scholarly journals Robust Train Scheduling Problem with Optimized Maintenance Planning on High-Speed Railway Corridors: The China Case

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Chuntian Zhang ◽  
Yuan Gao ◽  
Wenjie Li ◽  
Lixing Yang ◽  
Ziyou Gao
Keyword(s):  
Travel Time ◽  
High Speed ◽  
Mode Selection ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Planning Problem ◽  
Scheduling Problem ◽  
Maintenance Planning ◽  
State Variables ◽  
Train Scheduling

Simultaneously considering train scheduling problem and maintenance planning problem with uncertain travel time, we propose a two-stage integrated optimization model for the sunset-departure and sunrise-arrival trains (SDSA-trains). Specifically, in the first stage, we obtain an optimal solution of the SDSA-trains under each scenario, which leads to the minimum total travel time. In the second stage, a robust SDSA-train schedule is generated based on the optimal solutions of the first stage. The key is that we consider two operation modes to solve the conflict between the SDSA-trains and the maintenances. Some state variables are used to deal with train operation mode selection. Furthermore, some linearization techniques are used to formulate a mixed-integer linear programming (MILP) model. Finally, numerical experiments are implemented to prove the effectiveness of the proposed model and optimization method.

Integrated Overnight Train Scheduling and Maintenance Planning for High-Speed Railway Lines

2020 ◽  
pp. 036119812096883
Author(s):  
Dian Wang ◽  
Shuguang Zhan ◽  
Qiyuan Peng ◽  
Wentao Zhou
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Mixed Integer ◽  
Maintenance Planning ◽  
High Speed Train ◽  
High Speed Railway ◽  
Train Scheduling ◽  
Railway Line ◽  
High Speed Trains ◽  
Commercial Solver

Overnight high-speed trains are very popular and convenient for passengers in countries with a large territory like China. However, the overnight high-speed train operation inevitably conflicts with the regular evening maintenance. We focus on both overnight high-speed train scheduling and maintenance planning to eliminate the conflict. Because some of the daytime high-speed trains that run early in the morning or late in the evening also interact with overnight high-speed trains and maintenance, we also allow them to be to slightly rescheduled to improve both the quality of the overnight train timetable and the maintenance plan. Our integrated optimization problem is formulated as a mixed integer linear programming model, which can be solved efficiently by the commercial solver CPLEX. Finally, we validate our model on a large real-world case constructed based on the Beijing–Guangzhou high-speed railway line in China.

scholarly journals Combinatorial Optimization of Service Order and Overtaking for Demand-Oriented Timetabling in a Single Railway Line

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Dewei Li ◽  
Shishun Ding ◽  
Yizhen Wang
Keyword(s):  
Travel Time ◽  
High Speed ◽  
Cost Benefit ◽  
Theory And Practice ◽  
Mixed Integer ◽  
High Speed Rail ◽  
Railway Line ◽  
Integer Quadratic Programming ◽  
Total Travel Time ◽  
Mixed Integer Quadratic Programming

Train timetabling is crucial for passenger railway operation. Demand-oriented train timetable optimization by minimizing travel time plays an important role in both theory and practice. Most of the current researches of demand-oriented timetable models assume an idealized situation in which the service order is fixed and in which zero overtaking exists between trains. In order to extend the literature, this paper discusses the combinatorial effect of service order and overtaking by developing four mixed-integer quadratic programming timetabling models with different service order as well as overtaking conditions. With the objective of minimizing passengers’ waiting time and in-vehicle time, the models take five aspects as constraints, namely dwell time, running time, safety interval, overtaking, and capacity. All four models are solved by ILOG CPLEX; and the results, which are based on Shanghai-Hangzhou intercity high-speed rail data, show that either allowing overtaking or changing service order can effectively optimize the quality of timetable with respect to reducing the total passengers’ travel time. Although optimizing train overtaking and service order simultaneously can optimize the timetable more significantly, compared to overtaking, allowing the change of service order can help passengers save total travel time without extending the train travel time. Moreover, considering the computation effort, satisfying both of the conditions in the meantime, when optimizing timetable has not got a good cost benefit.

scholarly journals Solving a Location, Allocation, and Capacity Planning Problem with Dynamic Demand and Response Time Service Level

2014 ◽  
Vol 2014 ◽  
pp. 1-25 ◽  
Author(s):  
Carrie Ka Yuk Lin
Keyword(s):  
Response Time ◽  
Travel Time ◽  
Service Level ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Planning Problem ◽  
Time Requirement ◽  
Time Service ◽  
Location Allocation ◽  
Total Capacity

Logistic systems with uncertain demand, travel time, and on-site processing time are studied here where sequential trip travel is allowed. The relationship between three levels of decisions: facility location, demand allocation, and resource capacity (number of service units), satisfying the response time requirement, is analysed. The problem is formulated as a stochastic mixed integer program. A simulation-based hybrid heuristic is developed to solve the dynamic problem under different response time service level. An initial solution is obtained from solving static location-allocation models, followed by iterative improvement of the three levels of decisions by ejection, reinsertion procedure with memory of feasible and infeasible service regions. Results indicate that a higher response time service level could be achieved by allocating a given resource under an appropriate decentralized policy. Given a response time requirement, the general trend is that the minimum total capacity initially decreases with more facilities. During this stage, variability in travel time has more impact on capacity than variability in demand arrivals. Thereafter, the total capacity remains stable and then gradually increases. When service level requirement is high, the dynamic dispatch based on first-come-first-serve rule requires smaller capacity than the one by nearest-neighbour rule.

scholarly journals Solving methods for the quay crane scheduling problem at port of Tripoli-Lebanon.

2020 ◽  
Author(s):  
Ali Skaf ◽  
Sid Lamrous ◽  
Zakaria Hammoudan ◽  
Marie-Ange Manier
Keyword(s):  
Dynamic Programming Algorithm ◽  
Heuristic Method ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Programming Algorithm ◽  
Scheduling Problem ◽  
Exact Methods ◽  
Three Solutions ◽  
Quay Crane Scheduling ◽  
Crane Scheduling

The quay crane scheduling problem (QCSP) is a global problem and all ports around the world seek to solve it, to get an acceptable time of unloading containers from the vessels or loading containers to the vessels and therefore reducing the docking time in the terminal. This paper proposes three solutions for the QCSP in port of Tripoli-Lebanon, two exact methods which are the mixed integer linear programming and the dynamic programming algorithm, to obtain the optimal solution and one heuristic method which is the genetic algorithm, to obtain near optimal solution within an acceptable CPU time. The main objective of these methods is to minimize the unloading or the loading time of the containers and therefore reduce the waiting time of the vessels in the terminals. We tested and validated our methods for small and large random instances. Finally, we compared the results obtained with these methods for some real instances in the port of Tripoli-Lebanon.

scholarly journals Non-convex nested Benders decomposition

2022 ◽  
Author(s):  
Christian Füllner ◽  
Steffen Rebennack
Keyword(s):  
Benders Decomposition ◽  
Unit Commitment ◽  
Piecewise Linear ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Global Optimality ◽  
Value Functions ◽  
State Variables ◽  
Lipschitz Continuous ◽  
Nested Benders Decomposition

AbstractWe propose a new decomposition method to solve multistage non-convex mixed-integer (stochastic) nonlinear programming problems (MINLPs). We call this algorithm non-convex nested Benders decomposition (NC-NBD). NC-NBD is based on solving dynamically improved mixed-integer linear outer approximations of the MINLP, obtained by piecewise linear relaxations of nonlinear functions. Those MILPs are solved to global optimality using an enhancement of nested Benders decomposition, in which regularization, dynamically refined binary approximations of the state variables and Lagrangian cut techniques are combined to generate Lipschitz continuous non-convex approximations of the value functions. Those approximations are then used to decide whether the approximating MILP has to be dynamically refined and in order to compute feasible solutions for the original MINLP. We prove that NC-NBD converges to an $$\varepsilon $$ ε -optimal solution in a finite number of steps. We provide promising computational results for some unit commitment problems of moderate size.

scholarly journals Train Scheduling with Deep Q-Network: A Feasibility Test

2020 ◽  
Vol 10 (23) ◽  
pp. 8367
Author(s):  
Intaek Gong ◽  
Sukmun Oh ◽  
Yunhong Min
Keyword(s):  
Decision Process ◽  
Value Function ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Q Value ◽  
Train Scheduling ◽  
Mip Models ◽  
Express Train ◽  
Markov Decision ◽  
Feasibility Test

We consider a train scheduling problem in which both local and express trains are to be scheduled. In this type of train scheduling problem, the key decision is determining the overtaking stations at which express trains overtake their preceding local trains. This problem has been successfully modeled via mixed integer programming (MIP) models. One of the obvious limitation of MIP-based approaches is the lack of freedom to the choices objective and constraint functions. In this paper, as an alternative, we propose an approach based on reinforcement learning. We first decompose the problem into subproblems in which a single express train and its preceding local trains are considered. We, then, formulate the subproblem as a Markov decision process (MDP). Instead of solving each instance of MDP, we train a deep neural network, called deep Q-network (DQN), which approximates Q-value function of any instances of MDP. The learned DQN can be used to make decision by choosing the action which corresponds to the maximum Q-value. The advantage of the proposed method is the ability to incorporate any complex objective and/or constraint functions. We demonstrate the performance of the proposed method by numerical experiments.

scholarly journals Effect of Railway Track Segmentation Method on the Optimal Solution of Tamping Planning Problem

2021 ◽  
Vol 7 (12) ◽  
pp. 1998-2010
Author(s):  
Mohammad Daddow ◽  
Xinglin Zhou ◽  
Hasan A.H. Naji ◽  
Mo'men Ayasrah
Keyword(s):  
Programming Model ◽  
Optimal Solution ◽  
Railway Track ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
Planning Problem ◽  
Segmentation Method ◽  
Railway Network ◽  
Railway Infrastructure ◽  
Optimal Maintenance

The safety and continuality of the railway network are guaranteed by carrying out a lot of maintenance interventions on the railway track. One of the most important of these actions is tamping, where railway infrastructure managers focus on optimizing tamping activities in ballasted tracks to reduce the maintenance cost. To this end, this article presents a mixed integer linear programming model of the Tamping Planning Problem (TPP) and investigates the effect of track segmentation method on the optimal solution by three scenarios. It uses an opportunistic maintenance technique to plan tamping actions. This technique clusters many tamping works through a time period to reduce the track possession cost as much as possible. CPLEX 12.6.3 is used in order to solve the TPP instances exactly. The results show that the total number of machine preparations increases by increasing the number of track segments. It is also found that the total costs increase by 6.1% and 9.4% during scenarios 2 and 3, respectively. Moreover, it is better to consider the whole railway track as a single segment (as in scenarios 1) that consists of a set of sections during the tamping planning in order to obtain the optimal maintenance cost. Doi: 10.28991/cej-2021-03091774 Full Text: PDF

scholarly journals A High-speed Train Timetable Rescheduling Approach in case of Disturbances

2020 ◽  
Vol 308 ◽  
pp. 02001
Author(s):  
Xinyu Gao
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Optimal Solution ◽  
Computation Time ◽  
Mixed Integer ◽  
Railway Line ◽  
Train Timetable ◽  
Train Operation ◽  
Time Required ◽  
Short Time

This paper from a macroscopic viewpoint develops a train timetable rescheduling approach on a single high-speed railway line under disturbances, i.e. inevitable train delays in the duration of the train operation. A mixed-integer linear programming model is formulated to minimize the arrival delay and the departure delay altogether. The commercial optimization software CPLEX is adopted in an effort to seek the optimal solution in an acceptably short time required in the real-time rescheduling process. The proposed approach is further tested on a real-world case study and the numerical results show that compared with the results obtain by the traditional genetic algorithm, using CPLEX to solve the model can yield better solutions and consume the desired computation time, thereby demonstrating its effectiveness and efficiency.

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