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Stochastic Steepest Descent optimization of multiple-objective Mobile Sensor Coverage

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2012年第4期61卷 1810-1822页

作者： Ma, Chris Y. T. Yau, David K. Y. Yip, Nung Kwan Rao, Nageswara S. V. Chen, Jiming Adv Digital Sci Ctr Singapore 138632 Singapore Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ Dept Math W Lafayette IN 47907 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37831 USA Zhejiang Univ Dept Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

We propose a steepest descent method to compute optimal control parameters for balancing between multiple performance objectives in stateless stochastic scheduling, wherein the scheduling decision is effected by a simple constant-time coin toss operation only. We apply our method to the scheduling of a mobile sensor's coverage time among a set of points of interest (PoIs). The coverage algorithm is guided by a Markov chain, wherein the sensor at PoI i decides to go to the next PoI j with transition probability p(ij). We use steepest descent to compute the transition probabilities for optimal tradeoff among different performance goals with regard to the distributions of per-PoI coverage times and exposure times and the entropy and energy efficiency of sensor movement. For computational efficiency, we show how we can optimally adapt the step size in steepest descent to achieve fast convergence. However, we found that the structure of our problem is complex, because there may exist surprisingly many local optima in the solution space, causing basic steepest descent to easily get stuck at a local optimum. To solve the problem, we show how proper incorporation of noise in the search process can get us out of the local optima with high probability. We provide simulation results to verify the accuracy of our analysis and show that our method can converge to the globally optimal control parameters under different assigned weights to the performance goals and different initial parameters.

关键词： Mobile sensor network multiple-objective optimization steepest descent

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Optimal Allocation of Distributed Generation in Distribution System Considering Time Sequence Data and Low-carbon Economy

Optimal Allocation of Distributed Generation in Distribution...

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IEEE Power and Energy Society General Meeting

作者： Ke-yan Liu Wanxing Sheng Yuan Liu Power Distribution Department China Electric Power Research Institute

ISBN: (纸本)9781479964161

With the consideration of time sequence characteristics of load and distributed generation (DG), a novel method is presented for optimal sitting and sizing of DG in distribution system. multiple-objective functions have been formed with the consideration of minimum investment and operational cost of DG, minimum voltage deviation and maximal voltage stability margin. To solve the multiple-objective optimization problem, an Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) has been proposed. Several experiments have been made on the modified PG&E 69-bus and actual 292-bus test systems. The result and comparisons indicate the proposed method for optimal placement and sizing of DG units is feasible and effective.

关键词： Optimal allocation Distribution generation Distribution system planning multiple-objective optimization

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Mergers and Acquisitions: An Efficiency Evaluation

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Applied Mathematics 2013年第11期4卷 1583-1589页

作者： Paulo Rotela Junior Edson de Oliveira Pamplona Aneirson Francisco da Silva Institute of Production Engineering and Management Federal University of ItajubaItajubaBrazil Departament of Production Sao Paulo State UniversityGuaratinguetaBrazil

This article sheds light on how synergies arise through mergers and acquisitions (M&A). Enterprises go through the process of Mergers and Acquisitions (M&A) with the goal of improving performance, increasing efficiency and obtaining business synergy. Prior literature suggests that synergies could arise due to taxes, market power or efficiency improvements. This study evaluates the efficiency of M&A in Brazil among publicly-traded companies. We used models with multiple objectives from Goal Programming and Data Envelopment Analysis (GPDEA), employing accounting indicators as input and output variables, and thus evaluated the emergence of synergy gains. These models allow us to analyze and classify the M&A according to the efficiency obtained in such processes. Some of the M&A cases analyzed were mistakenly considered efficient when used traditional models. And, as expected, the GPDEA was proved to be superior to classical models;however it was noticed that few of the cases investigated were proved to be effective. We presented a new application for multi-objective approach that can be used to assess mergers and acquisitions. The dualapplication of GPDEA provided a greater understanding of efficiency generation in synergy creation by means of M&A.

关键词： Merger and Acquisition multiple-objective optimization Goal Programming Data Envelopment Analysis Synergy

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Model Development and Optimal Experimental Design of A Kinetically Controlled Synthesis System

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IFAC Proceedings Volumes 2013年第31期46卷 327-332页

作者： H. Yue P. Halling H. Yu Department of Electronic and Electrical Engineering University of Strathclyde Glasgow G1 1XW UK Department of Pure and Applied Chemistry University of Strathclyde Glasgow G1 1XL UK

A mathematical model has been developed for an enzymatic process with kinetically controlled synthesis. Model reduction and detailed system analysis have been undertaken to examine the main properties of this enzyme reaction system. Optimal experimental design (OED) is developed to obtain the experimental conditions that will generate the most informative measurement data for parameter estimation. Both single-input and multiple-inputs optimisation strategies have been investigated to determine the best intensity levels of control inputs. The results demonstrate that parameter estimation quality can be improved through proper model-based experimental design.

关键词： Model development optimal experimental design (OED) multiple-objective optimization least-squares parameter estimation enzyme reaction networks

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Bidding based generator maintenance scheduling with triple-objective optimization

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ELECTRIC POWER SYSTEMS RESEARCH 2012年 93卷 127-134页

作者： Zhang, Dabo Li, Wenyuan Xiong, Xiaofu BC Hydro & Power Author Vancouver BC V7X 1V5 Canada Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400030 Peoples R China

A new bidding based generating unit maintenance scheduling method under the electricity market environment is presented in this paper. The method can balance overall reliability and economy, and ensure the fairness to all generation companies (GENCOs). The unit maintenance scheduling is modeled as a triple-objective non-convex optimization problem with discrete variables and is solved using an improved version of the Non-dominated Sorting Genetic Algorithm (NSGA-II). A non-inferior solution set is directly produced from huge combinations. A few candidate solutions are selected from the non-inferior set by independent system operator (ISO) based on the overall reliability and economy, whereas the final maintenance schedule is determined by voting of GENCOs. The shares of vote are calculated from the bidding information of GENCOs and the right of each GENCO is fairly guaranteed. A case study of 50 units demonstrates the effectiveness of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Electricity market Bidding Generation Maintenance multiple-objective optimization Reliability

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multiple-objective dg optimal sizing in distribution system using an improved PSO algorithm

Multiple-objective dg optimal sizing in distribution system ...

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2nd IET Renewable Power Generation Conference (RPG 2013)

作者： Ke-yan Liu Kaiyuan He Wanxing Sheng China Electric Power Research Institute China

ISBN: (纸本)9781629936017

An improved particle swarm optimization (PSO) algorithm has been presented in optimal sizing of multiple DG units in this paper. Firstly, multiple-objective functions have been formed with the consideration of minimum line loss, minimum voltage deviation and maximal voltage stability margin. Through fuzzy set theory, the multiple-objective optimization problem has been transformed to single objective comprehensive optimization with membership degree. The global particle swarm optimization algorithm dominates the search direction and works out the result; in the meanwhile, the inertia weight w, the cognitive and social parameters are updated in adaptive mode. And multi-initialization method is utilized to refresh the particle populations and increase its diversity. Several experiments have been made based on the IEEE 33-bus, actual 292-588 and 1180-bus test cases with the consideration of multiple DG units. The computational result and comparison indicate the proposed algorithm for optimal sizing of DG in distribution system is feasible and effective.

关键词： multiple-objective optimization Distributed generation Fuzzy set Optimal planning

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Economic optimization with Environmental Cost for a Microgrid

Economic Optimization with Environmental Cost for a Microgri...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Ren, Hui Xiang, Anwei Teng, Weijun Cen, Rongjia North China Elect Power Univ Dept Elect Engn Baoding 071003 Hebei Peoples R China Guizhou Power Grid Corp Duyun Power Supply Bur Chongqing 5558000 Guizhou Peoples R China

ISBN: (纸本)9781467327299

This paper presents a generalized formulation for the optimal operation of a microgrid. multiple-objective optimization is applied to the environmental/economic problem of the MG, considering the operating cost, fuel cost, depreciation cost as well as environmental cost. Weight coefficient N is proposed to coordinate the proportion of generating cost objective and environmental cost objective. The optimization is aimed at minimizing the total cost of the system while satisfying the customer demand and system security. A detail case, in which the microgrid is consisting of a cluster of wind turbines, micro turbines, photovoltaic arrays and fuel cells, is discussed. And the results demonstrate the efficiency of the proposed approach to satisfy the load and maximize the microgrid's interests.

关键词： multiple-objective optimization microgrid environmental/economic problem weight coefficient wind turbines micro turbines photovoltaic arrays and fuel cells

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An assembly strategy scheduling method for human and robot coordinated cell manufacturing

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INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING AND CYBERNETICS 2011年第4期4卷 487-510页

作者： Chen, Fei Sekiyama, Kosuke Huang, Jian Sun, Baiqing Sasaki, Hironobu Fukuda, Toshio Nagoya Univ Dept Micro Nano Syst Engn Nagoya Aichi Japan Huazhong Univ Sci & Technol Dept Control Sci & Engn Wuhan Hubei Peoples R China Shenyang Univ Technol Sch Elect Engn Shenyang Liaoning Peoples R China

Purpose - The purpose of this paper is to propose a model of assembly strategy generation and selection for human and robot coordinated (HRC) cell assembly. High-Mix, Low-Volume production in small production manufacturing industry, tends to employ more flexible assembly cells. The authors propose innovative human and robot coordinated assembly cells to solve the problem of persistent growing cost for human resources and occasional changes in programs and configurations for robots. The first issue is to find out an optimal way to allocate the assembly subtasks to both humans and robots. Design/methodology/approach - A dual Generalized Stochastic Petri Net (GSPN) model is theoretically studied and then off line built based on a practical assembly task for human and robot coordination. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost or possible strategies, and multiple-objective optimization (MOOP) method related Cost-effectiveness analysis is adopted to select the optimal ones. Findings - It is discovered that human and robot coordinated assembly can reduce the assembly time and meanwhile reduce the assembly cost. The authors demonstrate the effectiveness of this approach by comparing the simulation and experimental results. Originality/value - The novelty with this work is that the human and robot coordinated flexible assembly cell, as the authors proved, is the main stream in small production in future due to the higher human source pressure from society and cost pressure upon the company. Based on this innovative work, the authors proposed a dual GSPN model to model the assembly task allocation process for human and robot, the model of which is also effective in modeling the possible robot and human behaviors.

关键词： Robots Assembly Process efficiency Stochastic Petri-net Human and robot coordination Assembly cell Monte Carlo multiple-objective optimization

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Simulation-based optimal calibration of spark ignition engines with multiple objectives: fuel economy and combustion variability

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2011年第D10期225卷 1427-1442页

作者： Lee, T-K Kramer, D. Ohl, G. Filipi, Z. S. Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA USA RDECOM TARDEC Warren MI USA Adapt Mat Inc Ann Arbor MI USA

Optimal engine calibration methodology at part-load operating conditions using a multi-scale simulation approach is proposed and demonstrated on a spark ignition engine with dual-independent variable valve timing and a charge motion control valve. Fuel economy is typically selected as a calibration objective at part-load operating conditions. However, to secure vehicle driveability and smooth engine operation with low vibration and harshness, the combustion variability is considered by introducing the coefficient of variation in the indicated mean effective pressure characterized through a statistical analysis of experimental data. Other engine responses are fully predicted through a co-simulation approach by implementing the quasi-dimensional combustion simulation into the one-dimensional gas exchange simulation, and subsequently captured by an artificial neural network for fast computation. The best actuator set points are determined by solving the constrained multiple-objective optimization problem with fuel economy and combustion variability objectives under part-load conditions. The calibration eliminates a high combustion variability at low loads and low speeds, maintaining most of the fuel economy benefit achievable with variable valve timing, and it can be used for an implementable feedforward control strategy.

关键词： internal combustion engine optimal calibration combustion variability fuel economy multiple-objective optimization artificial neural network virtual sensing

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A heuristic relief transportation planning algorithm for emergency supply chain management

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2010年第7期87卷 1638-1664页

作者： Chern, C. C. Chen, Y. L. Kung, L. C. Natl Taiwan Univ Dept Informat Management Taipei 106 Taiwan

Emergency supply chain operations have to fulfil all the demands in a very short period of time, using the limited resources at its disposal. Mixed integer programming (MIP) is a popular method to solve emergency supply chain planning problems. However, as such problem increases in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. This study proposes a heuristic algorithm, called the Emergency Relief Transportation Planning Algorithm (ERTPA). ERTPA will group and sort demands according to the required products, the imposed due dates, the possible shared capacities, and the distances from the demand nodes to the depots. Then, ERTPA plans the demands individually, using a shortest travelling-time tree and a minimum cost production tree. To show the effectiveness and efficiency of the heuristic algorithm, a prototype was constructed and tested, using complexity and computational analyses.

关键词： emergency response system emergency relief transportation heuristic algorithm emergency supply chain management multiple-objective optimization

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