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。     

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。     

面向电商终端物流配送的电动车配置与路径集成优化

在电子商务终端物流配送方面,存在能力与需求的矛盾。一方面,电动车存在货物容量约束和电池电量约束,配送能力有限;另一方面,一个物流配送点需要为众多的消费者进行门到门的配送,配送任务繁重。针对电子商务环境下终端物流配送规模大、电动车货物容量和行驶里程有限的问题,建立电商终端物流配送的电动车配置与路径规划集成优化模型,并提出一种基于临近城市列表的双策略蚁群算法,实现物流配送电动车辆配置与配送路径集成优化。该模型以电动车辆数最少和总路径最短为目标,以电动车货物容量和电池续航里程为约束,是带容量的车辆路径问题的进一步扩展,属于双容量约束路径规划问题。双策略蚁群算法在货物容量和续航里程的约束下,将蚁群搜索策略分为两类,即基于临近城市列表的局部搜索策略和全局搜索策略,在提高搜索效率的同时防止陷入局部优化。最后,通过阿里巴巴旗下菜鸟网络科技有限公司在上海的30组真实配送数据进行了测试,验证双策略蚁群算法显著优于一般蚁群算法。     

计及客户满意度的电动汽车物流配送路径规划与充放电管理

物流配送作为一种盈利型社会服务性行业,配送服务时间对客户满意度具有重要影响。论文考虑电动汽车(electric vehicle, EV)在配送途中和回到配送中心两个阶段,以物流配送成本最低和客户平均满意度最高为目标,构建了一种EV在换电模式下计及客户满意度的物流配送路径规划与充放电管理多目标优化模型,其中物流配送成本包括换电成本、车辆损耗成本以及慢速充放电成本。最后,以A-n29节点VRP基准测试系统插入四座换电站节点为例进行数值仿真,采用非支配排序遗传算法(Non-dominated sorting genetic algorithm, NSGA-II)对所提多目标优化模型进行求解,结果验证了所提方法的可行性和有效性。此外,论文进一步考查了EV慢速充放电管理对配电系统的影响,并对EV发车时间作了参数灵敏度分析,为管理者提供一些参考。     

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.     

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.     

Research on estimation model of the battery state of charge in a hybrid electric vehicle based on the classification and regression tree

ABSTRACT

In order to achieve the accurate estimation of state of charge (SOC) of the battery in a hybrid electric vehicle (HEV), this paper proposed a new estimation model based on the classification and regression tree (CART) which belongs to a kind of decision tree. The basic principle and modelling process of the CART decision tree were introduced in detail in this paper, and we used the voltage, current, and temperature of the battery in an HEV to estimate the value of SOC under the driving cycle. Meanwhile, we took the energy feedback of the HEV under the regenerative braking into consideration. The simulation data and experimental data were used to test the effectiveness of the estimation model of CART, and the results indicate that the proposed estimation model has high accuracy, the relative error of simulation is within 0.035, while the relative error of experiment is less than 0.05.     

Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach

Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra’s algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm.

     

Pricing and clearing combinatorial markets with singleton and swap orders

In this article we consider combinatorial markets with valuations only for singletons and pairs of buy/sell-orders for swapping two items in equal quantity. We provide an algorithm that permits polynomial time market-clearing and -pricing. The results are presented in the context of our main application: the futures opening auction problem. Futures contracts are an important tool to mitigate market risk and counterparty credit risk. In futures markets these contracts can be traded with varying expiration dates and underlyings. A common hedging strategy is to roll positions forward into the next expiration date, however this strategy comes with significant operational risk. To address this risk, exchanges started to offer so-called futures contract combinations, which allow the traders for swapping two futures contracts with different expiration dates or for swapping two futures contracts with different underlyings. In theory, the price is in both cases the difference of the two involved futures contracts. However, in particular in the opening auctions price inefficiencies often occur due to suboptimal clearing, leading to potential arbitrage opportunities. We present a minimum cost flow formulation of the futures opening auction problem that guarantees consistent prices. The core ideas are to model orders as arcs in a network, to enforce the equilibrium conditions with the help of two hierarchical objectives, and to combine these objectives into a single weighted objective while preserving the price information of dual optimal solutions. The resulting optimization problem can be solved in polynomial time and computational tests establish an empirical performance suitable for production environments.     

新能源移动充电车路径优化问题研究

在绿色城市背景下,新能源汽车的数量快速增长,现有公共充电设施的不完善使得移动充电服务应运而生。投入运营成本较高而利润低成为阻碍移动充电业务运营的瓶颈之一,如何通过科学合理的调度提高平台利润成为重要问题。本文研究了移动充电车队的调度和路径优化问题,以平台最大收益为目标,综合考虑顾客软时间窗、移动电池容量以及充电车续航里程等约束,建立数学规划模型;设计了一种最大最小蚁群算法,并通过数值实验验证了模型的合理性和算法的有效性,为移动充电企业运营提供决策参考。     

A tabu search algorithm for the periodic vehicle routing problem with multiple vehicle trips and accessibility restrictions

In this paper, we consider a periodic vehicle routing problem that includes, in addition to the classical constraints, the possibility of a vehicle doing more than one route per day, as long as the maximum daily operation time for the vehicle is not exceeded. In addition, some constraints relating to accessibility of the vehicles to the customers, in the sense that not every vehicle can visit every customer, must be observed. We refer to the problem we consider here as the site-dependent multi-trip periodic vehicle routing problem. An algorithm based on tabu search is presented for the problem and computational results presented on randomly generated test problems that are made publicly available. Our algorithm is also tested on a number of routing problems from the literature that constitute particular cases of the proposed problem. Specifically we consider the periodic vehicle routing problem; the site-dependent vehicle routing problem; the multi-trip vehicle routing problem; and the classical vehicle routing problem. Computational results for our tabu search algorithm on test problems taken from the literature for all of these problems are presented.     

Distributed algebraic multigrid for finite element computations

The Finite Element Method has been successfully applied to a variety of problems in engineering, medicine, biology, and physics. However, this method can be computationally intensive, particularly for problems in which an unstructured mesh of elements is generated. In such situations, the Algebraic Multigrid (AMG) can prove to be a robust method for solving the discretized linear systems that emerge from the problem. Unfortunately, AMG requires a large amount of storage (thus causing swapping on most sequential machines), and typically converges slowly. We show that distributing the algorithm across a cluster of workstations can help alleviate these problems. The distributed algorithm is run on a number of geomechanics problems that are solved using finite elements. The results show that distributed processing is extremely useful in maintaining the performance of the AMG algorithm with increasing problem size, particularly by reducing the amount of disk swapping required.     

Time domain simulation of Li-ion batteries using non-integer order equivalent electrical circuit

For electric vehicle (EV) or hybrid EV (HEV) development and integration of renewables in electrical networks, battery monitoring systems have to be more and more precise to take into account the state-of-charge and the dynamic behavior of the battery. Some non-integer order models of electrochemical batteries have been proposed in literacy with a good accuracy and a low number of parameters in the frequential domain. Nevertheless, time simulation of such models required to approximate this non-integer order system by an equivalent high integer order model. An adapted algorithm is then proposed in this article to simulate the non-integer order model without any approximation, thanks to the construction of a 3-order generalized state-space system. This algorithm is applied and validated on a 2.3 A.h Li-ion battery.     

A tabu search algorithm for the single vehicle routing allocation problem

In the single vehicle routing allocation problem (SVRAP) we have a single vehicle, together with a set of customers, and the problem is one of deciding a route for the vehicle (starting and ending at given locations) such that it visits some of the customers. Customers not visited by the vehicle can either be allocated to a customer on the vehicle route, or they can be isolated. The objective is to minimize a weighted sum of routing, allocation and isolation costs. One special case of the general SVRAP is the median cycle problem, also known as the ring star problem, where no isolated vertices are allowed. Other special cases include the covering tour problem, the covering salesman problem and the shortest covering path problem. In this paper, we present a tabu search algorithm for the SVRAP. Our tabu search algorithm includes aspiration, path relinking and frequency-based diversification. Computational results are presented for test problems used previously in the literature and our algorithm is compared with the results obtained by other researchers. We also report results for much larger problems than have been considered by others.     

An algorithm for the capacitated vehicle routing problem with route balancing

In this paper, we present a multi-objective evolutionary algorithm for the capacitated vehicle routing problem with route balancing. The algorithm is based on a formerly developed multi-objective algorithm using an explicit collective memory method, namely the extended virtual loser (EVL). We adapted and improved the algorithm and the EVL method for this problem. We achieved good results with this simple technique. In case of this problem the quality of the results of the algorithm is similar to that of other evolutionary algorithms.     

Memetic algorithms for solving job-shop scheduling problems

The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. In this paper, we developed a memetic algorithm (MA) for solving JSSPs. Three priority rules were designed, namely partial re-ordering, gap reduction and restricted swapping, and used as local search techniques in our MA. We have solved 40 benchmark problems and compared the results obtained with a number of established algorithms in the literature. The experimental results show that MA, as compared to GA, not only improves the quality of solutions but also reduces the overall computational time.     

The single vehicle pickup and delivery problem with time windows: intelligent operators for heuristic and metaheuristic algorithms

The single vehicle pickup and delivery problem with time windows is an important practical problem, yet only a few researchers have tackled it. In this research, we compare three different approaches to the problem: a genetic algorithm, a simulated annealing approach, and a hill climbing algorithm. In all cases, we adopt a solution representation that depends on a duplicate code for both the pickup request and its delivery. We also present an intelligent neighborhood move, that is guided by the time window, aiming to overcome the difficult problem constraints efficiently. Results presented herein improve upon those that have been previously published.     

Minimizing grid capacity in preemptive electric vehicle charging orchestration: Complexity,exact and heuristic approaches

Unlike refueling an internal combustion engine vehicle, charging electric vehicles is time-consuming and results in higher energy consumption. Hence, charging stations will face several challenges in providing high-quality charging services when the adoption of electric vehicles increases. These charging infrastructures must satisfy charging demands without overloading the power grid. In this work, we investigate the problem of scheduling the charging of electric vehicles to reduce the maximum peak power while satisfying all charging demands. We consider a charging station where the installed chargers deliver a preemptive constant charging power. These chargers can either be identical or non-identical. For both cases, we address two optimization problems. First, we study the problem of finding the minimum number of chargers needed to plug a set of electric vehicles giving different arrival and departure times and required energies. We prove that this problem belongs to the complexity class P, and we provide polynomial-time algorithms. Then, we study the problem of minimizing the power grid capacity. For identical chargers, we prove that the problem is polynomial, whereas it is NP-hard in the case of non-identical chargers. We formulate these problems as a mixed-integer linear programming model for both cases. To obtain near-optimal solutions for the NP-hard problem, we propose a heuristic and an iterated local search metaheuristic. Through computational results, we demonstrate the effectiveness of the proposed approaches in terms of reducing the grid capacity.     

Optimizing departure times in vehicle routes

Most solution methods for the vehicle routing problem with time windows (VRPTW) develop routes from the earliest feasible departure time. In practice, however, temporary traffic congestion make such solutions non-optimal with respect to minimizing the total duty time. Furthermore, the VRPTW does not account for driving hours regulations, which restrict the available travel time for truck drivers. To deal with these problems, we consider the vehicle departure time optimization (VDO) problem as a post-processing of a VRPTW. We propose an ILP formulation that minimizes the total duty time. The results of a case study indicate that duty time reductions of 15% can be achieved. Furthermore, computational experiments on VRPTW benchmarks indicate that ignoring traffic congestion or driving hours regulations leads to practically infeasible solutions. Therefore, new vehicle routing methods should be developed that account for these common restrictions. We propose an integrated approach based on classical insertion heuristics.     

Economic lot sizes and vehicle scheduling

In this paper a relationship between the vehicle scheduling problem and the dynamic lot size problem is considered. For the latter problem we assume that order quantities for different products can be determined separately. Demand is known over our n-period production planning horizon. For a certain product our task is to decide for each period if it should be produced or not. If it is produced, what is its economic lot size? Our aim here is to minimize the combined set-up and inventory holding costs. The optimal solution of this problem is given by the well-known Wagner-Whitin dynamic lot size algorithm. Also many heuristics for solving this problem have been presented. In this article we point out the analogy of the dynamic lot size problem to a certain vehicle scheduling problem. For solving vehicle scheduling problems the heuristic algorithm developed by Clark and Wright in very often used. Applying this algorithm to the equivalent vehicle scheduling problem we obtain by analogy a simple heuristic algorithm for the dynamic lot size problem. Numerical results indicate that computation time is reduced by about 50% compared to the Wagner-Whitin algorithm. The average cost appears to be approximately 0.8% higher than optimum.