考虑产品环境质量和消费者惯性的电动汽车供应链策略分析

按是否拥有传统汽车将潜在消费者分成两类,基于不同购买行为的消费者,根据消费者购买行为理论,针对每一类消费者分别建立效用模型,运用斯坦伯格博弈理论求解得到厂商的最优定价策略和政府的最优补贴策略,进而分析政府考虑包括厂商收益、消费者剩余、政府支出以及环境效益在内的社会福利最大化时电动汽车的环境质量、传统汽车原拥有比例以及消费者惯性三因素对电动汽车普及率、政府补贴以及生产商收益的影响。研究结果表明:当生产成本小于某阀值时,电动汽车的市场需求随电动汽车的环境质量的增加而增大,传统汽车的市场需求则随之减小,反之亦然。电动汽车的生产效率较高时,政府的最优补贴随产品环境质量的增加而增大,生产效率较低时,最优补贴随之减小,政府通过增大补贴的方式鼓励生产商提高生产效率。最后通过数值实验验证了以上结论,并分析得到以上三方面因素对生产商收益的影响。     

考虑绕行特征的电动汽车快速充电站选址问题及自适应遗传算法

快速充电站选址是电动汽车运营的重要内容之一。本文考虑电动汽车用户会通过绕行一定距离对车辆进行充电这一特征,建立了一个以电动汽车快速充电站建站成本和旅客整体绕行成本之和最小的双层整数规划模型。本文首先给出了用于生成绕行路径集合的A^*算法,然后设计了一种包含局部迭代搜索的自适应遗传算法对该模型进行求解。为了测试算法性能,通过两个不同规模的算例图与已有求解FPLM问题的遗传算法进行了比较,数值试验部分证明了算法的正确性和有效性。最后引入浙江省的高速路网图,从建站成本和截流量两方面对电池续航里程带来的影响进行了相关的灵敏度分析。     

集中通勤接送服务的最小化成本模型及算法

本文针对集中通勤接送服务业务流程中的车次分配与调度问题,建立了以最小化成本为目标的0-1混合整数规划模型,刻画了该问题所具有的多目标抵达地、多车协作、多行程车次等特征事实,同时考虑顾客满意度,以获得不同满意度下的近似最小成本。根据问题的复杂性和大规模性,设计开发了一种基于kNN思想的类标签启发式算法求解模型,拓展了构造路径的方法。算例数值结果表明,本文所设计模型和算法是有效性、可行的。     

基于交互式并行驾驶模拟实验的跟驰状态汽车驾驶倾向性动态辨识模型验证方法

驾驶倾向性的实时识别是实现汽车辅助驾驶尤其是主动安全预警系统智能化的关键理论和技术.通过交互式并行模拟驾驶实验获取跟驰状态下各倾向性类型的行车数据对建立的驾驶员倾向性的动态辨识模型进行验证,并将验证结果与心理问卷测试的结果相对比.结果表明所建辨识模型可行,能够实现对跟驰状态下驾驶员倾向性类型的实时识别,为个性化汽车主动安全系统的实现提供理论基础.     

自动驾驶时代基于地铁定价策略的多模式通勤出行管理

多模式交通系统的优化管理研究是交通领域的经典问题.文章结合自动驾驶时代背景,构建高速瓶颈公路传统汽车和共享自动驾驶汽车,以及独立地铁线路组成的多模式竞争交通模型,研究不同地铁定价策略（如地铁边际成本,平均成本,总社会成本最优以及考虑道路收费时受补贴的地铁边际成本定价）对共享自动驾驶汽车市场渗透率,出行模式划分以及总社会成本的影响.结果表明:随地铁定价策略变化,独驾传统汽车的通勤者数量未受影响,共享自动驾驶汽车和地铁的通勤者数量变化不唯一;当满足某个特定条件时,总社会成本最优定价在引导地铁出行,缓解道路拥堵,降低通勤者个体出行成本和总社会成本等方面具有最佳效果;而提高地铁运营的边际成本,受补贴的地铁边际成本定价将实现总社会成本最优.文章从不同视角为政府制定多目标和弹性的交通政策提供参考.     

基于两阶段求解策略的动态电动车辆路径优化研究

由于政府对新能源汽车的补贴政策和市区对燃油车限行政策的实时,越来越多的物流公司在城市配送中广泛采用电动汽车。然而,电动车续航里程受限,需要在途充电或者换电,同时客户需求的动态性以及充/换电设施的排队等现实因素也应该被考虑。为此,提出了分阶段策略求解动态电动车辆路径优化问题,并建立了两阶段的EVRP模型。其中第一阶段针对静态客户建立了静态EVRP模型,第二阶段在设计了换电站及动态客户插入策略的基础上,建立了动态EVRP模型以路径更新策略。最后,设计改进的CW-TS混合启发式算法来求解静态模型,设计贪婪算法求解动态模型。实验结果表明,模型与算法具有较好的适用性和有效性。     

多气源天然气管网运行优化问题的混合整数规划模型及其算法

对于多气源天然气管网运行优化问题,文章首先引入了刻画压缩机开关的0-1整数变量,并对非线性的管道压降方程进行了合理的松弛化处理,建立了更符合实际的非线性混合整数规划模型.其次,基于序列线性化的思想,设计了一种求解该模型的序列混合整数线性化算法.最后,在不同规模的天然气管网系统中进行了多方面的对比实验.实验结果表明,新模型及求解算法能够有效降低成本、减少压缩机能耗,并且所需求解时间大大减少.     

成品油配送多车舱车辆指派及路径优化问题研究

针对成品油配送中多车型、多车舱的车辆优化调度难题,综合考虑多车型车辆指派、多车舱车辆装载及路径安排等决策,以派车成本与油耗成本之和的总成本最小为目标,建立了多车型多车舱的车辆优化调度模型。为降低模型求解的复杂性,本文提出一种基于C-W节约算法的“需求拆分→合并装载”的车辆装载策略,并综合利用Relocate和Exchange算子进行并行邻域搜索改进,获得优化的成品油配送方案。最后,通过算例验证了本文提出的模型与算法用于求解大规模成品油配送问题的有效性。并通过数据实验揭示了以下规律:1)多车舱车辆相对于单车舱车辆在运营成本上具有优越性;2)大型车辆适合远距离配送,小型车辆适合近距离配送;3)多车型车辆混合配送相对于单车型车辆配送在运营成本上具有优越性。这些规律可为成品油配送公司的车辆配置提供决策参考。     

带时变生产成本的易变质经济批量模型的最优策略分析

考虑了具有时变生产成本的易变质产品经济批量模型．有限计划期内,单位生产成本、生产率以及需求率假定为时间的连续函数,生产固定成本则具有遗忘效应现象．当不允许缺货时,建立了以总成本最小为目标的混合整数优化模型并证明了此问题最优解的相关性质．对于此问题的特殊情形,将成本函数中的离散型变量松弛为连续型变量,通过分析其最优解的存在性及唯一性,求解了此最优解,将其作为初始值设计了求取一般情形最优解的有效算法．最后通过算例验证了理论结果的有效性．     

考虑乘客分流的异质车型通勤定制公交网络优化

定制公交作为城市需求响应交通的重要组成部分,其线网规划的优劣直接影响乘客满意度和系统运营成本.针对一类新的考虑乘客分流和异质车型的早晚高峰时期通勤定制公交网络规划向题,以最小化乘客旅行成本和公交系统运营成本为目标,同时优化异质车队路线、车型选择、行车时刻及乘客分配,构建了一个混合整数非线性规划模型.为有效求解该模型,提出了一种融合问题特征的改进自适应大邻域搜索算法,设计了基于问题特征的乘客分流破坏算子,以提升算法寻优能力.最后,大量算例结果验证了模型的正确性和算法的有效性.实验结果表明:求解小规模算例时,改进自适应大邻域搜索算法能在12秒以内获得距离最优解平均差距为0.24%的满意解;求解大规模算例时,与文献中大邻域搜索算法和遗传算法相比,所提出算法能获得更高质量解,平均成本节约分别为1.20%和2.27%;此外,与未考虑乘客分流和同质车型情形相比,考虑乘客分流和异质车型,得到的方案成本降低11.46%,提高了资源利用率.     

Dynamic energy management for a novel hybrid electric system based on driving pattern recognition

This paper focuses on the dynamic energy management for Hybrid Electric Vehicles (HEV) based on driving pattern recognition. The hybrid electric system studied in this paper includes a one-way clutch, a multi-plate clutch and a planetary gear unit as the power coupling device in the architecture. The powertrain efficiency model is established by integrating the component level models for the engine, the battery and the Integrated Starter/Generator (ISG). The powertrain system efficiency has been analyzed at each operation mode, including electric driving mode, driving and charging mode, engine driving mode and hybrid driving mode. The mode switching schedule of HEV system has been designed based on static system efficiency. Adaptive control for hybrid electric vehicles under random driving cycles with battery life and fuel consumption as the main considerations has been optimized by particle swarm optimization algorithm (PSO). Furthermore, driving pattern recognition based on twenty typical reference cycles has been implemented using cluster analysis. Finally, the dynamic energy management strategy for the hybrid electric vehicle has been proposed based on driving pattern recognition. The simulation model of the HEV powertrain system has been established on Matlab/Simulink platform. Two energy management strategies under random driving condition have both been implemented in the study, one is knowledge-based and the other is based on driving pattern recognition. The model simulation results have validated the control strategy for the hybrid electric vehicle in this study in terms of drive pattern recognition and energy management optimization.     

Routing a mix of conventional,plug-in hybrid,and electric vehicles

We introduce an electric vehicle routing problem combining conventional, plug-in hybrid, and electric vehicles. Electric vehicles are constrained in their service range by their battery capacity, and may require time-consuming recharging operations at some specific locations. Plug-in hybrid vehicles have two engines, an internal combustion engine and an electric engine using a built-in rechargeable battery. These vehicles can avoid visits to recharging stations by switching to fossil fuel. However, this flexibility comes at the price of a generally higher consumption rate and utility cost.To solve this complex problem variant, we design a sophisticated metaheuristic which combines a genetic algorithm with local and large neighborhood search. All route evaluations, within the approach, are based on a layered optimization algorithm which combines labeling techniques and greedy evaluation policies to insert recharging stations visits in a fixed trip and select the fuel types. The metaheuristic is finally hybridized with an integer programming solver, over a set partitioning formulation, so as to recombine high-quality routes from the search history into better solutions. Extensive experimental analyses are conducted, highlighting the good performance of the algorithm and the contribution of each of its main components. Finally, we investigate the impact of fuel and energy cost on fleet composition decisions. Our experiments show that a careful use of a mixed fleet can significantly reduce operational costs in a large variety of price scenarios, in comparison with the use of a fleet composed of a single vehicle class.     

带时间窗的三维装载物流配送优化方法研究

针对城市物流配送优化研究在客户服务时间窗和货物装载方式合理结合方面存在的不足,考虑物流配送车厢货物装载方式与客户访问序列相关的特征对车厢空间进行合理的区域划分。首先,构建了包含配送中心的固定成本、配送车辆的运输成本、维修成本、租赁成本和违反时间窗惩罚成本的物流运营成本最小化和配送车辆空间利用率最大化的双目标优化模型;然后,提出一种结合遗传算法(GA)全局搜索能力和禁忌搜索算法(TS)局部搜索能力的GA-TS混合算法求解模型;最后,结合重庆市某配送中心的三维装载物流配送实例数据进行了优化计算,实验结果给出了带时间窗的三维装载物流配送路径优化方案,并进行了不同车厢空间分区模式下平均装载率、物流运营成本和车辆使用数的比较分析。研究表明,当客户需求货物种类数与车辆的空间区域划分数相等且按货物类型进行区域划分时,物流运营成本最小,配送车辆使用数最少和车辆平均装载率最高。     

The two-echelon capacitated electric vehicle routing problem with battery swapping stations: Formulation and efficient methodology

In this paper, we present a two-echelon capacitated electric vehicle routing problem with battery swapping stations (2E-EVRP-BSS), which aims to determine the delivery strategy under battery driving range limitations for city logistics. The electric vehicles operating in the different echelons have different load capacities, battery driving ranges, power consumption rates, and battery swapping costs. We propose an integer programming formulation and a hybrid algorithm that combines a column generation and an adaptive large neighborhood search (CG-ALNS) to solve the problem. We conducted extensive computational experiments, demonstrate the applicability of the proposed model, and show the efficiency of the CG-ALNS algorithm. In addition, we explore the interplay between battery driving range and the effectiveness of vehicle emission reduction through sensitivity analysis.     

A MILP model to design hybrid wind–photovoltaic isolated rural electrification projects in developing countries

Electrification systems based on the use of renewable energy sources are a suitable option for providing electricity to isolated communities autonomously. Wind and hybrid wind–photovoltaic (PV) systems are increasingly getting attention. To electrify scattered communities, designs that combine individual systems and microgrids have recently proven advantageous. In this paper we present a mathematical programming model to optimize the design of hybrid wind–PV systems that solves the location of the wind–PV generators and the design of the microgrids, taking into account the demand of the consumption points and the energy potential. The criterion is the minimization of the initial investment cost required to meet the demand. The proposed hybrid model is tested with realistic size instances and results show the instances are efficiently solved. Moreover, the model is applied to real case studies in Peru; obtained results verify that the hybrid model efficiently finds solutions that significantly reduce costs.     

Extended Fuzzy C-Means and Genetic Algorithms to Optimize Power Flow Management in Hybrid Electric Vehicles

The need for personal transportation must be harmonized by considering the impact of so huge number of vehicles on the environment. The adoption of hybrid electric vehicles can provide a sensible improvement from an environmental viewpoint, but at the same time makes more difficult the definition and implementation of the overall powertrain control mechanism. In fact, powertrain control problems are known to be very complex due to conflicting requirements, and this difficulty augments in case of hybrid electric vehicles. Most of the features of the future hybrid electric vehicles are enabled by a new energy flow management unit designed to split the instantaneous power demand between the internal combustion engine and the electric motor, ensuring both an efficient power supply and reduced emissions. Classic approaches that rely on static thresholds, optimized on a fixed drive cycle, cannot face the high dynamicity and unpredictability of real-life drive conditions. The need to actually control a real vehicle stimulates the research of innovative methodologies for the real-time identification of the operating points of each energy source. This paper is framed into this context: after a brief discussion about a non-conventional formalization of the energy flows problem based on a multiobjective function, a knowledge-based control system for splitting the vehicle's power demand between the engine and motor is presented. The proposed approach exploits a fuzzy clustering criterion that combined with a genetic algorithm, permits to achieve better results, both in terms of a reduced computational effort and an improved efficiency of the control system over various driving cycles. To validate the proposed approach, simulation tests and comparisons with other energy management strategies are discussed.     

Optimal ordering policies for continuous review perishable inventory models

This paper extends the notions of perishable inventory models to the realm of continuous review inventory systems. The traditional perishable inventory costs of ordering, holding, shortage or penalty, disposal and revenue are incorporated into the continuous review framework. The type of policy that is optimal with respect to long run average expected cost is presented for both the backlogging and lost-sales models. In addition, for the lost-sales model the cost function is presented and analyzed.     

Tools of the mind: Techniques and methods for intellectual work: V. STIBIC North-Holland,Amsterdam, 1982, xiii + 297 pages,Dfl.85.00

Many location problems may be separated into a series of interrelated macro, meso and micro decision-making states. The macro scale decision determines the type, capacity and number of facilities, the meso scale decision determines the location and allocation of facilities and the micro scale decision determines such considerations as routing and scheduling of service vehicles. This paper concerns the first two levels of decision-making.The present paper demonstrates the use of two models: (i) an analytical model that uses continuum approximations and methods of calculus to determine the number of facilities, the capacity and the approximate location of each that minimizes the sum of the transportation and facility costs for a slowly varying demand rate, and (ii) a traditional location-allocation model that determines more exactly the resulting locations and allocations. These two approaches have specific requirements in terms of data input, cost of data collection and cost of solution and, consequently, yield unique insights and benefits for practising planners. The strengths and weaknesses of the two models are complementary. This thesis is developed with an analysis of the Calgary, Alberta refuse collection and disposal system.     

Charge depleting range dynamic strategy with power feedback considering fuel-cell degradation

A charge depleting range dynamic strategy considering the fuel-cell degradation is proposed for the plug-in fuel cell hybrid electric vehicles in this paper, to seek the excellent economy performance with various driving ranges. The proposed strategy is developed by means of incorporating an adaptive equivalent factor into equivalent consumption minimization strategy with power feedback control. Firstly, the mathematical modeling of the proposed strategy is formulated to adjust the equivalent factor corresponding to the battery state of charge, which may vary with the forthcoming trip distance to dominate the expected energy consumption. The fuel cell voltage decay rate is applied to the feedback control of the restricted fuel-cell power, which is designed as the degradation model of fuel cell and integrated with the strategy. Secondly, the realization process of the charge depleting range dynamic strategy with restricted power feedback control model is a new attempt to design the real-time control method. Finally, the proposed strategy is validated by a numerical model developed by using the MATLAB/Simulink software and its effectiveness was evaluated by comparing it with the equivalent consumption minimization strategy. A sensitivity analysis is also conducted to study the effects of different trip distances. The effectiveness of power feedback control in fuel cell durability is validated through a hardware-in-the-loop experiment. The verified results demonstrate the influence of the equivalent factor on the control process, which makes the proposed strategy possible to provide significant improvement in the economy performance and fuel-cell durability.