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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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Turkish Operations Management (JTOM) is the property of Ankara Yildirim Beyazit University Journal of Turkish Operations Management (JTOM) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Turkish Operations Management (JTOM) is the property of Ankara Yildirim Beyazit University Journal of Turkish Operations Management (JTOM) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Autonomous buses offer a promising solution to the first- and last-mile problems, but their initial deployment requires safety drivers to supervise bus operations. Compared to traditional bus driver scheduling, the safety driver scheduling is more flexible and allows safety drivers to start/end their tasks at any bus stop/terminal. However, uncertainties such as delays in public bus services can bring new challenges to the flexible safety driver scheduling. To address these challenges, we put forward a robust safety driver scheduling problem (RSDSP) for autonomous bus services in order to achieve the trade-off between cost reduction and robustness against possible delays. The proposed robust optimization model takes into account the propagation, uncertainty, and correlation of delays. Besides, we develop both exact and heuristic solution methods to solve the RSDSP efficiently. We demonstrate the effectiveness of the proposed model and solution methods through the extensive numerical experiments in which the instances are generated based on the historical operational data of Singapore's bus lines. The results highlight the importance of incorporating robust optimization into the safety driver scheduling for autonomous bus services. The proposed methods can also be applied to other robust driver/crew scheduling problems in public transportation. • Propose a robust safety driver scheduling problem for autonomous buses. • Consider the uncertainty, correlation, and propagation of bus delays. • Develop an efficient branch-price-and-cut algorithm to solve the robust model. • Demonstrate the effectiveness and efficiency of the proposed model and methods. [ABSTRACT FROM AUTHOR]

• We study the integrated bus vehicle and reliable driver scheduling problem. • Driver's reliability is the key concept that improves the operational assignments. • We reduce the number of no-covered to improve the user's satisfaction. • A constraint programming model is compared with a variable neighborhood search. • Enormous gains obtained in covered trips when the drivers' reliability is considered. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights • One of the first studies to investigate fatigue in winter maintenance operations. • Winter maintenance operations are adversely impacted by fatigue. • Winter maintenance equipment should be designed to reduce task-related fatigue. • Fatigue-related policies are needed to encourage frequent breaks or naps. Abstract Similar to commercial motor vehicle drivers, winter maintenance operators are likely to be at an increased risk of becoming fatigued while driving due to long, inconsistent shifts, environmental stressors, and limited opportunities for sleep. Despite this risk, there is little research concerning the prevalence of winter maintenance operator fatigue during winter emergencies. The purpose of this research was to investigate the prevalence, sources, and countermeasures of fatigue in winter maintenance operations. Questionnaires from 1043 winter maintenance operators and 453 managers were received from 29 Clear Road member states. Results confirmed that fatigue was prevalent in winter maintenance operations. Over 70% of the operators and managers believed that fatigue has a moderate to significant impact on winter maintenance operations. Approximately 75% of winter maintenance operators reported to at least sometimes drive while fatigued, and 96% of managers believed their winter maintenance operators drove while fatigued at least some of the time. Furthermore, winter maintenance operators and managers identified fatigue countermeasures and sources of fatigue related to winter maintenance equipment. However, the countermeasures believed to be the most effective at reducing fatigue during winter emergencies (i.e., naps) were underutilized. For example, winter maintenance operators reported to never use naps to eliminate fatigue. These results indicated winter maintenance operations are impacted by operator fatigue. These results support the increased need for research and effective countermeasures targeting winter maintenance operator fatigue. [ABSTRACT FROM AUTHOR]

A constrained self-adaptive harmony search algorithm (CSAHS) with 2-opt swapping (CSAHS-2opt) for driver scheduling problem of university shuttle bus (DSPUSB) was proposed in this study. In generating a schedule, there are some constraints that should be catered to obtain an optimum result. In DSPUSB, fairness of task distribution among drivers is one of the main issues that are very crucial and can affect the performance and quality of services. Therefore, to maximise the fairness, the violation of soft constraints for DSPUSB including shift and route should be minimised. CSAHS with 2-opt swapping was proposed focusing on minimising soft constraint violation. In standard harmony search (HS), the value of distance bandwidth (BW) parameter was static, while in this study the BW was dynamically changed and determined based on the current solution of each driver every week. Here, a set of distance BW value was formed based on shift constraint. In each iteration, the BW values were often changed and randomly chosen within the set, whereas the 2-opt swapping normally used in travelling salesman problem was applied for route constraint based on some rules. These improvements are capable of reducing the repetition task that leads to fairness issue. The result demonstrated that CSAHS-2opt gave better solutions compared with standard HS, improved HS and parameter-adaptive HS. [ABSTRACT FROM AUTHOR]

Highlights • This research seeks to develop a paradigm to minimize overall fleet crash risk by rescheduling. • Several types of high crash risk driver schedules were identified. • Rescheduling based on our algorithm could reduce the incidence of crashes by approximately 30 percent. Abstract Intercity bus crashes often involve driver fatigue, which itself is usually the result of sleep deprivation, long driving hours, a maladjusted circadian rhythm, or some combination of the above. And driver scheduling has long been suspected as the root cause affecting sleepiness and fatigue. As such, a fundamental question for intercity bus carriers is how to reduce crashes associated with driver schedules, while maintaining a nonstop service? This research seeks to develop a paradigm to minimize overall fleet crash risk by rescheduling. In this study, we first identified those driving schedules associated with the highest crash risks, and a rescheduling scheme is then proposed to reduce fleet crashes overall. A case-study approach was employed to identify driver scheduling associated with higher crash risk, and a mathematical program was then formulated to minimize fleet crash risk. Our results showed that several types of driver schedules would lead to higher crash risk; for example: (1) working in the afternoon or early hours in the morning for two consecutive days; and (2) commencing a driving shift in the mornings, the afternoon or the early hours of the morning after being off-duty for more than 24 h. To meet the challenge of maintaining a nonstop service while simultaneously minimizing the crash risk associated with these risk patterns, a mathematical program was developed, and it was found that rescheduling based on our algorithm could reduce the incidence of crashes by approximately 30 percent. [ABSTRACT FROM AUTHOR]

The authors developed and implemented mathematical optimization models that address a particularly complex variation of a problem that occurs in bulk chemicals transport, that is, the problem of assigning a large number of individual resources (transportation assets) to tasks (freight movements). All modes of freight transportation are subject to flow imbalances that affect the efficiency of asset utilization. The use of mathematical programming optimization models has a rich history of application to this problem. We use variations and extensions of the classical assignment problem to find globally optimal or near-optimal solutions to the problem of assigning a large number of individual resources (transportation assets) to tasks (freight movements). We address a particularly difficult variant of this problem that occurs in the bulk transport (fuels and chemicals) division of Schneider National Inc. This group accepts 350 customer orders per day, involving 10,000 distinct commodities, with pickup and delivery locations across the continental United States. The corresponding freight movements are executed using a fleet comprising a 1,000 drivers and 1,600 tanker trailers. Chemical interaction properties of these commodities impose complex product-sequencing constraints, interorder tanker wash and preparation processes, and the selection of specific trailer configurations. Schneider National must consider these complexities in addition to those encountered in more common fleet dispatch problems. To address this problem, the engineering group at Schneider National designed and implemented a multiphase, multidimensional matching algorithm and developed new business processes that enable business planners to leverage optimized solution recommendations. We documented over $4 million in annualized operational and capital cost savings, as well as significantly improved productivity and customer service, which this new system has been delivering since its implementation. [ABSTRACT FROM AUTHOR]

In this paper, a railway crew scheduling problem is presented. The basic requirements related to the drivers' working time, which should be considered in the presented topic, are characterized. The literature review of existing models for crew planning was also performed as well. In the main part of the article, the decision model, which will allow to plan a driver for a train, is defined. The article also contains an assessment of the possibility to use the presented model in the scheduling the work of rail crews in practice. [ABSTRACT FROM AUTHOR]

Due to the characteristics of electric buses, such as mileage limitations, multiple charging modes, and long charging times, traditional vehicle scheduling and driver scheduling strategies cannot meet the requirements for electric bus line scheduling. To address this issue, we propose a collaborative optimization model for establishing vehicle schedules, charging plans and driver schedules for electric bus lines incorporating three charging modes: fast charging, slow charging and battery swapping. The proposed model is solved by a two-stage solution algorithm based on "Generation and Selection". Case studies are carried out on bus lines in Beijing. The results show that the proposed model can reduce the costs of bus companies and improve the utilization rate of buses and drivers. In addition, the results indicate that bus line length has an important impact on the selection of charging mode. [ABSTRACT FROM AUTHOR]

The driver scheduling problem in public transportation is defined in the following way. There is a set of operational tasks, and the goal is to define the sequence of these tasks as shifts in such a way that every task must be assigned to a shift without overlapping. In real-world situations several additional constraints need to be considered, which makes large practical problems challenging to be solved efficiently. In practice it is also an important request with respect to a public transportation scheduling system to offer several versions of quasi-optimal solutions. In this paper we present an efficient driver scheduling solution methodology which is flexible in the above sense. [ABSTRACT FROM AUTHOR]

The hours of service (HOS) regulations of the department of transportation severely restrict the set of feasible driver schedules. So much so that establishing whether a sequence of full truckload transportation requests, each with a dispatch window at the origin, can feasibly be executed by a driver is no longer a matter of simple forward simulation. We consider this problem and prove that the feasibility of a driver schedule can be checked in polynomial time by providing an O(n3) algorithm for establishing whether a sequence of full truckload transportation requests, each with a dispatch window at the origin, can be executed by a driver. [ABSTRACT FROM AUTHOR]

Abstract: We consider the problem of determining the home locations, or domiciles, of truck drivers for a less-than-truckload carrier. Domiciling decisions are complex, in part due to regulations and union rules restricting driver schedules, but have a significant impact on the operating costs of less-than-truckload carriers. We present an iterative scheme, using driver dispatch technology in each iteration, to allocate drivers to terminals and to determine drivers’ bids so as to satisfy union requirements. Computational experiments demonstrate the value of the iterative scheme and quantify the impact of union rules on the number of drivers required (and the resulting operating costs). [Copyright &y& Elsevier]

This paper presents an applied study of scheduling buses and drivers for the Beijing Bus Group (BJBUS), the largest bus company in China. This is pioneering research in China for bus transit scheduling using computers based on a unified mode of operation. It is anticipated that the research fruits and experiences obtained would be of benefit to other bus operators in China. The bus transit scheduling problem in BJBUS is first presented in the paper. The particular characteristics are pointed out, which are different from those in developed countries, while many are common in China. After a brief review and analysis of the appropriateness of some currently successful approaches, the paper focuses on reporting the solution ideas and methods for BJBUS, especially those developed to solve the specific problems of BJBUS, such as scheduling buses with built-in meal periods, multi-type bus scheduling, restricting drivers to one or two particular buses, etc. Finally, the implementation and computational results are reported before the concluding remarks. [ABSTRACT FROM AUTHOR]

Abstract: Transportation service subcontracting is becoming an effective means of business for many leading carriers to maintain their market dominance and profitability. This research is based on an outsourcing practice in one of the few largest limousine fleet companies in the US, whose (mostly advance) demand for services often exceeds its capacity. We develop a model for ride service outsourcing. A column generation method is proposed. The outsourcing decision is made simultaneously with pre-scheduling of in-house ride services as well as staffing. Test with services data indicates that the developed method dramatically improves the carrier’s outsourcing decision. [Copyright &y& Elsevier]

For real life bus and train driver scheduling instances, the number of columns in terms of the set covering/partitioning ILP model could run into billions making the problem very difficult. Column generation approaches have the drawback that the sub-problems for generating the columns would be computationally expensive in such situations. This paper proposes a hybrid solution method, called PowerSolver, of using an iterative heuristic to derive a series of small refined sub-problem instances fed into an existing efficient set covering ILP based solver. In each iteration, the minimum set of relief opportunities that guarantees a solution no worse than the current best is retained. Controlled by a user-defined strategy, a small number of the banned relief opportunities would be reactivated and some soft constraints may be relaxed before the new sub-problem instance is solved. PowerSolver is proving successful by many transport operators who are now routinely using it. Test results from some large scale real-life exercises will be reported. [ABSTRACT FROM AUTHOR]

This paper describes a new hybrid method based on the application of the Population Training Algorithm (PTA) and linear programming (LP) for generation of schedules for drivers in a public transport system. These methods are applied in an iterative way, where PTA is responsible for the generation of good columns (low cost and good covering of the tasks), and LP for solving a set partitioning problem formed by these columns. The PTA employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals cannot be improved by such heuristics. The driver schedules are represented by columns in a large-scale set partitioning problem, which are formed when solving the linear programming relaxation. The computational results are compared against a Simulated Annealing metaheuristic using randomly formed instances based on real problems. [ABSTRACT FROM AUTHOR]

Problem: A homogeneous perception of safety is important for the achievement of a strong safety culture; however, employees may differ in their safety perceptions, depending on their position and/or hierarchical level within the organization. Moreover, there is limited information on the antecedents of safety culture. This study examines how safety training, driver scheduling autonomy, opportunity for safety input, and management commitment to safety influence individuals'' perceptions of safety culture. Method: Data for this study were drawn from 116 trucking firms, stratified by three safety performance levels. The data were collected from drivers (lowest hierarchical level), dispatchers (medium hierarchical level), and safety directors (highest hierarchical level), regarding their perceptions of their respective corporate safety cultures. Perceptions of safety culture were analyzed through a linear regression using dummy variables to differentiate among the three hierarchical groups. The resulting model allowed for examination of the specific antecedents of safety culture for the three employee groups and the extent to which the hierarchical groups were in agreement with each other. Results: Driver fatigue training, driver opportunity for safety input, and top management commitment to safety were perceived to be integral determinants of safety culture in all three groups. Impact on industry: Trucking firms seeking to strengthen employees'' perceptions of safety culture might begin by improving these safety management practices while appreciating that they may have a different impact depending on the employee''s hierarchical position (e.g., drivers'' perceptions of safety culture are more influenced by top management commitment and driver fatigue training). A fourth safety practice examined, driver scheduling autonomy, was not found to be instrumental in shaping safety culture for any of the three hierarchical levels. Consistent with previous research, implementation of stronger safety cultures should result in fewer accidents. [Copyright &y& Elsevier]

Public transport driver scheduling problems are well known to be NP-hard. Although some mathematically based methods are being used in the transport industry, there is room for improvement. A hybrid approach incorporating a genetic algorithm (GA) is presented. The role of the GA is to derive a small selection of good shifts to seed a greedy schedule construction heuristic. A group of shifts called a relief chain is identified and recorded. The relief chain is then inherited by the offspring and used by the GA for schedule construction. The new approach has been tested using real-life data sets, some of which represent very large problem instances. The results are generally better than those compiled by experienced schedulers and are comparable to solutions found by integer linear programming (ILP). In some cases, solutions were obtained when the ILP failed within practical computational limits. [ABSTRACT FROM AUTHOR]

A Mathematical Programming model of a driver scheduling system is described. This consists of set covering and partitioning constraints, possibly user-supplied side constraints, and two pre-emptively ordered objectives. The previous solution strategy addressed the two objectives using separate Primal Simplex optimisations; a new strategy uses a single weighted objective function and a Dual Simplex algorithm initiated by a specially developed heuristic. Computational results are reported. [ABSTRACT FROM AUTHOR]