A new approach to crew scheduling in rapid transit networks

We propose a new approach for the crew scheduling problem in rapid transit networks. With this approach we try to open a new branch for future research, providing a different way of facing the crew scheduling problem which makes integration with other planning problems easier than the traditional approach based on column generation for solving a set covering/partitioning problem. For solving this new model we develop a Lagrangian relaxation and we take advantage of an ad hoc decomposition based on time personnel clustering. We present some preliminary computational experiments for real case studies drawn from the main Spanish train operator, RENFE

Solving Practical Railway Crew Scheduling Problems with Attendance Rates

Arising from a practical problem in German rail passenger transport, a prototype for a multi-period railway crew scheduling problem with attendance rates for conductors is developed and evaluated in this paper. The consideration of attendance rates is of increasing importance in regional transport networks and requires decision support. For this purpose business analytics is applied in order to offer an approach to transform real-world data to concrete operational decision support (action). The focus here is on the analysis step using a new set covering model with several essential restrictions integrated for the first time. A hybrid column generation approach is applied, which solves the pricing problem by means of a genetic algorithm. The artifact is evaluated with the help of a case study of three real-world transport networks. It is shown that the hybrid solution approach is able to solve the problem more effectively and efficiently compared to conventional approaches used in practice

Railway crew capacity planning problem with connectivity of schedules

We study a tactical level crew capacity planning problem in railways which determines the minimum required crew size in a region while both feasibility and connectivity of schedules are maintained. We present alternative mathematical formulations which depend on network representations of the problem. A path-based formulation in the form of a set-covering problem along with a column-and-row generation algorithm is proposed. An arc-based formulation of the problem is solved with a commercial linear programming solver. The computational study illustrates the effect of schedule connectivity on crew capacity decisions and shows that arc-based formulation is a viable approach

Crew Scheduling Considering both Crew Duty Time Difference and Cost on Urban Rail System

Urban rail crew scheduling problem is to allocate train services to crews based on a given train timetable while satisfying all the operational and contractual requirements. In this paper, we present a new mathematical programming model with the aim of minimizing both the related costs of crew duty and the variance of duty time spreads. In addition to iincorporating the commonly encountered crew scheduling constraints, it also takes into consideration the constraint of arranging crews having a meal in the specific meal period of one day rather than after a minimum continual service time. The proposed model is solved by an ant colony algorithm which is built based on the construction of ant travel network and the design of ant travel path choosing strategy. The performances of the model and the algorithm are evaluated by conducting case study on Changsha urban rail. The results indicate that the proposed method can obtain a satisfactory crew schedule for urban rails with a relatively small computational time

Generalized Algorithms for Crew Planning: Survey and Future Directions for Railways

This paper studies the crew planning problem as observed in the transportation industry. We first survey the existing literature on crew scheduling applications in railways and airlines. Next, we identify the synergies in the two domains and propose new directions for railway crew scheduling inspired from the applications in airlines

A Lagrangian Relaxation Approach Based on a Time-Space-State Network for Railway Crew Scheduling

The crew scheduling problem is an important and difficult problem in railway crew management. In this paper, we focus on the railway crew scheduling problem with time window constraints caused by meal break rules. To solve this optimization problem, a solution method is proposed based on a time-space-state network and Lagrangian relaxation. In this method, the "hard constraints" corresponding to the crew rules are described as the state of vertices in the time-space-state network. Based on the network, this problem is modeled as a network flow model, referred to as an initial model. To break the symmetry and improve the strength of the formulation, five valid inequalities are added. To solve the problem, we relax the coupling constraints by Lagrangian relaxation. The resulting subproblems are shortest path problems in the time-space-state networks. We propose a Lagrangian heuristic to find a feasible solution. Finally, the solution method is tested on real-world instances from an intercity rail line and a regional railway network in China. We discuss the effects of additional valid inequalities and the effects of different length of meal time windows

Railway crew capacity planning problem with connectivity considerations in pairings

Crew is one of the most crucial resources in railway planning that needs to be considered at strategic, tactical and operational planning levels. During the last decade, crew-related costs outweigh energy expenditures and constitute more than one third of general expenditures in most railways. Therefore, sufficient but effective crew management is a critical planning problem which may lead to important savings. In this study, we deal with the tactical crew capacity planning problem which determines the minimum required number of crew members. In our setting, the feasibility of crew schedules and the connectivity of rosters are integrated to find a repeatable set of schedules that satisfy the operational rules and regulations. We develop a set-covering type formulation and propose a simultaneous column-and-row generation algorithm. We also propose a network representation of the problem and develop a corresponding network flow formulation. In order to compare efficiency and effectiveness of the two solution methods, we perform a comprehensive computational study with data sets acquired from Turkish State Railways and present the results

A Combined Adaptive Tabu Search and Set Partitioning Approach for the Crew Scheduling Problem with an Air Tanker Crew Application

This research develops the first metaheuristic approach to the complete air crew scheduling problem. It develops the first dynamic, integrated, set-partitioning based vocabulary scheme for metaheuristic search. Since no benchmark flight schedules exist for the tanker crew scheduling problem, this research defines and develops a Java™ based flight schedule generator. The robustness of the tabu search algorithms is judged by testing them using designed experiments. An integer program is developed to calculate lower bounds for the tanker crew scheduling problem objectives and to measure the overall quality of solutions produced by the developed algorithms

Models and methods for operational planning in freight railroads

textRailroads are facing increasing demand for freight transportation. Effective planning and scheduling are crucial to improve the utilization of expensive resources (such as crew and track), reduce operational costs, and provide on-time service. This dissertation focuses on problem modeling and solution method development for real planning problems faced by railroads. It consists of three chapters that study two important planning problems in the daily operations of U.S. freight railroads: crew assignment and train movement planning. Chapter 2 proposes an optimization model to decide crew-to-train assignments and deadheads for double-ended crew districts. We develop an effective solution approach, combining optimization and a standalone heuristic, that generates optimal solutions in minutes. The excellent performance of this solution approach makes it well-suited for implementation within a real-time decision support tool for crew dispatchers. Chapter 3 discusses crew repositioning given the uncertainty in trains’ arrival and departure times. We propose models that minimize the expected crew holding, train delay, and deadheading cost, and develop both exact and heuristic solution methods to provide insights for crew planning under train schedule uncertainty. The last chapter studies the movement planning problem for trains traveling in a territory with multiple through tracks (mainlines) and various junctions. We explore a number of heuristic algorithms to obtain good solutions within a reasonable amount of time. The contributions of this dissertation include modeling enhancements, algorithmic development, implementation and computational testing, and validation using real data.Operations Research and Industrial Engineerin

Modeling and Solving of Railway Optimization Problems

The main aim of this work is to provide decision makers suitable approaches for solving two crucial planning problems in the railway industry: the locomotive assignment problem and the crew scheduling problem with attendance rates. On the one hand, the focus is on practical usability and the necessary integration and consideration of real-life requirements in the planning process. On the other hand, solution approaches are to be developed, which can provide solutions of sufficiently good quality within a reasonable time by taking all these requirements into account