considerable savings are obtained over the benchmark, here, the state-of-the-art surrogate-assisted MO technique.

关键词： multiple objective programming Microwave design Simulation -based design Triangulation Pareto front interpolation

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Network design approach for hub ports-shipping companies competition and cooperation

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2013年 48卷 1-18页

作者： Asgari, Nasrin Farahani, Reza Zanjirani Goh, Mark Univ Portsmouth Dept Math Logist & Management Math Grp Portsmouth PO1 2UP Hants England Univ Kingston Kingston Business Sch Dept Informat & Operat Management London England Univ S Australia Sch Management Adelaide SA 5001 Australia Natl Univ Singapore NUS Business Sch Singapore 117548 Singapore

In the maritime industry, the stakeholders comprising the port authorities, shipping companies, and port operators often compete and collaborate within an ecological system. This paper investigates the competition and cooperation strategies amongst three parties: two major container hub ports and the shipping companies. This research develops a game theoretic network design model which considers three scenarios: (i) perfect competition between the hub ports, (ii) perfect cooperation between the hub ports, and (iii) cooperation between the shipping companies and the hub ports as a whole. The scenarios are tested using empirical data from two leading Asian hub ports: Singapore and Hong Kong. An interval branch and bound is designed to solve the models. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： Maritime transport Container port Network design multiple objective programming

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A multi-objective resource allocation problem in PERT networks

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2006年第3期172卷 838-854页

作者： Azaron, A Katagiri, H Sakawa, M Kato, K Memariani, A Hiroshima Univ Grad Sch Engn Dept Artificial Complex Syst Engn Higashihiroshima 7398527 Japan Bu Ali Sina Univ Dept Ind Engn Hamadan Iran

We develop a multi-objective model for resource allocation problem in PERT networks with exponentially or Erlang distributed activity durations, where the mean duration of each activity is a non-increasing function and the direct cost of each activity is a non-decreasing function of the amount of resource allocated to it. The decision variables of the model are the allocated resource quantities. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the total direct costs of the project (to be minimized), the mean of project completion time (min), the variance of project completion time (min), and the probability that the project completion time does not exceed a certain threshold (max). The surrogate worth trade-off method is used to solve a discrete-time approximation of the original problem. (c) 2005 Elsevier B.V. All rights reserved.

关键词： multiple objective programming project management and scheduling optimal control Markov processes

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

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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

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Representation and management of MOEA populations based on graphs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2004年第1期159卷 52-65页

作者： Alberto, I Mateo, PM Univ Zaragoza Fac Math Dept Stat Methods E-50009 Zaragoza Spain

Until now, in the literature, little attention has been paid to the storage and handling of populations of multiobjective evolutionary algorithms (MOEAs). In this work, we present a new tool for representing and managing populations of MOEAs by means of the use of graphs that keep the information on the relations among the individuals of the population. In the paper, we establish the theoretical basis of this sort of graph. In addition, we develop algorithms for its construction and updating (addition and removal of individuals in the population), analyzing their theoretical complexities. We also study several aspects of their practical behaviour including storage requirements, time needed for the construction and the management of these graphs. Finally, we present a selection process time comparison with and without the proposed methodology. (C) 2003 Elsevier B.V. All rights reserved.

关键词： evolutionary computation multiple objective programming graph theory heuristics

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Multiobjective nondominated neighbor coevolutionary algorithm with elite population

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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

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

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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

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