Optimized shunting with mixed-usage tracks

We consider the planning of railway freight classification at hump yards, where the problem involves the formation of departing freight train blocks from arriving trains subject to scheduling and capacity constraints. The hump yard layout considered consists of arrival tracks of sufficient length at an arrival yard, a hump, classification tracks of non-uniform and possibly non-sufficient length at a classification yard, and departure tracks of sufficient length. To increase yard capacity, freight cars arriving early can be stored temporarily on specific mixed-usage tracks. The entire hump yard planning process is covered in this paper, and heuristics for arrival and departure track assignment, as well as hump scheduling, have been included to provide the neccessary input data. However, the central problem considered is the classification track allocation problem. This problem has previously been modeled using direct mixed integer programming models, but this approach did not yield lower bounds of sufficient quality to prove optimality. Later attempts focused on a column generation approach based on branch-and-price that could solve problem instances of industrial size. Building upon the column generation approach we introduce a direct arc-based integer programming model, where the arcs are precedence relations between blocks on the same classification track. Further, the most promising models are adapted for rolling-horizon planning. We evaluate the methods on historical data from the Hallsberg shunting yard in Sweden. The results show that the new arc-based model performs as well as the column generation approach. It returns an optimal schedule within the execution time limit for all instances but from one, and executes as fast as the column generation approach. Further, the short execution times of the column generation approach and the arc-indexed model make them suitable for rolling-horizon planning, while the direct mixed integer program proved to be too slow for this. Extended analysis of the results shows that mixing was only required if the maximum number of concurrent trains on the classification yard exceeds 29 (there are 32 available tracks), and that after this point the number of extra car roll-ins increases heavily

Optimization models of rail transportation under the financial crisis

This paper proposes an analysis of the most used models to optimize the rail transportation. Are presented a series of optimization models of labor efficiency in this sector, but also elements that gives the information on the competitiveness of this mode of transport.railway, railway optimization, optimization models for railway

Optimisation of simultaneous train formation and car sorting at marshalling yards

Efficient and correct freight train marshalling is vital for high quality carload freight transportations. During marshalling, it is desirable that cars are sorted according to their individual drop-off locations in the outbound freight trains. Furthermore, practical limitations such as non-uniform and limited track lengths and the arrival and departure times of trains need to be considered. This paper presents a novel optimisation method for freight marshalling scheduling under these circumstances. The method is based on an integer programming formulation that is solved using column generation and branch and price. The approach minimises the number of extra shunting operations that have to be performed, and is evaluated on real-world data from the Hallsberg marshalling yard in Sweden

Robust multi-objective design of suspension systems

This thesis presents a robust multi-objective optimal design of four-degree-of-freedom passive and semi-active suspension systems. The passive suspension system is used in a racing car and the semi-active suspension is implemented on a passenger car. Mathematical models of the commercial and racing vehicle suspension systems are used in the computer simulations. A robust multi-objective design of the suspension systems is carried out by considering the minimization of three objectives: passenger’s head acceleration (HA), suspension deflection (SD), and tire deflection (TD). The first objective is concerned with the passenger’s health and comfort. The suspension stroke is described by SD and the tire holding is characterized by TD. The optimal design of the passive suspension involves tuning the coefficients of the sprung spring and damper, tire stiffness, and inertance of the inerter. Suspension systems’ parametric variations are very common and cannot be avoided in practice. To this end, a robust multi-objective optimization method that takes into consideration small changes in the design parameters should be considered. Unlike traditional multi-objective optimization problems where the focus is placed on finding the global Pareto-optimal solutions which express the optimal trade-offs among design objectives, the robust multi-objective optimization algorithms are concerned with robust solutions that are less sensitive to perturbations of decision variables. As a result, the mean effective values of the fitness functions are used as design objectives. Constraints on the design parameters and goals are applied. Numerical simulations show that the robust multi-objective design (RMOD) is very effective and guarantees a robust behavior as compared to that of the classical multi-objective design (MOD). The results also show that the robust region is inside the feasible search space and avoids all of its boundaries. The decision parameter space of the semi-active suspension includes both passive and active components. The passive components include the stiffness of the sprung spring, damping coefficient of the shock absorber, and stiffness of the tire. The active elements are the design details of the LQR algorithm. During the design, global sensitivity analysis is conducted to determine the elements of the suspension system that have high impact on the design objectives. The mass of the passenger’s head and upper body, the mass of the passenger’s lower body and cushion, passenger and cushion’s elastic properties, and the sprung mass of the vehicle are selected for the sensitivity analysis. Results show that the design goals are more sensitive to the variations in the sprung mass than the other parameters. As a result, parametric variations in the sprung mass of the vehicle and passive elements of the suspension system are considered. Similar to the design of the passive suspension, the mean effective values of SD, TD, and HA are used as design objectives. Also, constraints are applied on the objectives in compliance with the requirements of ISO 2631-1 on the design of car suspension systems. The optimization problem is solved by the NSGA-II (non-dominated sorting genetic algorithm) and robust Pareto front and set are obtained

Development of a prognostics and health management system for the railway infrastructure – Review and methodology

The Prognostics and Health Management (PHM) can be considered as a key process to deploy a predictive maintenance program. Since its inception as an engineering discipline, a lot of diagnostics and prognostics algorithms were developed and furthermore methodologies for health management and PHM development established. These solutions were applied in a lot of industrial cases aiming a maintenance transformation. In the Aerospace and Military systems, for example, the PHM has been applied more than 20 years with systems and components applications. During this last decade, the railway industry focused on maintenance issues and expressed a special interest on the PHM systems. The maintenance of the railway infrastructure requires considerable resources and an important budget. Many of the developed algorithms and methodologies can be imported to the Rail Transport systems. However, a methodology to develop a PHM system for a railway infrastructure must be established. This paper provides an overview on the key steps to design a PHM system regarding to the specific characteristics of the railway infrastructure. In addition, tools and procedures for each level of the PHM process are reviewed, as well as a summary of the existing monitoring, health assessment and decision solutions for the railway infrastructure

Smart maintenance and inspection of linear assets: An Industry 4.0 approach

Linear assets have linear properties, for instance, similar underlying geometry and characteristics, over a distance. They show specific patterns of continuous inherent deteriorations and failures. Therefore, remedial inspection and maintenance actions will be similar along the length of a linear asset, but because as the asset is distributed over a large area, the execution costs are greater. Autonomous robots, for instance, unmanned aerial vehicles, pipe inspection gauges, and remotely operated vehicles, are used in different industrial settings in an ad-hoc manner for inspection and maintenance. Autonomous robots can be programmed for repetitive and specific tasks; this is useful for the inspection and maintenance of linear assets. This paper reviews the challenges of maintaining the linear assets, focusing on inspections. It also provides a conceptual framework for the use of autonomous inspection and maintenance practices for linear assets to reduce maintenance costs, human involvement, etc., whilst improving the availability of linear assets by effective use of autonomous robots and data from different sources