Driver Scheduling Research Articles

Using state-space shortest-path heuristics to solve the long-haul point-to-point vehicle routing and driver scheduling problem subject to hours-of-service regulatory constraints

Using state-space shortest-path heuristics to solve the long-haul point-to-point vehicle routing and driver scheduling problem subject to hours-of-service regulatory constraints

Optimal Charging Scheduling and Management with Bus‐Driver‐Trip Assignment considering Mealtime Windows for an Electric Bus Line

Compared to a charging scheduling and management problem characterized by predetermined trip assignment, this study takes bus and driver scheduling into account, and mealtime windows must be guaranteed as one of the major labor regulations. A discretized mixed‐integer linear programming (MIPL) model is developed based on a single electric bus route. We aim to obtain fast and high‐quality global solutions for this problem, and the model can be easily executed by bus operators by directly invoking an available optimization solver such as IBM ILOG CPLEX. We test our model on a real round‐trip bus route. Numerical experiments show that CPLEX takes approximately 6 sec to obtain an optimal solution. The model can not only reasonably arrange daily trips for each electric bus and driver but also effectively determine the optimal charging schedule and management for an electric bus line. Besides, we analyze the sensitivity of the key parameters in the model. With the increase in the drivers’ maximum workload, the drivers’ average idle time decreases by approximately 11.25%. The objective value decreases by approximately 38.71% and 40.04% with increases in the battery capacity and fleet size, respectively, and the objective value increases by approximately 30.06% with the decrease in the initial battery driving range. In addition, we compare the effectiveness of our time discretization modeling method in solving the same case study to that from other similar studies, and the validity of our method can be verified by the calculation time. We also compare the computational efficiency of CPLEX in solving the same case study problem with and without implementing valid inequalities, and the computational efficiency of the valid inequality method is greatly improved. Finally, through the testing of a multiline network, the potential application of the model to a large‐scale traffic network is verified.

Tour Scheduling in Attended Home Delivery

In this paper we study a tour-scheduling problem for an attended home delivery problem with uncertain order requests. The problem is modelled as a two-stage stochastic programming problem and solved using the multi-cut L-shaped method. Weekly working patterns are generated by means of a pricing heuristic. Numerical results on randomly generated instances show that including weekly working rules increases the total cost by only a small amount (up to 2.54%) when compared to an approach that only considers daily working rules for the generation of driver schedules.

Truck driver scheduling with interdependent routes and working time constraints

In transportation and logistic applications, scheduling hours of service for multiple, interdependent truck drivers to ensure compliance with regulatory resting requirements and service time windows is an extremely challenging task. Models previously designed to schedule truck drivers with interdependent routes have been capable of capturing such scenarios. However, until now, there has been no efficient way to schedule drivers when facing multiple types of interdependency constraints. To overcome this challenge, we introduce properties of truck driver schedules under working time constraints for single-day journeys. A two-stage algorithm exploits these properties to quickly produce schedules for a large number of truck routes. The algorithm minimizes the latest completion time of multiple truck schedules while ensuring that all drivers can complete their route with sufficient recovery periods and within their working hours. Computational experiments demonstrate the superiority of the approach compared to both existing algorithms and a mathematical programming approach. • Identification of properties of truck driver schedules. • Backtracking algorithm employing the properties. • Multiple types of route interdependencies can be tackled. • The proposed algorithm outperforms all pre-existing methods.

A column generation approach for the driver scheduling problem with staff cars

Given a set of timetabled bus trips, transport companies are faced with the challenge of finding a feasible driver schedule that covers all trips and abides by various labor union regulations. The regulations are primarily concerned with providing sufficient breaks for the drivers during the day. Practical limitations in the city network enforce drivers to travel by cars between bus stops to have breaks. Transport companies have a limited number of cars, known as staff cars, which have to be returned to their respective depots at the end of the day. The simultaneous scheduling of drivers and staff cars for the drivers is known as the driver scheduling problem with staff cars (DSPSC). It is estimated that the DSPSC accounts for 60% of a bus company’s operational expense, and this paper proposes a column generation approach that attempts to minimize operational expense. The column-generation framework iterates between a master problem, a subproblem for generating driver variables and a subproblem for generating staff car variables. The subproblem related to the drivers is formulated as a resource constrained shortest path problem, which is solved by a dynamic programming approach. Several heuristic branching strategies are explored to find integer solutions. The proposed methodology is tested on eight real-life instances from seven Northern European bus companies. A comparison with a state-of-the-art mixed integer programming (MIP) solver and an adaptive large neighborhood search (ALNS) heuristic indicate that the column generation approach provides improved solutions for six instances and the average improvement is 1.45%.

A Dynamic Tree Algorithm for Peer-to-Peer Ridesharing Matching

On-demand peer-to-peer ridesharing services provide flexible mobility options and are expected to alleviate congestion by sharing empty car seats. An efficient matching algorithm is essential to the success of a ridesharing system. The matching problem is related to the well-known dial-a-ride problem, which also tries to find the optimal pickup and delivery sequence for a given set of passengers. In this paper, we propose an efficient dynamic tree algorithm to solve the on-demand peer-to-peer ridesharing matching problem. The dynamic tree algorithm benefits from given ridesharing driver schedules and provides satisfactory runtime performances. In addition, an efficient pre-processing procedure to select candidate passenger requests is proposed, which further improves the algorithm performance. Numerical experiments conducted in a small network show that the dynamic tree algorithm reaches the same objective function values of the exact algorithm, but with shorter runtimes. Furthermore, the proposed method is applied to a larger size problem. Results show that the spatial distribution of ridesharing participants influences the algorithm performance. Sensitivity analysis confirms that the most critical ridesharing matching constraints are the excess travel times. The network analysis suggests that small vehicle capacities do not guarantee overall vehicle-kilometer travel savings.

Comprehensive Analysis Based on Massive Taxi Trajectory Data

With the increasing demand for taxis, a large number of taxi trajectory data have been used for in-depth analysis. However, most early studies only used limited data or simulations to analyze the route choice behavior or traffic congestion problems. To extract more useful information from taxi trajectory data, this paper analyzes massive raw dates collected in Chengdu from 4 different perspectives. To begin with, passenger demand is analyzed by counting the number of orders in each period. K-means clustering is used for regional planning and provides appropriate boarding points for passengers. Furthermore, this paper focus on quantity duration of order duration, which presents an approximate Poisson distribution. Finally, the ratio of driving distance to the linear distance between the start and end points is defined as the overtravel ratio. Through the comprehensive analysis of the taxi trajectory data, it can provide assistance to the travel demand of passengers and the scheduling of taxi drivers, and maximize resource efficiency. Moreover, the data collected in Chengdu are very representative and large, so the corresponding suggestions can also provide hints for public transport problems.

On-Demand Public Transit: A Markovian Continuous Approximation Model

With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.

An Experimental Data of Lithium-Ion Battery Time Series Analysis

The experimental data of Lithium-ion battery has its specific sense. This paper is proposed to analyze and forecast it by using autoregressive integrated moving average (ARIMA) and spectral analysis, which has effective and statistical results. The method includes the identification of the data, estimation and diagnostic checking, and forecasting the future values by Box and Jenkins. The analysis shows that the time series models are related with the present value of a series to past values and past prediction errors. After transferring the data by different function, improving autocorrelations are significant. Forecasting the future values of the possible observations show significantly fluctuated such as increasing or decreasing in specific ranges accordingly. In spectral analysis, the parameters of the model were determined by performing spectral analysis of the experimental data to look periodicities or cyclical patterns, and to check the existence of white noise in the data. The Bartlett's Kolmogorov-Smirnov statistic suggests the white noise of the data. The spectral analysis for the series reveals non-11-second cycle of activity for dynamic stress test current, but strong 45-second that highlights the position of the main peak in the spectral density; strong 21-second and 45-second for the urbane dynamometer driver schedule current and voltage, respectively; but no significance for dynamic stress test current.

Scheduling truck drivers with interdependent routes under European Union regulations

When transporting goods, truck drivers may have to perform load transfers between two vehicles at cross-dock or transfer locations. These transfers create interdependencies between the routes of different truck drivers and require careful temporal synchronization. The situation becomes even more complicated when driver schedules must comply with customer time windows, working periods and regulatory resting requirements. Although the scheduling of hours of service for truck drivers is a well-known problem, existing algorithms cannot cope with interdependent routes. We introduce the Truck Driver Scheduling Problem with Interdependent Routes to successfully accommodate this real-world characteristic. This paper proposes a mathematical model and a label propagation algorithm to solve this new problem under the regulations of the European Union. Experiments indicate that the algorithm can quickly schedule a large number of interdependent routes and outperforms a mathematical programming approach. The algorithm produces valid schedules for multiple drivers and thus becomes a foundation for future vehicle routing algorithms that address interdependent routes and hours of service regulations.

Branch and Price for Bus Driver Scheduling with Complex Break Constraints

This paper presents a Branch and Price approach for a real-life Bus Driver Scheduling problem with a complex set of break constraints. The column generation uses a set partitioning model as master problem and a resource constrained shortest path problem as subproblem. Due to the complex constraints, the branch and price algorithm adopts several novel ideas to improve the column generation in the presence of a high-dimensional subproblem, including exponential arc throttling and a dedicated two-stage dominance algorithm. Evaluation on a publicly available set of benchmark instances shows that the approach provides the first provably optimal solutions for small instances, improving best-known solutions or proving them optimal for 48 out of 50 instances, and yielding an optimality gap of less than 1% for more than half the instances.

Sistem Informasi Penjadwalan Supir Bus Po Logos Berbasis Website

The problem with the PO Logos company is that the schedule for the driver or bus driver is still done manually which results in the old operator determining the bus driver who will depart. And the distribution of the driver's departure schedule is not evenly distributed so that the bus driver who has just departed is scheduled to leave again the next day which causes the driver to contact the operator to report this in order to get a replacement driver, because the rules for drivers at PO Logos company are once every three days. From these problems raised the idea to create a driver scheduling information system for PO Logos. This was built using the Waterfall methodology which consists of 5 stages, namely Requirement Analysis and Definition of the old system analysis and the new system of scheduling using genetic algorithms which are described by flowcharts, System and Software Design using Unifed Modeling Language (UML), Implementation and Unit Testing using programming languages PHP and MySQL, Integration and System Testing Testing is done by black-box testing. The results of making a bus driver scheduling information system in making a driver schedule using a genetic algorithm through stages, namely the formation of the initial population, evaluating the fitness value, finding the two best fitness values ??using tournament selection, crossover using one-point crossover, mutation and fitness evaluation again for looking for the best value.

A network flow-based algorithm for bus driver rerostering

Bus driver rostering generates the work plan for a pool of drivers during a planning period of predefined length. This plan, called the roster, must consider the balance between the pressure of costs, the provision of a service of high quality, labour agreements, and the goodwill of the workers. The bus driver rerostering problem occurs during real-time operational planning, when unexpected events—such as non-planned absences of drivers—disrupt the roster. To reconstruct a roster which is originally built in a context of days off schedules for drivers, we propose a reactive methodology based on a multicommodity flow assignment mixed integer linear programming model. The objective is to minimise the number of depot drivers who are assigned to drive and the number of postponed days off, as well as the dissimilarity between the reconstructed and the original roster and the balancing of the workload. The proposed algorithm enables the disrupted roster to be reconstructed at the expense of a relatively small number of changes in drivers’ work and rest periods, while, at the same time, controlling the dimension of the multicommodity flow network. Computational experience based on real-life based instances revealed that the algorithm has the ability to produce reconstructed rosters with few changes to the drivers’ original work assignment, in a short CPU time.

Vehicle and reliable driver scheduling for public bus transportation systems

We propose the bus vehicle and reliable driver scheduling problem that is an integrated approach for the vehicle and the crew scheduling problems considering driver’s reliability information to reduce the number of no-covered trips along the day and thus improve the user’s satisfaction. An exact constraint programming model is proposed and compared with a variable neighborhood search that incorporates the driver’s reliability and the trip’s importance. The obtained trip-vehicle-driver assignments are evaluated on many scenarios with a Monte Carlo method to simulate the driver’s absenteeism. Experimental results on randomly generated instances based on a real case study show our methodologies’ efficiency and the enormous gains in covered trips when the drivers’ reliability is considered.

The long-haul full-load vehicle routing and truck driver scheduling problem with intermediate stops: An economic impact evaluation of Brazilian policy

Abstract This paper presents a methodology to identify the economic impact of different policies for heavy vehicle routing and driver scheduling in long-haul full-load trips with intermediate stops for refueling and for regulation-driven meal and rest periods. This methodology can be used both as a planning tool to help policy-makers define policies through scenario simulations, and as an evaluation tool to examine ex-post economic impact once policies have been changed as illustrated herein for the Brazilian case. The paper introduces a mathematical formulation, proposes a state-space graph search approach, and develops an algorithmic solution for a variant of the vehicle routing and truck driver scheduling problem with intermediate stops (VRTDSPIS), which is especially important in countries with vast territory where long-range freight trucking trips are common. In addition, it is particularly relevant for the economies of emerging markets that may have evolving regulatory frameworks and suboptimal infrastructure environments. Regulatory maximum driving and minimum driver resting time windows impose significant constraints to trucker routines, especially in sparsely populated regions through which long range vehicle trips with full load are common. Such trips can take days to be concluded and numerous alternative routes are possible, each with very different alternatives for refueling and rest. While the literature to date has focused mostly on truck driver scheduling given pre-determined routes and individual country’s laws and regulations, this paper proposes a joint solution for the routing and scheduling problem with time window constraints, determining simultaneously a routing and scheduling plan that meets all maximum driving and minimum resting time interval regulatory requirements at minimum cost. An illustration applying the methodology to evaluate the economic impact of truck driver regulatory changes in Brazil is presented.

Modeling and Simulation of Replay Attack Detection using V2X Message in Autonomous Vehicles in WSN based IoT Environment

An autonomous vehicle is a car that drives itself to its destination without driver intervention. Autonomous driving provides driver convenience and prevents accidents caused by driver carelessness. Autonomous vehicles recognize external environments using sensors such as cameras and riders. In addition, autonomous vehicles collect information by using vehicle-to-everything communication in places they do not recognize. During vehicle-to-everything communication, vehicle-to-infrastructure communication communicates with the infrastructure installed on the road and receives information. In other words, the autonomous vehicle receives information from the infrastructure located in an unrecognized place and grasps the road conditions. However, because infrastructure is expensive to install and maintain, technology that uses wireless sensor networks instead of infrastructure has been proposed. Since the sensor node used in the wireless sensor network is placed outside and communicates wirelessly, it is easily compromised from an attacker. Attackers can use a compromised node to attempt various attacks that affect the system, such as replay attacks. These attacks can also have a fatal effect on autonomous vehicles that use information from sensor nodes. The attacker constantly transmits false information to autonomous vehicles, causing a disruption in the driver's schedule. In addition, autonomous vehicles may cause traffic accidents due to path planning using incorrect information. The proposed scheme in this paper uses an autonomous vehicle to defend against replay attacks and detects compromised nodes. The sensor node sends a message to the base station and the autonomous vehicle to notify them when an event occurs. Thereafter, the message is transmitted to the traffic management center and the base station to be mutually verified. This paper shows that by modeling and simulating EF-ITS, it is possible to defend against replay attacks with a probability of 90% and detect compromised nodes.

Railway crew scheduling with semi-flexible timetables

We investigate the impact of coordinating the timetable and the crew schedule in an operational freight railway system. Usually, those problems are solved sequentially—resulting in suboptimal schedules for train drivers due to large idle times between two train rides. We seek to coordinate the timetable and the crew schedule on the operational level by adding flexibility to the timetable. We introduce small time windows that allow to shift entire trains forwards and backwards by discrete time periods. We present a mathematical model and solve it with a column generation heuristic. We test our model on three real datasets of a major European Freight Railway Operator and show that significant reduction in idle time and cost can be achieved.

Solving Large Real-Life Bus Driver Scheduling Problems with Complex Break Constraints

When scheduling drivers for public transport, in addition to covering the demand and dealing with the spatial dimension, a range of legal requirements, collective agreements and company policies need to be respected. The level of concentration required while driving leads to strict rules for break assignments. This results in a complex problem where creating cost-efficient and employee-friendly schedules is challenging. This paper deals with bus driver scheduling using the rules of the Austrian collective agreement for private omnibus providers. The contributions are the formalization of the complex Austrian rules for bus drivers, a new set of publicly available instances based on the characteristics of real-life instances, and a metaheuristic solution approach for the problem. The algorithm was able to significantly improve the solutions of real-life instances and is evaluated on the generated instances. Further we provide insight in the necessity of objectives for employee satisfaction and their effects. Our method can even be successfully applied to improve results on a problem with very different constraints from Brasil.

Gap Analysis of the Workshop of Transportation Vertical

This paper aims to identify the gaps in cold chain logistic service providers. Data has been collected using on field observations. The proposed methodology has been applied to the case study of a transport and logistics company ABC PVT LTD in order to find an optimal solution for five major processes. Different scenarios have been examined with real data provided by the company. The purpose of this research is to conduct gap analysis for cold supply chain processes namely (i) Fuel consumption, (ii) Maintenance of Trucks, (iii) Trip settlement, (iv) Driver Scheduling and (v) Vehicle Tracking. The results of this study show standard operating procedures or corrections in existing process to be adopted at each phase in these processes to make them more efficient and effective one. The results demonstrated the feasible recommendations to avoid gaps in major five processes with respect to transport operations.

A Branch-and-Price-and-Cut Algorithm for the Integrated Scheduling and Rostering Problem of Bus Drivers

In the transportation industry, crew management is typically decomposed into two phases: crew scheduling and crew rostering. Due to the complexity of scheduling and rostering, bus transportation is not an exception and many relevant studies do not consider both procedures simultaneously. However, such a decomposition can yield inferior schedules/rosters. To address this issue, this paper proposes an integrated scheduling and rostering model for bus drivers and devises a branch-and-price-and-cut (BPC) algorithm to solve the complex problem. The proposed solution framework is empirically applied to real-world instances with various problem sizes whose data is collected from H Bus Company located in southern Taiwan. To validate the effectiveness and evaluate the efficiency of the proposed solution framework, this paper compares the solution obtained from the BPC algorithm with that of a benchmark optimization package. The results show that the proposed BPC algorithm can solve problems with large real-world instances within a reasonable computational time. Moreover, in the numerical experiments, this paper finds that the scheduling and rostering results of the bus drivers are more sensitive to the rostering constraints. Also, the proposed integrated framework can yield a better solution than the solution from a conventional two-phase approach, which demonstrates the advantage of the integration in this paper. The proposed method provided can be employed to deal with the challenges in driver planning for bus companies.