Two memetic algorithms for heterogeneous fleet vehicle routing problems

The vehicle routing problem (VRP) plays an important role in the distribution step of supply chains. From a depot with identical vehicles of limited capacity, it consists in determining a set of vehicle trips of minimum total length, to satisfy the demands of a set of customers. In general, the number of vehicles used is a decision variable. The heterogeneous fleet VRP (HFVRP or HVRP) is a natural generalization with several vehicle types, each type being defined by a capacity, a fixed cost, a cost per distance unit and a number of vehicles available. The vehicle fleet mix problem (VFMP) is a variant with an unlimited number of vehicles per type. This paper presents two memetic algorithms (genetic algorithms hybridized with a local search) able to solve both the VFMP and the HVRP. They are based on chromosomes encoded as giant tours, without trip delimiters, and on an optimal evaluation procedure which splits these tours into feasible trips and assigns vehicles to them. The second algorithm uses a distance measure in solution space to diversify the search. Numerical tests on standard VFMP and HFVRP instances show that the two methods, especially the one with distance measure, compete with published metaheuristics and improve several best-known solutions.     

Identifying Critical Components of a Public Transit System for Outbreak Control

Modern public transport networks provide an efficient medium for the spread of infectious diseases within a region. The ability to identify components of the public transit system most likely to be carrying infected individuals during an outbreak is critical for public health authorities to be able to plan for outbreaks, and control their spread. In this study we propose a novel network structure, denoted as the vehicle trip network, to capture the dynamic public transit ridership patterns in a compact form, and illustrate how it can be used for efficient detection of the high risk network components. We evaluate a range of network-based statistics for the vehicle trip network, and validate their ability to identify the routes and individual vehicles most likely to spread infection using simulated epidemic scenarios. A variety of outbreak scenarios are simulated, which vary by their set of initially infected individuals and disease parameters. Results from a case study using the public transit network from Twin Cities, MN are presented. The results indicate that the set of transit vehicle trips at highest risk of infection can be efficiently identified, and are relatively robust to the initial conditions of the outbreak. Furthermore, the methods are illustrated to be robust to two types of data uncertainty, those being passenger infection levels and travel patterns of the passengers.     

An adaptive guidance approach for the heuristic solution of a minimum multiple trip vehicle routing problem

One of the most important problems in combinatorial optimization is the well-known vehicle routing problem (VRP), which calls for the determination of the optimal routes to be performed by a fleet of vehicles to serve a given set of customers. Recently, there has been an increasing interest towards extensions of VRP arising from real-world applications. In this paper we consider a variant in which time windows for service at the customers are given, and vehicles may perform more than one route within a working shift. We call the resulting problem the minimum multiple trip VRP (MMTVRP), where a “multiple trip” is a sequence of routes corresponding to a working shift for a vehicle. The problem objective is to minimize the overall number of the multiple trips (hence the size of the required fleet), breaking ties in favor of the minimum routing cost.     

On-line reschedule optimization for passenger railways in case of emergencies

This paper deals with a special case of the Vehicle Re-Scheduling Problem (VRSP) for passenger railways in case of emergencies. These emergencies arise when a service line is disturbed due to a huge and unexpected demand which exceeds the service line capacity. In order to provide a proper response to this type of emergency, an on-line optimization model based on a discrete-event simulation model is proposed to provide and support decisions about reassigning vehicles from other lines of the transport system to the disturbed line. The simulation model is based on a dynamic network loading model, a dynamic demand generation model, and a schedule-based service network. The on-line optimization model has been solved using two greedy heuristics which automatically generate near-optimal decisions about vehicle reassignments considering the total time in system for passengers as the minimization criterion. An experimental study, based on a synthetic network and the Madrid Regional Railway network, shows that the second proposed heuristic provides near-optimal reassignment decisions with short computation times compatible with real-time use.     

A GRASP×ELS for the vehicle routing problem with basic three-dimensional loading constraints

This paper addresses an extension of the capacitated vehicle routing problem where the client demand consists of three-dimensional weighted items (3L-CVRP). The objective is to design a set of trips for a homogeneous fleet of vehicles based at a depot node which minimizes the total transportation cost. Items in each vehicle trip must satisfy the three-dimensional orthogonal packing constraints. This problem is strongly connected to real-life transportation systems where the packing of items to be delivered by each vehicle can have a significant impact on the routes. We propose a new way to solve the packing sub-problem. It consists of a two-step procedure in which the z-constraints are first relaxed to get a (x,y) positioning of the items. Then, a compatible z-coordinate is computed to get a packing solution. Items can be rotated but additional constraints such as item fragility, support and LIFO are not considered. This method is included in a GRASP×ELS hybrid algorithm dedicated to the computation of VRP routes. The route optimization alternates between two search spaces: the space of VRP routes and the space of giant trips. The projection from one to the other is done by dedicated procedures (namely the Split and the concatenation algorithms). Moreover, a Local Search is defined on each search space. Furthermore, hash tables are used to store the result of the packing checks and thus save a substantial amount of CPU time. The effectiveness of our approach is illustrated by computational experiments on 3L-CVRP instances from the literature. A new set of realistic instances based on the 96 French districts are also proposed. They range from 19 nodes for the small instances to 255 nodes for the large instances and they can be stated as realistic since they are based on true travel distances in kilometers between French cities. The impact of the hash tables is illustrated as well.     

A clonal selection algorithm for urban bus vehicle scheduling

The bus vehicle scheduling problem addresses the task of assigning vehicles to cover the trips in a timetable. In this paper, a clonal selection algorithm based vehicle scheduling approach is proposed to quickly generate satisfactory solutions for large-scale bus scheduling problems. Firstly, a set of vehicle blocks (consecutive trips by one bus) is generated based on the maximal wait time between any two adjacent trips. Then a subset of blocks is constructed by the clonal selection algorithm to produce an initial vehicle scheduling solution. Finally, two heuristics adjust the departure times of vehicles to further improve the solution. The proposed approach is evaluated using a real-world vehicle scheduling problem from the bus company of Nanjing, China. Experimental results show that the proposed approach can generate satisfactory scheduling solutions within 1 min.     

An exact decomposition method to save trips in cooperative pickup and delivery based on scheduled trips and profit distribution

Compared to the non-cooperative mode, the cooperative mode is a powerful way to reduce operational cost in pickup and delivery service. In order to protect business sensitive information, sometimes participants are unwilling to open the customer's detailed information. Thus, we utilize the publishable trip scheduled results to compute the saved trips brought by cooperation. A mathematical model minimizing trips of cooperation is proposed. To obtain the exact solution, we define the cooperative trip set. We prove that only when cooperative trip set exists it is possible to save trips by cooperation. For a two-trip cooperative trip set, we exactly obtain the saved trips by enumerating all feasible cooperative cases. For a K-trip cooperative trip set, we propose an exact method to obtain the saved trips by decomposing it to at most K-1 two-trip cooperative trip sets. Computational complexity of the based-on-decomposition exact algorithm is O(N), where N is the total number of trips. Using the based-on-decomposition algorithm, we calculate the exact Shapley value to distribute profit. To empirically verify the exact method, we perform the extensive experiment cases of the real cooperative pickup and delivery service, i.e., “picking up and delivering customers to airport service” (PDCA).     

基于AKF和RLS的车辆簧载质量辨识研究

实际应用中,车辆负载会随着乘客和货物的变化而发生显著改变.提出结合自适应卡尔曼滤波器(AKF)与递推最小二乘算法(RLS)进行车辆簧载质量的在线辨识.首先,采集四分之一车辆悬架的簧载振动加速度、动行程及车轮垂向加速度信号,对车辆悬架系统中的簧载质量和车轮的绝对速度进行估计,进而由遗忘因子递推最小二乘算法辨识车辆簧载质量.分析了在不同路面等级下,卡尔曼滤波器的过程噪声协方差和测量噪声协方差对悬架状态估计精度的影响.仿真结果显示,在选取与车辆行驶路面等级匹配的过程噪声协方差和测量噪声协方差时,车辆悬架状态参数的估计精度较高,并能够在线准确地辨识得到车辆的簧载质量值.     

Review of real-time vehicle schedule recovery methods in transportation services

This paper presents a comprehensive review on methods for real-time schedule recovery in transportation services. The survey concentrates on published research on recovery of planned schedules in the occurrence of one or several severe disruptions such as vehicle breakdowns, accidents, and delays. Only vehicle assignment and rescheduling are reviewed; crew scheduling and passenger logistics problems during disruptions are not. Real-time vehicle schedule recovery problems (RTVSRP) are classified into three classes: vehicle rescheduling, for road-based services, train-based rescheduling, and airline schedule recovery problems. For each class, a classification of the models is presented based on problem formulations and solution strategies. The paper concludes that RTVSRP is a challenging problem that requires quick and good quality solutions to very difficult and complex situations, involving several different contexts, restrictions, and objectives. The paper also identifies research gaps to be investigated in the future, stimulating research in this area.     

A multi-start evolutionary local search for the two-dimensional loading capacitated vehicle routing problem

This paper addresses an extension of the capacitated vehicle routing problem where customer demand is composed of two-dimensional weighted items (2L-CVRP). The objective consists in designing a set of trips minimizing the total transportation cost with a homogenous fleet of vehicles based on a depot node. Items in each vehicle trip must satisfy the two-dimensional orthogonal packing constraints. A GRASP×ELS algorithm is proposed to compute solutions of a simpler problem in which the loading constraints are transformed into resource constrained project scheduling problem (RCPSP) constraints. We denote this relaxed problem RCPSP-CVRP. The optimization framework deals with RCPSP-CVRP and lastly RCPSP-CVRP solutions are transformed into 2L-CVRP solutions by solving a dedicated packing problem. The effectiveness of our approach is demonstrated through computational experiments including both classical CVRP and 2L-CVRP instances. Numerical experiments show that the GRASP×ELS approach outperforms all previously published methods.     

Vehicle routing problem with time windows considering overtime and outsourcing vehicles

The vehicle routing problem with time windows (VRPTW) is an important problem in third-party logistics and supply chain management. We extend the VRPTW to the VRPTW with overtime and outsourcing vehicles (VRPTWOV), which allows overtime for drivers and the possibility of using outsourced vehicles. This problem can be applied to third-party logistics companies for managing central distributor-local distributors, local distributor-retailers (or customers), and manufacturers. We developed a mixed integer programming model, a genetic algorithm (GA), and a hybrid algorithm based on simulated annealing. The computational results demonstrate the efficiency of the developed algorithms. We also develop a decision support system for the VRPTWOV that is equipped with a vehicle route rescheduling function for realistic situations based on the GA.     

Optimal bus scheduling and fleet selection: A programming approach

A set of nonlinear programming models is developed here to determine an optimal vehicle-mix and fleet selection. The institutional framework assumed is as follows: a given number of n vehicles is available for dispatch from some source to serve passengers along fixed routes; the arriving passengers follow a simple queueing process, i.e. if a passenger cannot be served in a given time interval i, he has to wait until the next interval, thus forming a queue. One wishes to choose the number of buses n_i, to assign in interval i (i = 1, 2, …, 9) so as to optimize a criterion function, which includes such components as costs of operating the fleet, gross returns per vehicle per trip and the implicit cost of passengers' waiting time. Stochastic aspects of travel demand are handled through some formulations of stochastic programming. It is shown that under suitable simplifying assumptions, stochastic linear programming methods could provide good approximations to the more general nonlinear programming models.     

Efficient Insertion Heuristic Algorithms for Multi-Trip Inventory Routing Problem with Time Windows,Shift Time Limits and Variable Delivery Time

Networks and Spatial Economics - Efficient insertion heuristic algorithms allowing multi trips per vehicle (EIH-MT) and allowing a single trip per vehicle with post-processing greedy heuristic...     

EasyRide: active transponders for a fare collection system

The EasyRide ticketing system relies on radio frequency identification cards or tags to monitor passenger access to public transportation. Cards equipped with a miniature radio communication module identify passengers and record the information necessary to collect fares. Passengers equipped with an electronic card can board vehicles without prior ticket purchase. Equipment installed on each vehicle automatically detects the card and registers the passenger's entry and exit locations. The collected data, which contains the card and vehicle identification, stop names, and time stamps, is then forwarded from the vehicle to a fare calculation and billing system. Passengers only need to carry the card in their pockets or luggage during the trip, and the whole access control process is executed unnoticeably     

一种求解多车辆合乘匹配问题的适应性算法

车辆合乘匹配问题是研究如何通过优化车辆路线及车辆一乘客匹配来搭乘尽量多的乘客的问题。目前国内 外的研究多存在模型单一、脱离实际、算法效率不高等问题。针对该问题,提出一种基于吸引粒子群算法的问题求解 方法。通过吸引粒子群算法进行多车辆问题向单车辆问题的转化,形成车辆同乘客之间的初次匹配。根据初次匹配 结果利用先验聚类的思想将初次匹配结果进行排序,寻找较优需求序列排序方式。最后,通过相应的匹配再优化策略 将需求序列进行再优化。对比实验表明,基于吸引粒子群算法的问题求解方式能以较高的搭乘成功率以及较低的花 费完成车辆合乘匹配问题。     

Scheduling the vehicles of bus rapid transit systems: a case study

Bus rapid transit (BRT) is a cost-efficient, traffic-free bus-based transportation system competing with subways. There are 205 municipalities around the world that implemented their own BRT systems. Istanbul, having the sixth-most congested traffic in the world, built its own BRT system (Metrobüs), which serves more than 830,000 people (6.45% of all public transportation usage) in a day with 6254 trips covered by its current fleet of 496 vehicles. In this study, we model the vehicle scheduling problem of Metrobüs as a multiple depot vehicle scheduling problem. The model aims to minimize the fleet size and total deadhead kilometers while covering all timetabled trips. We propose a new heuristic, trips merger (TM), to solve the model and show that there exists cost reduction opportunities in terms of both fleet size and deadhead kilometers. The proposed heuristic is a member of the state-space reduction heuristics family, which first reduces the problem size, then solves the reduced problem. Computational study reveals that TM performed better than the existing state-space reduction heuristics for the Metrobüs case.     

Task effects on fatigue symptoms in overnight driving.

This study examined the effects of task and time-on-task on fatigue symptoms in overnight driving. Four participants drove an instrumented car 1200 km overnight and completed the same trip as passengers on another night. Subjective ratings of drowsiness, eye blink frequency and duration, microsleeps, and steering-wheel inputs were analysed as a function of time-on-task, and for separate samples when meeting oncoming heavy vehicles. Four video cameras were used to monitor the road view and the face of both the driver and passenger. In terms of eye closure duration, the reported microsleeps were shorter while driving (mean = 0.7 s, SD = 0.2 s) than as a passenger (mean = 2.6 s, SD = 2.0 s). Blink frequency increased with time-on-task as expected, indicating tiredness, and decreased when approaching an oncoming heavy vehicle, indicating attentive response to a potential critical situation. No consistent effect of time-on-task on high-frequency steering-wheel inputs when meeting oncoming heavy vehicles was found. The results raise the important question of what makes a driver wake from a microsleep earlier than a passenger and, given proper monitoring of long eyelid closures, what the proper intervention should be.     

遗传算法在多类型的车辆人员排班系统中的应用与研究

公交车辆人员排班的主要问题就是在给定时间点和车次数的情况下,以最小代价覆盖所有的车次。与以往都是针对单类型车辆的人员排班不同,该文主要提供对多类型的车辆人员排班的支持。首先利用高效的Auction算法获取代价最小的车次分组并根据分组情况分配车辆的营运类型;然后使用遗传算法进行随机化搜索以获得最优解。实验表明,遗传算法应用于多类型的公交车辆人员排班具有很好的效果。     

Optimal phase searching of PTS using modified genetic algorithm for PAPR reduction in OFDM systems

In this paper,a novel genetic algorithm assisted partial transmit sequence(NGA-PTS)is proposed to reduce the peak-to-average power ratio(PAPR)of orthogonal frequency division multiplexing(OFDM).However,the search complexity of the optimum PTS(OPTS)scheme is too large for the typical number of sub-blocks.Therefore,some artificial intelligence methods,such as genetic algorithm technique,and particle swarm optimization,are introduced to reduce the complexity.As traditional GA-PTS(TGA-PTS)technique risks finding a suboptimal solution,how to avoid this disadvantage of TGA-PTS is an interest topic.In order to obtain a better suboptimal solution,a phase factor optimal pair technique and an abandon/introduction new chromosome technique are proposed in GA here.Simulation results show that the proposed scheme achieves a significant improvement over the TGA-PTS scheme in PAPR.Furthermore,by use of the inherent diversity of constellation for each OFDM candidate,in the receiver part,the proposed scheme enables data recovery without any side information.Simulation results show the efficiency of the proposed scheme.     

Profile based routing in vehicular ad-hoc networks

One of the main problems in the VANET(vehicular ad-hoc network)routing algorithms is how to establish the stable routes.The link duration in these networks is often very short because of the frequent changes in the network topology.Short link duration reduce the network efficiency.Different speeds of the vehicles and choosing different directions by the vehicles in the junctions are the two reasons that lead to link breakage and a reduction in link duration.Several routing protocols have been proposed for VANET in order to improve the link duration,while none of them avoids the link breakages caused by the second reason.In this paper,a new method for routing algorithms is proposed based on the vehicles trips history.Here,each vehicle has a profile containing its movement patterns extracted from its trips history.The next direction which each vehicle may choose at the next junction is predicted using this profile and is sent to other vehicles.Afterward each vehicle selects a node the future direction of which is the same as its predicted direction.Our case study indicates that applying our proposed method to ROMSGP(receive on most stable group-path)routing protocol reduces the links breakages and increases the link duration time.