考虑时变速度的多车场绿色车辆路径模型及优化算法

针对多车场绿色车辆路径问题,根据顾客的坐标位置,采用K-means聚类方法将顾客分配给不同的车场;考虑时变速度和实时载重对车辆油耗和碳排放的影响,确定车辆油耗和碳排放的度量函数;在此基础上,以车辆油耗成本、碳排放成本、车辆使用成本、驾驶员工资以及时间窗惩罚成本之和最小化作为优化目标,构建多车场绿色车辆路径模型,并根据模...     

A robust green traffic-based routing problem for perishable products distribution

Nowadays, transportation and logistics are considered as the drivers of economic development in the countries due to their impacts on the main variables of the country's economy such as production, employment, price, and the cost of living. Statistics indicate that fuel consumption constructs a major part of transportation costs, where its optimization leads to the creation of an energy-efficient and sustainable transportation system. On the other hand, vehicles' traffic is also one of the main criteria affecting the travel time of vehicles between demand nodes in a supply chain, increasing fuel consumption, and, consequently, damaging effects of greenhouse gasses. In this paper, a novel robust mixed-integer linear programming model is developed for a green vehicle routing problem with intermediate depots considering different urban traffic conditions, fuel consumption, time windows of services, and uncertain demand for perishable products. To validate and solve the suggested model, CPLEX solver of GAMS software is employed as an exact method. Finally, a case study problem is investigated to evaluate the applicability of the proposed model and determine the optimal managerial insights and policies in the real-world conditions using sensitivity analyses. Moreover, a novel robustness threshold comparison is conducted to find the optimal level of budget assignment.     

基于混合整数规划的智能网联车冲突区时序优化模型

智能网联车的大规模应用为交通冲突区域的优化与管理提供了新的机遇和挑战.为保证冲突区域车辆的汇入安全,提高冲突区域车流的运行效率,引入优化时间区间的概念,以车辆平均延误为优化目标,车辆通过冲突区域的最小安全时间间隔为约束条件,构建车辆进入冲突区域时序的混合整数线性规划模型.为研究不同流量状态对模型结果的影响,设计不同流量场景的数值仿真实验.结果表明:与不优化时序相比,所提出的模型和算法能够有效地减少车辆的延误和油耗,且最大能够减少54.23%的车辆延误和34.36%的燃油消耗.     

A multi-objective green UAV routing problem

This paper introduces an Unmanned Aerial Vehicle (UAV) heterogeneous fleet routing problem, dealing with vehicles limited autonomy by considering multiple charging stations and respecting operational requirements. A green routing problem is designed for overcoming difficulties that exist as a result of limited vehicle driving range. Due to the large amount of drones emerging in the society, UAVs use and efficiency should be optimized. In particular, these kinds of vehicles have been recently used for delivering and collecting products. Here, we design a new real-time routing problem, in which different types of drones can collect and deliver packages. These aerial vehicles are able to collect more than one deliverable at the same time if it fits their maximum capacity. Inspired by a multi-criteria view of real systems, seven different objective functions are considered and sought to be minimized using a Mixed-Integer Linear Programming (MILP) model solved by a matheuristic algorithm. The latter filters the non-dominated solutions from the pool of solutions found in the branch-and-bound optimization tree, using a black-box dynamic search algorithm. A case of study, considering a bi-layer scenario, is presented in order to validate the proposal, which showed to be able to provide good quality solutions for supporting decision making.     

城市物流配送的混合车辆路径规划模型与优化算法

针对城市部分区域限行、物流系统中燃油车与电动车同时并存的实际情况,综合考虑客户需求量、服务时间、电动车行驶里程、已有充电设施、部分充电策略、燃油车油耗与碳排放等因素,以车辆使用固定成本、驾驶员工资、电动车的充电成本、燃油车的油耗与碳排放成本之和最小为目标构建混合车辆路径规划模型.根据模型特征设计一种改进蚁群算法求解,并采用多类型算例进行实验.实验结果表明,所提方法能在非常短的时间内给出符合决策者目标的混合车辆路径规划方案,有效降低总配送成本,减少燃油车油耗与碳排放,具有合理性、可行性与有效性.     

基于配送收益均衡的多目标绿色车辆路径优化算法

针对电商平台物流中的碳排放成本较大以及配送过程中配送员收益不均衡的情况,为满足平台减少物流成本和人力成本的需求,提高车辆配送效率,降低碳排放量,实现低碳绿色出行,研究带有时间窗、配送收益均衡的多目标绿色车辆路径规划问题,并设计混合智能求解算法.首先,建立基于行驶速度的燃油消耗、基于模糊客户满意度的惩罚成本和配送收益均衡函数,构建以最小化燃油消耗量、惩罚成本和配送收益方差为目标的多目标绿色车辆路径模型;然后,将变邻域搜索算子融入NSGA-II算法,设计求解上述模型的多目标进化优化算法,以提高算法的寻优性能;最后,选择Solomon中的18个测试数据集进行实验,通过与2个模型和3种算法的超体积值和knee点值进行对比,验证所提出模型的可行性和算法的有效性,为降低碳排放量、实现低碳绿色出行提供新方案.     

基于智能垃圾桶的垃圾分类动态收运路径优化问题研究

针对城市生活垃圾分类收运过程中存在的环境二次污染和垃圾产生量不确定性等问题,提出了一种基于智能垃圾桶的动态收运车辆路径优化方法。建立以最小化碳排放成本、燃油消耗成本、固定成本和车辆延迟到达惩罚成本为目标的动态车辆路径优化模型。采用滚动时域的方式将动态问题转换为一系列静态问题,并设计两阶段算法进行求解。首先采用粒子群算法对收运车辆路径进行规划,而后在每个时域末,综合考虑待清运垃圾桶的位置和垃圾量、垃圾收运车辆的位置和装载量以动态调整现有车辆路径。研究结果表明,相较于传统的静态收运方案,动态垃圾收运方案能够在降低车辆运输成本和碳排放成本的同时,显著降低由于清运不及时造成的环境二次污染的风险。     

Mitigation of greenhouse gas emissions in vehicle routing problems with backhauling

In this paper, the decrease in the emission of greenhouse gases is evaluated using the vehicle routing problem with backhauls and time windows by considering the energy required for each route and estimating the load and distance between customers. Using a scatter search, problems from the literature with up to 100 randomly distributed customers were analyzed. Our results indicate that the distance traveled and the transportation costs increase as the required energy decreases, but the amount of fuel consumed also decreases; therefore, the emission of greenhouse gases also decreases. In some cases, the number of vehicles remains the same or increases because a better solution is achieved with shorter, better distributed routes. In addition, using a cooperative game approach, we found that different transportation companies operating jointly results in decreased emissions as well as operating costs.     

Variable Neighborhood Search heuristic for the Inventory Routing Problem in fuel delivery

In this paper we observe the extension of the vehicle routing problem (VRP) in fuel delivery that includes petrol stations inventory management and which can be classified as the Inventory Routing Problem (IRP) in fuel delivery. The objective of the IRP is to minimize the total cost of vehicle routing and inventory management. We developed a Variable Neighborhood Search (VNS) heuristic for solving a multi-product multi-period IRP in fuel delivery with multi-compartment homogeneous vehicles, and deterministic consumption that varies with each petrol station and each fuel type. The stochastic VNS heuristic is compared to a Mixed Integer Linear Programming (MILP) model and the deterministic “compartment transfer” (CT) heuristic. For three different scale problems, with different vehicle types, the developed VNS heuristic outperforms the deterministic CT heuristic. Also, for the smallest scale problem instances, the developed VNS was capable of obtaining the near optimal and optimal solutions (the MILP model was able to solve only the smallest scale problem instances).     

交通信息物理系统中的车辆协同运行优化调度

运输成本及温室气体的排放是衡量智能交通系统的重要指标，有效的运输调度可以降低运输成本和环境损害.针对路网中集成环保型货车的运输问题，本文提出一种基于交通信息物理系统（Transportation cyber physical system，TCPS）的大规模车辆协同调度及合并方案，以最大限度地降低运输成本和碳排放量.首先，采用局部调度策略，结合领队车辆选择算法及聚类分析，构建可合并车辆集合；然后，通过数学规划方法，实现每个车队集合中车辆路径与速度的改进优化处理；最后，通过突发情况的简易处理说明本文调度策略的可扩展性.仿真实验表明，用本文方法将车辆编组合并成车队行驶，较固定路径合并策略可显著降低路网中货运车辆的整体油耗.     

Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model

Today’s growth in the level of traffic in cities is leading to both congestion and environmental pollution (exhaust emissions and noise), as well as increased costs. Traffic congestion makes cities less pleasant places to live in, a particular problem being the negative impact on health as a result of increased exhaust emissions. In addition to these emissions, another major effect of transport which can lead to serious health problems is noise (EEA, 2013a, 2013b). There is a strong tendency in the world towards the development of “clean” motor vehicles that do not pollute the environment, that is, that do not emit harmful substances in their exhaust fumes and which create less noise without causing other types of pollution. The growth in the influence of transport on the environment has resulted in planners formulating procedures which take into account the effect of traffic on the quality of life in urban areas. This paper presents a model for the routing of light delivery vehicles by logistics operators. The model presented takes into account the fact that logistics operators have a limited number of environmentally friendly vehicles (EFV) available to them. When defining a route, EFV vehicles and environmentally unfriendly vehicles (EUV) are considered separately. For solving the problem of routing in the model, an adaptive neural network was used which was trained by a simulated annealing algorithm. An adaptive neural network was used for assessing the performance of the network branches. The input parameters of the neural network were the logistics operating costs and environmental parameters (exhaust emissions and noise) for the given vehicle route. Each of the input parameters of the neural network was thoroughly examined. The input parameters were broken down into elements which further describe the state of the environment, noise and logistics operating costs. After obtaining the performance of the network links for calculating the route for EFV and EUV vehicles a modified Clark–Wright algorithm was used. The proposed model was tested on a network which simulates the conditions in the very centre of Belgrade. All of the input parameters of the model were obtained on the basis of 40 automatic measuring stations for monitoring the air quality (SEA, 2012).     

Control strategies for high-power electric vehicles powered by hydrogen fuel cell,battery and supercapacitor

Problems relating to oil supply, pollution, and green house effects justify the need for developing of new technologies for transportation as a replacement for the actual technology based on internal combustion engines (ICE). Fuel cells (FCs) are seen as the best future replacement for ICE in transportation applications because they operate more efficiently and with lower emissions. This paper presents a comparative study performed in order to select the most suitable control strategy for high-power electric vehicles powered by FC, battery and supercapacitor (SC), in which each energy source uses a DC/DC converter to control the source power and adapt the output voltage to the common DC bus voltage, from where the vehicle loads are supplied.Five different controls are described for this kind of hybrid vehicles: a basic control based on three operation modes of the hybrid vehicle depending on the state of charge (SOC) of the battery (operation mode control); a control strategy based on control loops connected in cascade, whose aim is to control the battery and SC SOC (cascade control); a control based on the technique of equivalent fuel consumption, called equivalent consumption minimization strategy (ECMS); and two based on control techniques very used nowadays, the first one of them is a fuzzy logic control and the second one is a predictive control. These control strategies are tested and compared by applying them to a real urban street railway. The simulation results reflect the optimal performance of the presented control strategies and allow selecting the best option for being used in this type of high-power electric vehicles.     

Open vehicle routing problem with demand uncertainty and its robust strategies

We investigate the open vehicle routing problem with uncertain demands, where the vehicles do not necessarily return to their original locations after delivering goods to customers. We firstly describe the customer’s demand as specific bounded uncertainty sets with expected demand value and nominal value, and propose the robust optimization model that aim at minimizing transportation costs and unsatisfied demands in the specific bounded uncertainty sets. We propose four robust strategies to cope with the uncertain demand and an improved differential evolution algorithm (IDE) to solve the robust optimization model. Then we analyze the performance of four different robust strategies by considering the extra costs and unmet demand. Finally, the computational experiments indicate that the robust optimization greatly avoid unmet demand while incurring a small extra cost and the optimal return strategy is the best strategy by balancing the trade-off the cost and unmet demand among different robust strategies.     

Multi-objective optimization of the maritime cargo routing and scheduling problem

This paper addresses the multi-objective maritime cargo routing and scheduling problem, in which the delivery of bulk products from pickup to delivery ports is served by a heterogeneous fleet of vessels. A mixed integer linear programming (MILP) model is formulated to simultaneously minimize total operation costs, the scheduling makespan, and delays in selected deliveries. The model accounts for several real features, such as time windows, capacity of the vessel's compartments, and ports requirements. A fuzzy weighted max–min method was applied to solve the problem. Two heuristics were developed to effectively handle the complex generated MILP models during the solution process. Experiments were conducted to evaluate the optimization approach using real-life instances provided by a fertilizer company. Finally, a case study shows that the developed model and algorithmic framework are flexible and effective in coping with real problems, incorporating specific business rules from different companies.     

Maintenance costs and makespan minimization for assembly permutation flow shop scheduling by considering preventive and corrective maintenance

The joint optimization of production scheduling and maintenance planning has a significant influence on production continuity and machine reliability. However, limited research considers preventive maintenance (PM) and corrective maintenance (CM) in assembly permutation flow shop scheduling. This paper addresses the bi-objective joint optimization of both PM and CM costs in assembly permutation flow shop scheduling. We also propose a new mixed integer linear programming model for the minimization of the makespan and maintenance costs. Two lemmas are inferred to relax the expected number of failures and CM cost to make the model linear. A restarted iterated Pareto greedy (RIPG) algorithm is applied to solve the problem by including a new evaluation of the solutions, based on a PM strategy. The RIPG algorithm makes use of novel bi-objective-oriented greedy and referenced local search phases to find non-dominated solutions. Three types of experiments are conducted to evaluate the proposed MILP model and the performance of the RIPG algorithm. In the first experiment, the MILP model is solved with an epsilon-constraint method, showing the effectiveness of the MILP model in small-scale instances. In the remaining two experiments, the RIPG algorithm shows its superiority for all the instances with respect to four well-known multi-objective metaheuristics.     

A column generation approach to the heterogeneous fleet vehicle routing problem

We consider a vehicle routing problem with a heterogeneous fleet of vehicles having various capacities, fixed costs and variable costs. An approach based on column generation (CG) is applied for its solution, hitherto successful only in the vehicle routing problem with time windows. A tight integer programming model is presented, the linear programming relaxation of which is solved by the CG technique. A couple of dynamic programming schemes developed for the classical vehicle routing problem are emulated with some modifications to efficiently generate feasible columns. With the tight lower bounds thereby obtained, the branch-and-bound procedure is activated to obtain an integer solution. Computational experience with the benchmark test instances confirms that our approach outperforms all the existing algorithms both in terms of the quality of solutions generated and the solution time.     

考虑拥堵区域的多车型绿色车辆路径问题优化

针对降低物流配送过程中产生的碳排放问题,从绿色环保角度出发,提出一种考虑交通拥堵区域的多车型物流配送车辆的绿色车辆路径问题（GVRP）。首先分析不同类型车辆、不同拥堵状况对车辆行驶路线规划的影响,然后引入基于车辆行驶速度和载重的碳排放速率度量函数;其次以车辆管理使用费用和油耗碳排放成本最小作为优化目标,构建双目标绿色车辆路径模型;最后根据模型的特点设计一种融合模拟退火算法的混合差分进化算法对问题进行求解。通过实验仿真验证模型和算法可以有效规避拥堵区域,与只使用单一4 t车型配送相比,所提模型总成本降低了1.5%,油耗碳排放成本降低了4.3%;和以行驶距离最短为目标的模型相比,所提模型的总配送成本降低了8.1%。说明该模型提高物流企业的经济效益也促进了节能减排。同时所提算法与基本差分算法相比,总配送成本可以降低3%~6%;与遗传算法相比,优化效果更明显,总配送成本可以降低4%~11%,证明该算法更具有优越性。综上所提模型和算法可以为物流企业城市配送路径决策提供良好的参考依据。     

生鲜电商配送的TDVRPTW研究: 基于经济成本与环境成本兼顾的视角

基于经济成本与环境成本兼顾的视角,研究时变网络下生鲜电商配送的带时间窗车辆路径问题(TDVRPTW),综合考虑车辆时变行驶速度、车辆油耗、碳排放、生鲜农产品的易腐易损性、客户时间窗与最低新鲜度限制等因素,设计跨时间段的路段行驶时间计算方法,引入农产品新鲜度度量函数与碳排放率度量函数.在此基础上,以经济成本与环境成本之和最小为目标构建具有最低新鲜度限制的TDVRPTW数学模型,并根据模型特点设计一种自适应改进蚁群算法求解.最后采用案例验证所提出方法能有效规避交通拥堵时间段、降低总配送成本、促进物流配送领域的节能减排.     

Generalized mixed integer and VNS heuristic approach to solving the multisize containers drayage problem

This paper addresses the problem of vehicle routing in drayage operations, where vehicles can carry containers of different sizes. The multisize container drayage problem with time windows is modeled as a multiple matching problem and formulated as a mixed integer linear program (MILP) model. The proposed MILP model determines optimal pickup and delivery routes of vehicles and is applicable to any type of vehicles capable of simultaneously transporting any arbitrary number of 20‐ and 40‐ft containers. To solve larger sized problems, we proposed a variable neighborhood search (VNS) heuristic. Both MILP and VNS approaches have been tested on numerous test instances and their performances are reported.     

汽车综合性能检测线的建模与短期调度

研究了汽车综合性能检测线批次检测车辆的建模与短期调度问题. 调度的目标是使给定数量车辆检测时间最短. 该过程是一个变存储策略的序贯多目的批处理过程. 建立调度数学模型时, 把Moon等人[1] 的工作推广到了NW, NIS, FIS存储策略, 并根据汽车综合性能检测线的实际情况建立了顺序相关存储策略和人力资源约束的数学表达. 该数学模型表达为一个混合整数非线性规划 (MINLP), 经线性化处理转化为一个混合整数线性规划问题 (MILP), 求解使用了分枝定界法 (BAB). 计算结果对汽车综合性能检测线