Driver Scheduling Research Articles

Dynamic evolution of high-speed railway driver fatigue across scheduling shifts – a simulator study in China

ABSTRACT Scheduling significantly contributes to fatigue among high-speed rail (HSR) drivers, impacting both driving performance and safety. This study aims to analyse distinctiveness and evolutionary patterns of driver fatigue across different scheduling shifts, leveraging an HSR simulator and wearable devices. Through an extensive statistical examination, differences in most heart rate variability features were identified across shifts. The evolution of self-reported fatigue and physiological fatigue was investigated using a Markov chain and a hidden Markov model (HMM) that is extended using softmax regression, respectively. Results indicated a similarity in the initial fatigue state distribution and evolution trends across shifts, albeit with varying degrees of fluctuation. Morning shifts exhibited a higher likelihood of fatigue accumulation. The study revealed that even adequate pre-rest did not entirely counterbalance the impact of circadian rhythms. These findings may contribute to comprehending the temporal dynamics of driver fatigue evolution and offer insights for optimizing driver scheduling practices.

A shift scheduling model for ridepooling services

AbstractThe planning of efficient shift schedules is a key challenge for many service companies whose economic success heavily relies on the efficient employment of personnel. In spite of the recent advances in autonomous driving, mobility services, such as ride pooling, still heavily rely on the use of human drivers and will presumably remain in this category in the near to midterm. As a consequence, shift scheduling of drivers is one of the key success factors in the current industry environment. Determining appropriate shifts that minimize an under- and oversupply of vehicles for all planning periods is a challenging task, since demand can vary heavily over time and the assignment flexibilities are limited due to driver preferences and regulations. In this work, we present a shift scheduling model for ridepooling services. Moreover, we introduce a data generator for instances with realistic properties of a ridepooling service. Using it, we study the effect of different kinds of flexibilities on solution quality.

Investigating Large Neighbourhood Search for Bus Driver Scheduling

The Bus Driver Scheduling Problem (BDSP) is a combinatorial optimisation problem with high practical relevance. The aim is to assign bus drivers to predetermined routes while minimising a specified objective function that considers operating costs as well as employee satisfaction. Since we must satisfy several rules from a collective agreement and European regulations, the BDSP is highly constrained. Hence, using exact methods to solve large real-life-based instances is computationally too expensive, while heuristic methods still have a considerable gap to the optimum. Our paper presents a Large Neighbourhood Search (LNS) approach to solve the BDSP. We propose several novel destroy operators and an approach using column generation to repair the sub-problem. We analyse the impact of the destroy and repair operators and investigate various possibilities to select them, including adaptivity. The proposed approach improves all the upper bounds for larger instances that exact methods cannot solve, as well as for some mid-sized instances, and outperforms existing heuristic approaches for this problem on all benchmark instances.

On the fair scheduling of truck drivers in delivery companies: balancing fairness and profit

AbstractFairness is crucial in transportation systems to ensure that all drivers are treated equally and have the same opportunities. Fair payment policies, equal access to work opportunities, and fair scheduling are some of the policies delivery companies implement to ensure fairness between drivers. In this paper, we study a fair scheduling mixed-integer programming problem where we consider a bi-objective function that aims to maximize profit and improve fairness between drivers by minimizing the maximum deviation from the average driving time. To solve this problem, we employ the weighted comprehensive criterion method and propose an iterative population-based heuristic. The results show that the relative gap between the heuristic and exact approach is acceptable. We also report the fairness price which is the relative difference between total profit with and without incorporating fairness. We find out that improving fairness between drivers does not always lead to a significant reduction in total profit. When the reduction in total profit is important, we recommend formulating the scheduling problem differently where instead of minimizing the maximum deviation from the average driving time, drivers are rewarded when their driving times are longer than the average. We explore incorporating the cost of rewarding these drivers in the objective function.

A label-setting algorithm for the truck driver scheduling problem in accordance with European Community social legislation

In this paper, a label-setting algorithm is proposed in order to efficiently solve the weekly Truck Driver Scheduling Problem (TDSP), which can be seen as a single-machine problem that minimizes the completion time of a fixed sequence of jobs with release and due times and break periods. A focus is made on the night working rule that has only partially been considered in the literature. In the wide literature dedicated to Vehicle Routing Problems, some published heuristics and exact algorithms include constraints related to break/rest periods. But the night working rule defined by the European Community Social Legislation is only considered in some simplified and sub-optimal versions. This constraint is of interest, since it has a direct impact on the practical applicability of routes. Moreover, from a scheduling point of view, the night working rule enforces minimum and maximum time lag constraints, which makes challenging the development of an efficient solution algorithm. The method is benchmarked with a mixed integer linear programming model. Computational experiments show the efficiency of the proposed methods and the effect of the night working constraint on the feasibility of the schedules.

Efficient algorithms for driver scheduling with interdependent routes

Efficient algorithms for driver scheduling with interdependent routes

An intelligent system for constructing metro train driver working schedules

Aim. The paper aims to tests and analyse the results of application of an intelligent system for constructing metro train driver working schedules that is intended for automatically improving the efficiency of utilisation of the working time of metro train drivers. In this case, depending on the task at hand and the chosen criterion, efficiency is understood as a reduction of the number of drivers involved in the implementation of the specified standard train schedule, improved uniformity of alternation of driver work and rest periods, as well as reduction of the duration of down time within work shifts.Methods. The study presented in the paper uses the graph theory, recursive and heuristic algorithms.Results. The authors have developed recursive algorithms for preparing work schedules for main metro train drivers and substitute drivers that operate during main drivers’ lunch breaks. The developed algorithms are used in the intelligent system for metro train driver work schedule planning. The algorithm for generating main driver work schedule includes driver allocation to a number of work lines preliminarily defined based on the rolling stock operation schedule for the purpose of ensuring traffic according to the planned metro train schedule. The algorithm for generating substitute driver work schedule involves substitute driver work time planning based on the possibility of arranging lunch breaks for main drivers.Conclusion. The paper presents the results of a trial of the developed intelligent system for train driver work planning for the Moscow Metro using the example of the Zamoskvoretskoye Depot of the Zamoskvoretskaya Line, the Vykhino Depot of the Tagansko-Krasnopresnenskaya Line, as well as the results of the system’s adaptation to the Moscow Central Circle. It also presents a comparative analysis of driver work schedules, i.e., the actual one and one obtained using the developed intelligent system. The application of the developed system may enable as much as a 28% improvement of the efficiency of metro train drivers’ work hour use.

A matheuristic for tactical locomotive and driver scheduling for the Swiss national railway company SBB Cargo AG

AbstractAt the scale of Switzerland, the national railway company SBB Cargo AG has to schedule its locomotives and drivers in order to be able to pull all trains. Two objective functions are considered in a two-stage lexicographic fashion: (1) the locomotive and driver costs and (2) the driver time that is spent without driving. As the problem instances tend to reach really big sizes (up to 1900 trains), we propose to schedule locomotives and drivers in a sequential way, thus having a sequence of smaller problems to solve. Moreover, for smaller instances, we also propose to schedule jointly locomotives and drivers in an integrated way, therefore increasing the search space but possibly leading to better solutions. In this paper, we present a mathematical formulation and model for the problem. We also consider the contract-related constraints of the drivers, and we propose a way to integrate some time flexibility in the schedules. Next, we propose an innovative matheuristic to solve the problem, relying on a descent local search and a rolling horizon decomposition. An important goal of this method is to explore thoroughly at which extent a general-purpose solver can be used on this problem. Finally, the benefits of each aspect of the model and of the method are analyzed in detail on the results obtained for 20 real SBB Cargo AG instances.

Effects on operating costs of adjusting bus departure times during peak-hour traffic in Sweden

The cost of public transport has increased more than the supply in recent years in Sweden. One of the main cost drivers identified is peak-hour traffic. The major operating cost factors are the need for a large bus fleet for short periods during mornings and afternoons and the scheduling of drivers for shorter periods than the minimum working hour restriction. The objective of this paper is to study the effect of the number of buses needed (and hence the operating cost) during peak hours when adjusting the bus departure times. The study also analyses the increase in public transport supply and the number of boarding passengers if the cost reduction is re-invested. The analysis is based on case studies and simulated scenarios of possible adjustments in departure times for buses. The results show that by marginally adjusting the departure times, fewer buses are needed which leads to decreased operating costs. Further, the results show that the reduction in costs can be used to improve public transport in the area by expanding the supply of public transport in the long run.

Dynamic Weight Setting for Personnel Scheduling with Many Objectives

When large sets of constraints and objectives are combined in a practical optimization problem, managing all these potentially conflicting goals can become very difficult and might require to solve an instance multiple times. First, an instance might be infeasible with the current constraints, in which case our system introduces a novel violation score to help identify the constraints that need to be relaxed for the next run. Second, multiple objectives are often combined using a linear combination with hand-crafted weights, which are very difficult to set such that the result matches the expectations regarding the balance between individual objectives. Instead, the user can tell our system particular thresholds for the expected changes in objectives, e.g., to reduce objective 1 by 10 % while not increasing objective 2 by more than 5 %. Dynamic weight setting automatically adapts the weights to reach these thresholds or uses the violation scores to explain reasons for not reaching thresholds. It can not only be used for soft constraints, but also to determine weights when hard constraints are internally represented as soft constraints in meta-heuristics. While the methodology is general, we have implemented it in the context of a personnel scheduling framework of our industry partner and present a detailed evaluation on the domain of Bus Driver Scheduling, where its benefits can be seen in multiple scenarios.

A construction-and-repair based method for vehicle scheduling of bus line with branch lines

Bus vehicle scheduling is vital to reduce operational cost and ensure service quality for bus companies. Bus Vehicle Scheduling Problem of a bus line with Branch Lines (VSP-BL) is common in practice, but there is no study on this problem. In this paper, we propose a Construction-and-Repair based Method (C&RM) to solve the problem. In addition, existing studies seldom consider bus driver scheduling and rostering factors in vehicle scheduling problem, thus that the solution to the problem is hard for driver scheduling and rostering. To address this dilemma, we take driver scheduling and rostering factors into account in the solution method. C&RM includes two stages: The first stage uses a trip assignment strategy to assign timetabled trips to vehicles to construct a solution. Vehicle scheduling and some driver scheduling constraints are considered in this stage. If the constructive solution dose not satisfy all driver scheduling constraints, the second stage adopts a repair method to improve the solution to satisfy all the vehicle scheduling and driver scheduling constraints. Experimental results show that C&RM outperforms the comparative methods on the number of vehicles and in balancing vehicles’ driving tasks; moreover, the solution found by C&RM satisfies all the constraints of driver scheduling and rostering, which facilitates driver scheduling and rostering.

A Multi-Agent System for Operating Hyperconnected Freight Transportation in the Physical Internet

In the emerging Physical Internet, hyperconnected freight transportation allows using multiple short-haul drivers through hyperconnected meshed hub networks to deliver long-haul freight in multiparty multimodal relay mode. One of its core challenges lies in dynamic operational planning associated with the flows of freight, containers, carriers, and drivers to ensure agile, efficient, resilient, and sustainable performances given demand knowledge and state monitoring. In this paper, we focus on large-scale hyperconnected freight transportation, with testbed as the road-based tractor-hauler delivery of vehicles in the Southeastern USA. We propose an autonomous operating system for dynamic operational decision making from sources through hubs to destinations, including freight shipment consolidation, assignment of freight to haulers and haulers to tractors, multi-hop routing, coordination and scheduling of drivers, tractors and haulers, as well as driver-task matching based on individual prefrences. The system is based on a multi-agent network architecture, where each agent is responsible for different key operational decisions by running heuristic optimization algorithms and interacts with one another through information exchange. We provide preliminary results from simulation-based experiments encompassing vehicle delivery of a major automotive manufacturer from multiple sources, including factories, ports, and railheads, to 500+ dealers across the Southeastern United States. The analysis of results proves that each of agents can effectively and efficiently cope with the large-scale decision making and the overall proposed system is capable of operating hyperconnected freight transportation with reliable order-to-delivery performance, high hauler filling rates, and desirable environemnt friendliness, while improving quality of life by enabling truckers to be back home daily and alleviate truck driver shortage issues.

Development of a Personnel Management and Position and Energy Tracking System for Electric Vehicles

The challenges faced by managers of transportation systems in developing nations such as Nigeria are numerous. These include driver scheduling, which, in many cases, is still being done manually; diversion of vehicles to unauthorized routes by drivers for selfish reasons results in illegal use of fuel meant for official duties, causing the organization lose considerable revenue. In its drive to reduce its carbon footprint, Elizade University, Nigeria, is embarking on the development and use of electric vehicles (EV) as a means of transportation within the university campus. This research is geared towards supporting this initiative by developing an EV tracking system that combines tracking of EV drivers with vehicle position and battery power (energy) tracking in order to mitigate the challenges outlined above. Personnel tracking was achieved using an RFID-enabled staff identity card that authenticates authorized drivers before activating the vehicle ignition system, position tracking was achieved using a geographical positioning system (GPS), and current and voltage sensors were used for tracking of electric vehicle power. Tests revealed that the EV system administrator operated through a personal computer was able to track the EV driver, position and power through a web interface/Google Maps and e-mail in real time. Whereas previous studies either considered tracking of vehicle position or power without personnel, others tracked personnel with less emphasis on the vehicle position or energy. In this study, we combined different technologies such as RFID, GPS and power sensors to consider EV administration in a holistic manner, thereby providing intervention in an infrastructurally deficient setting.

A Time-Space Network-Based Optimization Method for Scheduling Depot Drivers

The driver scheduling problem at Chinese electric multiple-unit train depots becomes more and more difficult in practice and is studied in very little research. This paper focuses on defining, modeling, and solving the depot driver scheduling problem which can determine driver size and driver schedule simultaneously. To solve this problem, we first construct a time-space network based on which we formulate the problem as a minimum-cost multi-commodity network flow problem. We then develop a Lagrangian relaxation heuristic to solve this network flow problem, where the upper bound heuristic is a two-phase method consisting of a greedy heuristic and a local search method. We conduct a computational study to test the effectiveness of our Lagrangian relaxation heuristic. The computational results also report the significance of the ratio of driver size to task size in the depot.

The problem of assigning bus drivers to trips in a Spanish public transport company

This article addresses a particular bus Crew Scheduling Problem (CSP) that arises in a public transport company in Spain. The classical CSP problem tries to create bus driver schedules covering all the bus schedules published by the company. The problem posed does not require satisfying any restriction due to the lack of drivers. This relaxed restriction emerged in the time of Covid-19 for different reasons. This is why the need arose in the company for a tool that allows optimizing the daily work of the drivers. The objective function of the problem requires servicing the greatest number of passengers possible, instead of minimizing the schedule cost. A model and strategies for solving the problem exactly are introduced: clustering, re-optimization, etc. Additionally, a re-optimization model is proposed using prior feasible solutions to speed up the resolution of the problem. The integration and practical use of the solutions obtained and their corresponding monitoring in the decision-making process of the company are described.

Robust and Cost-Efficient Integrated Multiple Depot Vehicle and Crew Scheduling with Controlled Trip Shifting

For vehicle and crew scheduling in public bus transport, two concepts are intensively studied to reduce resource usage and minimize operational costs: Integrated, that is, simultaneous scheduling of vehicles and drivers, and resource scheduling with simultaneous trip shifting, that is, slightly modifying departure and arrival times of particular trips without changing their duration. Applying these two concepts can lead to highly cost-efficient and tight schedules. Unfortunately, such schedules are more susceptible to delays during operations. Furthermore, shifting of particular trips may have an unwanted impact on the original timetable structure as, for example, headways between consecutive trips of a line might be changed. In this work, we develop an integrated vehicle and crew scheduling approach with the possibility to shift scheduled trips controllably within small defined time windows. It is a Lagrangian heuristic based on column generation. In addition to minimizing planned costs, the approach aims at two further objectives: building robust schedules by minimizing the expected overall propagation of delays through the transport network and minimizing timetable alteration. To reduce the possible propagation of delays, the mutual dependencies between vehicle and crew schedules are taken into account during integrated scheduling. To increase both robustness and cost-efficiency, the possibility to shift trips within small defined time windows is used. Robust and cost-efficient vehicle itineraries and crew duties are generated in the column-generation pricing problems. Computational experiments on data sets from the real world show that resource usage can be further reduced compared with pure cost-efficient integrated scheduling, whereas resource schedules remain robust and timetable alterations can be minimized. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1154 .

Hyper-Heuristics for Personnel Scheduling Domains

In real-life applications problems can frequently change or require small adaptations. Manually creating and tuning algorithms for different problem domains or different versions of a problem can be cumbersome and time-consuming. In this paper we consider several important problems with high practical relevance, which are Bus Driver Scheduling, Rotating Workforce Scheduling, and Minimum Shift Design. Instead of designing very specific solution methods, we propose to use the more general approach based on hyper-heuristics which take a set of simpler low-level heuristics and combine them to automatically create a fitting heuristic for the problem at hand. This paper presents a major study on applying hyper-heuristics to these domains, which contributes in three different ways: First, it defines new low-level heuristics for these scheduling domains, allowing to apply hyper-heuristics to them for the first time. Second, it provides a comparison of several state-of-the-art hyper-heuristics on those domains. Third, new best solutions for several instances of the different problem domains are found. These results show that hyper-heuristics are able to perform well even on very complex practical problem domains in the area of scheduling and, while being more general and requiring less problem-specific adaptation, can in several cases compete with specialized algorithms for the specific problems. These results help to improve industrial systems in use for solving different scheduling scenarios by allowing faster and easier adaptation to new problem variants.

Electrifying New York City Ride-Hailing fleets: An examination of the need for public fast charging

SummaryThis report assesses the scale of public fast charging needed to electrify approximately 20,000 vehicles across the yellow cab and for-hire segments in New York City. The analysis considers real-world trip data in conjunction with driver home locations, overnight charging access rates, driver schedules, and more. Outcomes indicate that the existing charging network in New York City is not adequate even in the most optimistic scenario; 1,054 150-kW ports are required when 15% of drivers have access to overnight charging, whereas 367 150-kW ports are needed when 100% of drivers have access. Results also indicate that although charging is demanded in areas nearby high trip demand, fast charging ports are also demanded in areas near driver residences as a supplement for home charging in scenarios with limited overnight charging access. These findings motivate investment into both overnight charging and public fast charging to meet the charging demands of ride-hailing fleets.

A survey of train driver schedules, sleep, wellbeing, and driving performance in Australia and New Zealand

Train drivers work long hours on 24 h schedules and many factors impact their fatigue risk at work, creating a clear imperative for good rostering practice. Adopting a systems approach, this study investigated the relationship between multiple interrelated factors (train drivers’ schedule, sleep, wellbeing, and fatigue) and the perceived influence of these factors on train driving performance and safety using an online survey distributed in Australia and New Zealand. In addition to demographics and work schedule, passenger and freight train drivers (n = 751) answered questions about: (1) Sleep duration; (2) wellbeing, including physical and mental health, the extent to which shiftwork causes sleep, social, domestic, and work problems, and (3) the extent to which ten fatigue, health and wellbeing factors in the work and home environment negatively impact their driving performance. The key factor that emerged from analyses, with the largest and most consistent negative effects (and controlling for other factors) was schedule irregularity. Approved rosters were ranked as having the most important impact on day-to-day driving performance, followed by physical and mental health, and outside work factors. Results also suggested that schedule irregularity may amplify the negative impacts of the roster, impaired physical and mental health, and outside work factors on driving performance. As shift variability and schedule irregularity are often poorly represented in existing industry guidance, these results provide evidence for increased reflection on current fatigue management guidelines for train drivers and suggest a need for greater focus on schedule irregularity through the lens of a systems approach.

A GENERAL FRAMEWORK FOR EVALUATING DRIVER SCHEDULES IN PUBLIC TRANSPORT

A unified approach is presented as the principle for a potential framework for evaluating driver schedules in public transport. There are two reasons for the need for such a framework. On the one hand, constructing optimal driver schedules is a challenging problem, and practical solutions cannot be compared to the optimum, which brings about the need to analyse the results with respect to an appropriate estimated value. On the other hand, the specific constraints and rules in different countries and for different companies make it difficult to model solutions in a unified way. Our new approach provides a solution to the above problems by employing an efficient general methodology from both the data modelling and process modelling point of view. By introducing the concept of reducible working time, our approach gives a realistic evaluation framework with an efficient solution method. The applicability of our approach is demonstrated through real-world cases.