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Multipollutant Air Quality Management Strategies: T-Sets Based optimization Technique Under Imprecise Environment

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2017年第6期19卷 1927-1939页

作者： Garai, Arindam Mandal, Palash Roy, Tapan Kumar Sonarpur Mahavidyalaya Dept Math Sonarpur 700149 WB India IIEST Dept Math Sibpur 711103 WB India

In this paper, technique to find Pareto optimal solutions to multiple objective optimization problems under imprecise environment is discussed. In 1976, Zimmermann described optimization technique under fuzzy environment. Jimenez and Bilbao (Fuzzy Sets Syst 150:2714-2721, 2009) showed that fuzzy efficient solutions may not be Pareto optimal solutions to multiple objective optimization problems in case that one of the fuzzy goals is fully achieved. Wu et al. (Fuzzy Optim Decis Mak 14:43-55, 2015) redefined membership functions of fuzzy set theory and proposed two-phase approach. But under imprecise environment, it is observed that the prime intention of maximizing up-gradation of most misfortunate is better served by removing some constraints that are obtained by applying existing fuzzy optimization technique in mathematical models. Further in existing fuzzy optimization technique, it is observed that membership functions are not utilized as per their definitions. Moreover, some constraints in existing fuzzy optimization technique may make a model infeasible. Consequently, in this paper, one new function viz. T-characteristic function is introduced to supersede membership function of fuzzy set, and subsequently, one new set viz. T-set is introduced to supersede fuzzy set for representing uncertainty. Then, one general algorithm has been developed to find Pareto optimal solutions to multiple objective optimization problems by applying newly introduced T-sets. One model on multipollutant air quality management strategies under imprecise environment illustrates the limitations of existing fuzzy optimization technique as well as advantages of using proposed algorithm. Finally, conclusions are drawn.

关键词： Air quality management Fuzzy mathematical programming multiple objective optimization Multipollutant control T-characteristic function T-set

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A multiple objective framework for planning accelerated life tests

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IEEE TRANSACTIONS ON RELIABILITY 2005年第1期54卷 58-63页

作者： Tang, LC Xu, K Natl Univ Singapore Dept Ind & Syst Engn Singapore 117576 Singapore

The article presents information related to accelerated life test (ALT). ALT is a widely used technique in industry to meet the resources, and time constraints, for revealing product weakness, and assessing the reliability of products. Some authors have considered test plans under some implicit cost constraints through the use of different censoring times at different levels of stresses, expected test time constraint, and obtaining the optimum number of test units. This article focuses on a framework with two conflicting objectives in the planning of ALT, i.e., meeting a desired level of statistical precision for an estimate of interest, and meeting a cost target for conducting the test.

关键词： goal programming multiple objective optimization planning accelerated testing

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Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm

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APPLIED SOFT COMPUTING 2010年第4期10卷 1096-1107页

作者： Ghoseiri, Keivan Ghannadpour, Seyed Farid Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA Iran Univ Sci & Technol Sch Railway Engn Tehran *** Iran

This paper presents a new model and solution for multi-objective vehicle routing problem with time windows (VRPTW) using goal programming and genetic algorithm that in which decision maker specifies optimistic aspiration levels to the objectives and deviations from those aspirations are minimized. VRPTW involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper uses a direct interpretation of the VRPTW as a multi-objective problem where both the total required fleet size and total traveling distance are minimized while capacity and time windows constraints are secured. The present work aims at using a goal programming approach for the formulation of the problem and an adapted efficient genetic algorithm to solve it. In the genetic algorithm various heuristics incorporate local exploitation in the evolutionary search and the concept of Pareto optimality for the multi-objective optimization. Moreover part of initial population is initialized randomly and part is initialized using Push Forward Insertion Heuristic and lambda-interchange mechanism. The algorithm is applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances. Results show that the suggested approach is quiet effective, as it provides solutions that are competitive with the best known in the literature. (C) 2010 Elsevier B. V. All rights reserved.

关键词： Vehicle routing problem with time windows (VRPTW) Goal programming (GP) Genetic algorithm multiple objective optimization Pareto ranking

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Multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric bus based on stein soft actor-3-critic

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ENERGY 2024年 307卷

作者： Zhou, Jiaxuan Peng, Jiankun Wu, Jingda Wei, Zhongbao Fan, Yi Guo, Xin Southeast Univ Sch Transportat Nanjing 211102 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

Eco-driving strategies increasingly emphasize enhancing multi-performance including safety, comfort, durability, and traffic efficiency under optimal energy-saving. This paper introduces a multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric buses (FCHEBs) based on the stein soft actor-3-critic (SSA3C) algorithm. By leveraging the correlation between the international roughness index (IRI) and weighted root mean square acceleration (WRMSA), energy management system (EMS) and adaptive cruise control (ACC) are integrated the to enhancing multi-performance including vertical comfort and energy efficiency. To address the insufficient action representation and overestimation of Q-values of traditional soft actor-critic (SAC) algorithm, improvements are made to the actor and critic networks using stein variational gradient descent (SVGD) and clipped triple Q-learning (3C), respectively. Composite cycles integrating the standard cycle and IRI are developed to enhance the strategy's generalization. Experimental results indicate that the proposed eco-driving strategy, while maintaining dynamics, safety, durability, and efficiency, efficiently achieves 98.1 % energy savings and equivalent comfort level as dynamic programming (DP). Ablation experiments on vertical comfort demonstrate improvements of 1.24% in energy efficiency and 2.94% in WRMSA. Finally, the effectiveness of the network improvements and the adaptability of the proposed strategy are demonstrated through comparative experiments.

关键词： Eco-driving Fuel cell hybrid electric bus Vertical comfort Soft actor-critic multiple objective optimization

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Optimum design of sensor arrays via simulation-based multivariate calibration

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SENSORS AND ACTUATORS B-CHEMICAL 2011年第2期156卷 854-862页

作者： Geng, Zongyu Yang, Feng Wu, Nianqiang W Virginia Univ Ind & Management Syst Engn Dept Morgantown WV 26506 USA W Virginia Univ Dept Mech & Aerosp Engn Morgantown WV 26506 USA

This paper is concerned with the optimum design of sensor arrays (i.e., electronic noses or tongues) using simulation experiments. The proposed design method considers multiple criteria simultaneously for the evaluation of sensor arrays, and a multiple objective tabu search algorithm was adapted to search for a number of Pareto optimum array designs in terms of those criteria. The evaluation of a candidate sensor array is based on its multivariate calibration model, which is efficiently estimated from well-designed simulation experiments. The method can be used to optimize the design of both linear and nonlinear sensor arrays. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Sensor array Design optimization Multivariate calibration multiple objective optimization Sensor simulation

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Optimal multiobjective design of robust power system stabilizers using genetic algorithms

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IEEE TRANSACTIONS ON POWER SYSTEMS 2003年第3期18卷 1125-1132页

作者： Abdel-Magid, YL Abido, MA King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

Optimal multiobjective design of robust multimachine power system stabilizers (PSSs) using genetic algorithms is presented in this paper. A conventional speed-based lead-lag PSS is used in this work. The multimachine power system operating at various loading conditions and system configurations is treated as a finite set of plants. The stabilizers are tuned to simultaneously shift the lightly damped and undamped electromechanical modes of all plants to a prescribed zone in the s-plane. A multiobjective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The problem of robustly selecting the parameters of the power system stabilizers is converted to an optimization problem which is solved by a genetic algorithm with the eigenvalue-based multiobjective function. The effectiveness of the suggested technique in damping local and interarea modes of oscillations in multimachine power systems, over a wide range of loading conditions and system configurations, is confirmed through eigenvalue analysis and nonlinear simulation results.

关键词： dynamic stability genetic algorithms multiple objective optimization robustness simultaneous stabilization

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Approach to optimization of part machining service combination

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2011年第5-8期56卷 767-776页

作者： Sun, Huibin Wan, Neng Chang, Zhiyong Mo, Rong NW Polytech Univ Key Lab Contemporary Design & Integrated Mfg Tech Minist Educ Xian 710072 Peoples R China

To find out the best part machining service provider combination, the theory of multiple objective optimization is discussed in detail via mathematic formulation. Relationships among service provider candidates, service types and parameters are analyzed and uncovered by matrix accumulation. The evaluation index and the practical strength are defined as parameters' functions. Typical evaluation indexes, such as assembly quality and cost, are chosen to be analyzed in detail, and dimensionless method is put forward to combine them together. A problem-solving algorithm is designed on the basis of the particle swarm optimization. An example is presented to validate that the proposed methods is efficient in solving the multiple objective optimization of the part machining service provider combination problem.

关键词： Product service system Part machining service multiple objective optimization

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Clustering aided approach for decision making in computationally expensive multiobjective optimization

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optimization METHODS & SOFTWARE 2009年第2期24卷 157-174页

作者： Aittokoski, Timo Ayramo, Sami Miettinen, Kaisa Univ Jyvaskyla Dept Math Informat Technol SF-40351 Jyvaskyla Finland

Typically, industrial optimization problems need to be solved in an efficient, multiobjective and global manner, because they are often computationally expensive (as function values are typically based on simulations), they may contain multiple conflicting objectives, and they may have several local optima. Solving such problems may be challenging and time consuming when the aim is to find the most preferred Pareto optimal solution. In this study, we propose a method where we use an advanced clustering technique to reveal essential characteristics of the approximation of the Pareto optimal set, which has been generated beforehand. Thus, the decision maker (DM) is involved only after the most time consuming computation is finished. After the initiation phase, a moderate number of cluster prototypes projected to the Pareto optimal set is presented to the DM to be studied. This allows him/her to rapidly gain an overall understanding of the main characteristics of the problem without placing too much cognitive load on the DM. Furthermore, we also suggest some ways of applying our approach to different types of problems and demonstrate it with an example related to internal combustion engine design.

关键词： multiple objective optimization multicriteria optimization Pareto optimality efficient methods learning-oriented methods

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multiple Electric Components Health-Aware Eco-Driving Strategy for Fuel Cell Hybrid Electric Vehicle Based on Soft Actor-Critic Algorithm

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第3期10卷 6242-6257页

作者： Peng, Jiankun Zhou, Jiaxuan Chen, Jun Pi, Dawei Wu, Jingda Wang, Hongliang Ding, Fan Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

The eco-driving strategy based on deep reinforcement learning holds significant potential for achieving energy efficiency, longevity, and safety of fuel cell hybrid electric vehicle (FCHEV). This article proposes a health-aware eco-driving strategy for FCHEV based on the soft actor-critic (SAC) algorithm. Building upon health awareness of multiple electric components including power battery, fuel cell, and driving motor, this eco-driving strategy integrates energy management system (EMS) and adaptive cruise control (ACC) to comprehensively optimize vehicle performance. By incorporating health awareness into the eco-driving approach, this study aims to maximize the lifespan of electric components, enhance energy utilization efficiency, and ensure driving comfort and safety. SAC algorithm not only enhances optimization performance in complex nonlinear multiobjective optimization problems but also accommodates real-time control requirements under diverse driving conditions. The simulation results demonstrate that the proposed strategy achieves 0.41% reduction in H2 consumption and same level health maintenance of electric components compared with the dynamic programming (DP) benchmark of EMS, while maintaining the comfort within 6% of the gap but safer following performance compared with the intelligent driver model (IDM) benchmark of ACC. Moreover, the comparative experiments demonstrate that the effectiveness and adaptability of proposed eco driving strategy.

关键词： Medical services optimization Transportation Energy management Degradation Costs Adaptation models Eco-driving fuel cell hybrid electric vehicle (FCHEV) health awareness multiple objective optimization soft actor-critic (SAC)

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An Energy Efficient Power Management Solution for a Fault-Tolerant More Electric Engine/Aircraft

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2019年第7期66卷 5663-5675页

作者： Zhang, Yicheng Peng, Gabriel Ooi Heo Banda, Joseph Kiran Dasgupta, Souvik Husband, Mark Su, Rong Wen, Changyun Nanyang Technol Univ Rolls Royce NTU Corp Lab Singapore 639798 Singapore Rolls Royce Singapore Pte Ltd Singapore 797575 Singapore Rolls Royce PLC London SW1E 6AT England Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This paper provides the concept of design, implementation, and system integration of energy efficient power management solution for a fault-tolerant more electric engine/aircraft (MEE/MEA). The power management solution consists of three components, i.e., the power management system (PMS), the stability analysis module, and the condition-based control (CBC) module. The PMS is to optimize the electrical power system (EPS) level efficiency based on constraints with consideration of power limits of the dc system in the MEE/MEA architecture, which are calculated by the stability analysis module, and system reconfiguration, which is provided by the CBC module. This paper focuses on the design and implementation of PMS algorithm for EPS of a fault-tolerant MEE/MEA architecture. A PMS solution to optimize EPS level efficiency optimization is proposed, which is based on multiple objective optimization problem (MOOP). The proposed MOOP is solved with a modified nondominated genetic algorithm with local search mechanisms. The proposed power management solution are implemented in the MEE/MEA simulation model as well as hardware-in-loop virtual testbed and results show the effectiveness of the proposed algorithm.

关键词： Condition-based control (CBC) fault tolerance more electric aircraft (MEA) multiple objective optimization power management system (PMS)

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