1,687 research outputs found
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
Analytical Models in Rail Transportation: An Annotated Bibliography
Not AvailableThis research has been supported, in part, by the U.S. Department of Transportation under contract DOT-TSC-1058, Transportation Advanced Research Program (TARP)
Single-machine scheduling with stepwise tardiness costs and release times
We study a scheduling problem that belongs to the yard operations component of the railroad planning problems, namely the hump sequencing problem. The scheduling problem is characterized as a single-machine problem with stepwise tardiness cost objectives. This is a new scheduling criterion which is also relevant in the context of traditional machine scheduling problems. We produce complexity results that characterize some cases of the problem as pseudo-polynomially solvable. For the difficult-to-solve cases of the problem, we develop mathematical programming formulations, and propose heuristic algorithms. We test the formulations and heuristic algorithms on randomly generated single-machine scheduling problems and real-life datasets for the hump sequencing problem. Our experiments show promising results for both sets of problems
Optimization of Locomotive Management and Fuel Consumption in Rail Freight Transport
For the enormous capital investment and high operation expense of locomotives, the locomotive management/assignment and fuel consumption are two of the most important areas for railway industry, especially in freight train transportation.
Several algorithms have been developed for the Locomotive Assignment Problem (LAP), including exact mathematics models, approximate dynamic programming and heuristics. These previously published optimization algorithms suffer from scalability or solution accuracy issues. In addition, each of the optimization models lacks part of the constraints that are necessary in real-world train/locomotive operation, e.g., maintenance/shop constraints or consist busting avoidance. Furthermore, there are rarely research works for the reduction of total train energy consumption on the locomotive assignment level.
The thesis is organized around our three main contributions. Firstly we propose a âconsist travel planâ based LAP optimization model, which covers all the required meaningful constraints and which can efficiently be solved using large scale optimization techniques, namely column generation (CG) decomposition. Our key contribution is that our LAP model can evaluate the occurrence of consist busting using the number of consist travel plans, and allows locomotive status transformation in flow conservation constraints.
In addition, a new column generation acceleration architecture is developed, that allows the subproblem, i.e., column generator to create multiple columns in each iteration, that each is an optimal solution for a reduced sub-network. This new CG architecture reduces computational time greatly comparing to our original LAP model.
For train fuel consumption, we derive, linearize and integrate a train fuel consumption model into our LAP model. In addition, we establish a conflict-free pre-process for time windows for train rescheduling without touching train-meet time and position. The new LAP-fuel consumption model works fine for the optimization of the train energy exhaustion on the locomotive assignment level.
For the optimization models above, the numerical results are conducted on the railway network infrastructure of Canada Pacific Railway (CPR), with up to 1,750 trains and 9 types of locomotives over a two-week time period in the entire CPR railway network
Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review
Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities
Control of the Supply Chain Optimization with Vehicle Scheduling of Logistics under Uncertain Systems
AbstractVehicle routing problem to operations research theory and practice closer together production, achieved a lot in recent decades, the field of operations research in recent decades one of the most successful research. Vehicle routing problem is to optimize transportation and supply chain optimization organization's core problem. This collection of domestic and foreign scholars through the research literature on the VRP data and literature data collation, classification, details of VRP problems at home and abroad Research on VRP models and algorithms were analyzed by the introduction of uncertain systems using system optimization based on uncertainty, so that you can use the methods for processing, logistics and vehicle scheduling the final decision to provide a scientific basis for decision making; through the VRP problem, models, algorithms and factor analysis, the proposed vehicle scheduling for logistics system development proposals
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
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