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A Partition-Based Match Making algorithm for Dynamic Ridesharing

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2015年第5期16卷 2587-2598页

作者： Pelzer, Dominik Xiao, Jiajian Zehe, Daniel Lees, Michael H. Knoll, Alois C. Aydt, Heiko TUM CREATE Modeling & Optimizat Architectures & Infrastruct Singapore 138602 Singapore Univ Amsterdam Fac Sci Sect Computat Sci NL-1012 Amsterdam Netherlands Tech Univ Munich Dept Informat D-80333 Munich Germany

Ridesharing offers the opportunity to make more efficient use of vehicles while preserving the benefits of individual mobility. Presenting ridesharing as a viable option for commuters, however, requires minimizing certain inconvenience factors. One of these factors includes detours which result from picking up and dropping off additional passengers. This paper proposes a method which aims to best utilize ridesharing potential while keeping detours below a specific limit. The method specifically targets ridesharing systems on a very large scale and with a high degree of dynamics which are difficult to address using classical approaches known from operations research. For this purpose, the road network is divided into distinct partitions which define the search space for ride matches. The size and shape of the partitions depend on the topology of the road network as well as on two free parameters. This allows optimizing the partitioning with regard to sharing potential utilization and inconvenience minimization. Match making is ultimately performed using an agent-based approach. As a case study, the algorithm is applied to investigate the potential for taxi sharing in Singapore. This is done by considering about 110 000 daily trips and allowing up to two sharing partners. The outcome shows that the number of trips could be reduced by 42% resulting in a daily mileage savings of 230 000 km. It is further shown that the presented approach exceeds the mileage savings achieved by a greedy heuristic by 6% while requiring 30% lower computational efforts.

关键词： Network partitioning optimization algorithm dynamic ridesharing taxi sharing multi-agent systems

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Stochastic Cost-Profit Tradeoff Model for Locating an Automotive Service Enterprise

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2015年第2期12卷 580-587页

作者： Tian, Guangdong Zhou, MengChu Chu, Jiangwei Qiang, Tiangang Hu, Hesuan Northeast Forestry Univ Transportat Coll Harbin 150040 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 201804 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Xidian Univ Sch Electromech Engn Xian 710071 Shaanxi Peoples R China

Facility location allocation (FLA) is considered as the problem of finding optimally a facility's location with the maximum customer satisfaction, the maximum profit of investors of the facility, and the minimum transportation cost of its oriented-customers. In practice, some factors of the FLA problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus the problem features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic profit and cost issues of FLA. However, a decision-maker hopes to obtain the specific profit of investors of building facility and meanwhile to minimize the cost of target customers. To handle this issue via a more practical manner, it is essential to address the cost-profit tradeoff issue of FLA. Moreover, some region constraints can greatly influence FLA. By taking the vehicle inspection station as a typical automotive service enterprise example, this work presents new stochastic cost-profit tradeoff FLA models with region constraints. A hybrid algorithm integrating stochastic simulation and Genetic algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm. Note to Practitioners-This work concerns the uncertainty involved in facility location allocation (FLA). To deal with such uncertainty, it proposes a novel stochastic tradeoff method to solve an FLA problem. The prior research handles such a problem through a methodology based on the stochastic profit or cost, which is ineffective without considering the balance between the obtained profit of investors and the transportation cost of target customers. The goal of this work is to establish the tradeoff FLA model, i.e., to obtain assuredly the specific profit of investors, while minimizing the transportation cost of the service costumers for FLA. Both theoretical and simulat

关键词： Facility location allocation (FLA) modeling and simulation optimization algorithm

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Partial Derivatives of the Solution to the Lambert Boundary Value Problem

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2015年第9期38卷 1563-1572页

作者： Arora, Nitin Russell, Ryan P. Strange, Nathan Ottesen, David CALTECH Jet Prop Lab Pasadena CA 91011 USA Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

Two methods for deriving first-order partial derivatives of the outputs with respect to the inputs of the Lambert boundary value problem are presented. The first method assumes the Lambert problem is solved via the universal vercosine formulation. Taking advantage of inherent symmetries and intermediate variables, the derivatives are expressed in a computationally efficient form. The typical added cost of computing these partials is found to be approximate to 15 to 35% of the Lambert computed cost. A second set of the same partial derivatives is derived from the fundamental perturbation matrix, also known as the state transition matrix of the Keplerian initial value problem. The equations are formulated in terms of Battin's partitions of the state transition matrix and its adjoint. This alternative approach works with any Lambert formulation, including one that solves a perturbed Lambert problem, subject to the availability of the associated state transition matrix. The analytic partial derivatives enable fast trajectory optimization formulations that implicitly enforce continuity constraints via embedded Lambert problems.

关键词： Boundary Value Problems Lambert algorithm Computing Effort Computing Cost Trajectory optimization Eccentric Anomaly Orbit Determination FORTRAN Finite Difference Method optimization algorithm

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Optimal sizing of power generation unit capacity in ICE-driven CCHP systems for various residential building sizes

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APPLIED ENERGY 2015年 158卷 203-219页

作者： Farahnak, Mehdi Farzaneh-Gord, Mahmood Deymi-Dashtebayaz, Mandi Dashti, Farshad Shahrood Univ Technol Dept Mech Engn Shahrood *** Iran Hakim Sabzevari Univ Dept Mech Engn Sabzevar Iran Shahrood Univ Technol Sch Architectural Engn Shahrood Iran

Currently, CCHP systems are being used over a wide area as a key alternative for producing power, heat and refrigeration. Here, a sample residential building in Mashhad city (Iran) has been selected as a case study to investigate feasibility of employing CCHP systems to meet the energy demands for various buildings sizes. An optimization algorithm is developed to find the best operation point of the Power Generation Unit (PGU) at minimum energy cost. The algorithm optimizes the operation of the CCHP systems at first step. The results of the algorithm implementation for different PGU capacities and various buildings sizes, demonstrate the performance of the operationally optimized CCHP systems at second step. The results show that CCHP system has higher performance comparing to the separate production system considering different evaluation parameters. In small buildings, primary energy saving ratio is positive for PGU capacities between 1 kW and 15 kW, with a maximum amount of 17.24%. But in large buildings, it is positive for PGU capacities between 1 kW and 30 kW, with a maximum amount of 5.1%. The PGU with capacity of 350 kW in 30-units building by 51.18% has the highest amount of energy cost saving ratio. The CCHP system with 120 kW PGU capacity in 30-units building has the least simple payback ratio by 5.08 years. Considering each parameter individually, it is showed that there are some useful mathematical relationships between optimum PGU capacity and building size. Also it is found that there is an equation for best optimum PGU capacity, considering all of evaluation criteria simultaneously. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Optimal sizing optimization algorithm CCHP system Internal combustion engine (ICE) Residential building

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Binary control of plasmonic nano rods to design an optical switch

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OPTICAL AND QUANTUM ELECTRONICS 2015年第8期47卷 3071-3080页

作者： Akhlaghi, Majid Keshavarz, Rasul Emami, Farzin Islamic Azad Univ Young Researchers & Elite Club Omidieh Branch Omidieh Iran Shiraz Univ Technol Shiraz Iran

An efficient binary optimization method named binary particle swarm optimization (BPSO) algorithm was used to optimize an array of plasmonic nano-rods in order to design an optical switch. In the proposed switch, the optical switch has been excited by two monochromatic incident plan-waves with the same frequency and two angles of incident theta = 0 and theta = 90. When only the signal with theta = 0 is applied, the incident wave is transmitted and when both signals are applied to the switch simultaneously, the coherent perfect absorption occurs and the two incident waves are suppressed. Therefore, the signal with theta = 90 acts as control signal. BPSO, a swarm of birds including a matrix with binary entries responsible for controlling nano-rods in the array, shows the presence with symbol of ('1') and the absence with ('0').

关键词： Plasmonic nano particles optimization algorithm Optical switch

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Optimal management of hybrid PV/fuel cell/battery power system: A comparison of optimal hybrid approaches

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2015年 42卷 377-393页

作者： Bigdeli, Nooshin Imam Khomeini Int Univ EE Dept Qazvin Iran

In this paper, different optimal hybrid techniques have been proposed for management of a hybrid power generation system including photovoltaic (PV),. fuel cell and battery. The main power of the hybrid system comes from the photovoltaic panels, while the fuel cell and batteries are used as back up units. In order to achieve maximum power point tracking for the photovoltaic system, both fuzzy logic controller and perturb and observation methods are examined and their performances have been investigated via simulations. Next, the performance of the hybrid system has been improved via employing a family of well-known optimization approaches for load sharing among the available resources. Imperialist Competitive algorithm (ICA), Particle Swarm optimization (PSO), Quantum behaved Particle Swarm optimization (QPSO), Ant Colony optimization (ACO), and Cuckoo optimization algorithm (COA) are used to manage the load sharing to achieve optimal performance while the system constraints are met. The optimal performance has been characterized via the control strategy performance measure being the ratio of the amount of hydrogen production with respect to the hydrogen consumption. In order to verify the system performance, simulation studies have been carried out using practical load demand data and real weather data (solar irradiance and air temperature). Different combination of maximum power point tracking methods with various optimization algorithms have been compared with each other. The results show that the combination of fuzzy logic controller with QPSO has the best performance among the considered combinations. In this situation, when the solar irradiation is noticeably high, the required load is supplied mainly by PV array, while the battery is charged, simultaneously. In the other times, the load is mainly fed by the battery and fuel cell while the performance constraints of battery is met and the daily performance measure is optimized. (C) 2014 Elsevier Ltd. All right

关键词： Hybrid system Photovoltaic Fuel cell optimization algorithm Quantum behaved particle swarm optimization algorithm (QPSO) Imperialist competitive algorithm (ICA) Load sharing

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Comparison between Differential Evolution and Particle Swarm optimization algorithms

Comparison between Differential Evolution and Particle Swarm...

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11th IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Zhang, Dan Wei, Bin Univ Ontario Inst Technol Dept Automot Mech & Mfg Engn Oshawa ON L1H 7K4 Canada

ISBN: (纸本)9781479939794

In this paper, the performance of differential evolution ( DE) and particle swarm optimization (PSO) algorithms are compared and evaluated. The comparison is performed on eight benchmark functions f1-f8. New findings have been discovered for the PSO algorithm and the comparison results in this report show that DE generally is better than PSO in term of solution accuracy and robustness in almost all the problems. Generally, from the numerical results and graphic illustrations, we can demonstrate that DE is more efficient and robust compare to PSO, although PSO gives good results in some cases.

关键词： optimization algorithm differential evolution (DE) particle swarm optimization (PSO)

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A Multi-objective Shuffled Bat algorithm for Optimal Placement and Sizing of DGs with Load variations

A Multi-objective Shuffled Bat Algorithm for Optimal Placeme...

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IEEE Region 10 Conference

作者： Chandrasekhar Yammani National Institute of Technology Warangal

ISBN: (纸本)9781509025985

A new hybrid Multi-objective Shuffled Bat optimization algorithm is proposed in this paper for Distributed generations (DGs) optimal placement and sizing. Multiple objectives like system power losses, cost of DG and system voltage profiles are considered to evaluate the impact of DG placement and sizing for an optimal development of the distribution system with load variations. Furthermore, the study is demonstrated with different % loading such as 80,100 and 120% of base load condition. The proposed technique is tested in 33 bus distribution network, and compared against Non-dominated Sorting Genetic algorithm II (NSGA-II).

关键词： Distributed Generation Distribution system loading Power Loss minimization optimization algorithm Shuffled Bat algorithm

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Wireless indoor network planning for advanced exposure and installation cost minimization

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2015年第1期2015卷 1页

作者： Liu, Ning Plets, David Vanhecke, Kris Martens, Luc Joseph, Wout Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Ghent Univ iMinds Dept Informat Technol B-9050 Ghent Belgium

The possibility of having information access anytime and anywhere has caused a huge increase of the popularity of wireless networks. Requirements of users and owners have been ever-increasing. However, concerns about the potential health impact of exposure to radio frequency (RF) sources have arisen and are getting accounted for in wireless network planning. In addition to adequate coverage and reduced human exposure, the installation cost of the wireless network is also an important criterion in the planning process. In this paper, a hybrid algorithm is used to optimize indoor wireless network planning while satisfying three demands: maximum coverage, minimal full installation cost (cabling, cable gutters, drilling holes, labor, etc.), and minimal human exposure. For the first time, wireless indoor networks are being optimized based on these advanced and realistic conditions. The algorithm is investigated for three scenarios and for different configurations. The impact of different exposure requirements and cost scenarios is assessed.

关键词： Indoor wireless network planning optimization algorithm WiFi (IEEE 802.11n) Coverage Installation cost and exposure

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High-speed optical FSK demodulator using plasmonic nanorods

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JOURNAL OF MODERN OPTICS 2015年第4期62卷 307-312页

作者： Akhlaghi, Majid Nozhat, Najmeh Emami, Farzin Islamic Azad Univ Omidieh Branch Young Researchers & Elite Club Omidieh Iran Shiraz Univ Technol Optoelect Dept Shiraz Iran

An optical frequency-shift keying (FSK) demodulator with ultrasmall plasmonic nanorods that can filter the coherent optical frequency is developed. Since the filtering efficiency depends strongly on the position and number of the plasmonic nanorods in the array, binary teaching-learning-based optimization (BTLBO) algorithm is proposed to design an array of the plasmonic nanorods in order to achieve maximum absorption coefficient spectrum. In BTLBO, a group of learner consists of a matrix with binary entries;control the presence ('1') or the absence ('0') of the nanoparticles in the array.

关键词： word optical demodulator plasmonic nanoparticles optimization algorithm

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