Optimizing Skip Stop Service in Passenger Rail Transportation

2012 ◽  
Author(s):  
Samuel L. Sogin ◽  
Brennan M. Caughron ◽  
Samantha G. Chadwick
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Solution Space ◽  
Mixed Integer ◽  
Rail Transportation ◽  
Travel Times ◽  
Passenger Rail ◽  
Optimal Balance ◽  
Passenger Travel

Two-track passenger rail lines typically operate with all trains serving every station. Without additional infrastructure, transit planners have limited options to improve travel times. Service could be improved by operating a skip-stop service where trains only serve a subset of all the station stops. A skip-stop pattern must find an optimal balance between faster passenger travel times and lower service frequencies at each station. A mixed integer formulation is proposed to analyze this tradeoff; however, the mixed integer formulation could not scale efficiently to analyze a large scale commuter line. A genetic algorithm is presented to search the solution space incorporating a larger problem scope and complexity. In a case study of a Midwest commuter line, overall passenger travel time could be decreased by 9.5%. Both analyses can give insights to transit operators on how to improve their service to their customers and increase ridership.

Research of the Method for Traffic Generating Based on Genetic Algorithm

2014 ◽  
Vol 701-702 ◽  
pp. 3-7
Author(s):  
Liu Bo
Keyword(s):  
Genetic Algorithm ◽  
Network Traffic ◽  
Large Scale ◽  
Network Reliability ◽  
Actual Usage ◽  
Large Scale Network ◽  
Scale Network ◽  
Self Similar ◽  
Optimal Values

It has great impact on result of the network test or simulation if the test simulated traffic is corresponding to real situation. The network traffic is the superposition of different traffic streams in the actual usage of the network. But because of the complexity and time-consumption to generate different traffic streams, it is difficult to generate the network traffic in the simulation for the large scale network. This paper proposes a kind of method for traffic generating based on genetic algorithm .According to building the self-similar traffic model ,the optimal values of the model’s parameters has been obtained. A case study shows the effectiveness of the method for the network reliability.

Study on the Scheduling Problem of Powder Painting Production - A Case Study

2010 ◽  
Vol 97-101 ◽  
pp. 2459-2464
Author(s):  
Zhang Yong Hu ◽  
Qiang Su ◽  
Jun Liu ◽  
Hai Xia Yang
Keyword(s):  
Large Scale ◽  
Production Efficiency ◽  
Production Performance ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Adaptive Search ◽  
Optimal Sequence ◽  
Integer Nonlinear Programming ◽  
Minlp Model

A large-scale powder-painting scheduling problem is explored. The purpose is to find out the optimal sequence of a number of batches that dynamically arrive from upstream processes within a given scheduling horizon. The objective is to enhance the production efficiency and decrease the production cost as well. To solve this problem, a mixed integer nonlinear programming (MINLP) model is constructed and an algorithm called greedy randomized adaptive search procedure (GRASP) is designed. Case studies demonstrate that the proposed approach can improve the production performance significantly.

scholarly journals Mathematical Analysis and an Exact Solution Combined with Preprocessing Method for Resynchronizing of Bus Timetable Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Yinghui Wu ◽  
Yifan Zhu ◽  
Tianyu Cao
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Solution Space ◽  
Mixed Integer ◽  
Preprocessing Method ◽  
Proposed Model ◽  
Long Time ◽  
Bus Networks ◽  
Transfer Stations ◽  
Timetable Problem

Bus timetabling is a subproblem of bus network planning, and it determines departure time of each trip of lines to make vehicles from different lines synchronously arrive at transfer stations. Due to the well-designed coordination of bus timetables, passengers can make a smooth transfer without waiting a long time for connecting buses. This paper addresses the planning level of resynchronizing of bus timetable problem allowing modifications to initial timetable. Timetable modifications consist of shifts in the departure times and headways. A single-objective mixed-integer programming model is proposed for this problem to maximize the number of total transferring passengers benefiting from smooth transfers. We analyze the mathematical properties of this model, and then a preprocessing method is designed to reduce the solution space of the proposed model. The numerical results show that the reduced model is effectively solved by branch and bound algorithm, and the preprocessing method has the potential to be applied for large-scale bus networks.

Risk-Averse Optimization for Resilience Enhancement Under Uncertainty

2020 ◽  
Author(s):  
Jiaxin Wu ◽  
Pingfeng Wang
Keyword(s):  
Large Scale ◽  
Risk Measure ◽  
Recovery Process ◽  
Recovery Phase ◽  
Mixed Integer ◽  
Network Systems ◽  
Additional Risk ◽  
Deterministic Equivalent ◽  
Study Results

Abstract With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable towards external disturbances. Hence, managing potential disruptive events during design, operating, and recovery phase of an engineered system therefore improving the system’s resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed integer linear programming (MILP) based restoration framework using heterogenous dispatchable agents. Scenario based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from the nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves with a large-scale MILP problem and thus an adequate decompaction technique, i.e., modified Langragian Relaxation, is also proposed in order to achieve tractable time complexity. Case study results based on the IEEE 37-buses test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.

scholarly journals Charging Network Planning for Electric Bus Cities: A Case Study of Shenzhen, China

2019 ◽  
Vol 11 (17) ◽  
pp. 4713 ◽  
Author(s):  
Yuping Lin ◽  
Kai Zhang ◽  
Zuo-Jun Max Shen ◽  
Lixin Miao
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Cost Minimization ◽  
Transportation Network ◽  
Power Grid ◽  
Mixed Integer ◽  
Charging Infrastructure ◽  
Second Order Cone ◽  
Charging Stations

In 2017, Shenzhen replaced all its buses with battery e-buses (electric buses) and has become the first all-e-bus city in the world. Systematic planning of the supporting charging infrastructure for the electrified bus transportation system is required. Considering the number of city e-buses and the land scarcity, large-scale bus charging stations were preferred and adopted by the city. Compared with other EVs (electric vehicles), e-buses have operational tasks and different charging behavior. Since large-scale electricity-consuming stations will result in an intense burden on the power grid, it is necessary to consider both the transportation network and the power grid when planning the charging infrastructure. A cost-minimization model to jointly determine the deployment of bus charging stations and a grid connection scheme was put forward, which is essentially a three-fold assignment model. The problem was formulated as a mixed-integer second-order cone programming model, and a “No R” algorithm was proposed to improve the computational speed further. Computational studies, including a case study of Shenzhen, were implemented and the impacts of EV technology advancements on the cost and the infrastructure layout were also investigated.

Efficient airplane arrival scheduling using a set partitioning-based branch-and-price method

2017 ◽  
Vol 232 (16) ◽  
pp. 2939-2951
Author(s):  
Jae-Hoon Song ◽  
Han-Lim Choi
Keyword(s):  
Large Scale ◽  
Time Window ◽  
Heuristic Method ◽  
Solution Space ◽  
Exact Algorithm ◽  
Set Partitioning ◽  
Mixed Integer ◽  
Branch And Price ◽  
Mixed Integer Linear Program ◽  
Public Data

This article presents an exact algorithm that is combined with a heuristic method to find the optimal solution for an airplane landing problem. For a given set of airplanes and runways, the objective is to minimize the accumulated deviations from the target landing time of the airplanes. A cost associated with landing either earlier or later than the target landing time is incurred for each airplane within its predetermined time window. In order to manage this type of large-scale optimization problem, a set partitioning formulation that results in a mixed integer linear program is proposed. One key contribution of this article is the development of a branch-and-price methodology, in which the column generation method is integrated with the branch-and-bound method in order to find the optimal integer solution. In addition to the exact algorithm, a simple heuristic method is also presented to tighten the solution space. Numerical experiments are undertaken for the proposed algorithm in order to confirm its effectiveness using public data from the OR-Library. As an application in the real-world situation of airplane landing, air traffic data from Incheon International Airport is employed to assure the efficiency of the proposed algorithm.

scholarly journals The genome design suite: enabling massive in-silico experiments to design genomes

2019 ◽  
Author(s):  
Oliver Chalkley ◽  
Oliver Purcell ◽  
Claire Grierson ◽  
Lucia Marucci
Keyword(s):  
In Silico ◽  
Large Scale ◽  
Computational Models ◽  
Solution Space ◽  
Mycoplasma Genitalium ◽  
Specific Model ◽  
Computer Clusters ◽  
Optimisation Algorithms ◽  
Genome Design

AbstractMotivationComputational biology is a rapidly developing field, and in-silico methods are being developed to aid the design of genomes to create cells with optimised phenotypes. Two barriers to progress are that in-silico methods are often only developed on a particular implementation of a specific model (e.g. COBRA metabolic models) and models with longer simulation time inhibit the large-scale in-silico experiments required to search the vast solution space of genome combinations.ResultsHere we present the genome design suite (PyGDS) which is a suite of Python tools to aid the development of in-silico genome design methods. PyGDS provides a framework with which to implement phenotype optimisation algorithms on computational models across computer clusters. The framework is abstract allowing it to be adapted to utilise different computer clusters, optimisation algorithms, or design goals. It implements an abstract multi-generation algorithm structure allowing algorithms to avoid maximum simulation times on clusters and enabling iterative learning in the algorithm. The initial case study will be genome reduction algorithms on a whole-cell model of Mycoplasma genitalium for a PBS/Torque cluster and a Slurm cluster.AvailabilityThe genome design suite is written in Python for Linux operating systems and is available from GitHub on a GPL open-source [email protected], [email protected], and [email protected].

scholarly journals Optimizing Painting Sequence Scheduling Based on Adaptive Partheno-Genetic Algorithm

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1714
Author(s):  
Jun Yang ◽  
Tong Sun ◽  
Xiuxiang Huang ◽  
Ke Peng ◽  
Zhongxiang Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Programming Problem ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Real Life ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Rule Based ◽  
Multi Objective ◽  
Color Changes

In this paper, we formulate and solve a novel real-life large-scale automotive parts paint shop scheduling problem, which contains color arrangement restrictions, part arrangement restrictions, bracket restrictions, and multi-objectives. Based on these restrictions, we construct exact constraints and two objective functions to form a large-scale multi-objective mixed-integer linear programming problem. To reduce this scheduling problem’s complexity, we converted the multi-objective model into a multi-level objective programming problem by combining the rule-based scheduling algorithm and the adaptive Partheno-Genetic algorithm. The rule-based scheduling algorithm is adopted to optimize color changes horizontally and bracket replacements vertically. The adaptive Partheno-Genetic algorithm is designed to optimize production based on the rule-based scheduling algorithm. Finally, we apply the model to the actual optimization problem that contained 829,684 variables and 137,319 constraints, and solved this problem by Python. The proposed method solves the optimal solution, consuming 575 s.

scholarly journals Solution of Multi-Crew Depots Railway Crew Scheduling Problems: The Chinese High-Speed Railway Case

2022 ◽  
Vol 14 (1) ◽  
pp. 491
Author(s):  
Chunxiao Zhao ◽  
Junhua Chen ◽  
Xingchen Zhang ◽  
Zanyang Cui
Keyword(s):  
Genetic Algorithm ◽  
Development Strategy ◽  
High Speed ◽  
Large Scale ◽  
Mathematical Formulation ◽  
Solution Space ◽  
Working Time ◽  
Crew Scheduling ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm

This paper presents a novel mathematical formulation in crew scheduling, considering real challenges most railway companies face such as roundtrip policy for crew members joining from different crew depots and stricter working time standards under a sustainable development strategy. In China, the crew scheduling is manually compiled by railway companies respectively, and the plan quality varies from person to person. An improved genetic algorithm is proposed to solve this large-scale combinatorial optimization problem. It repairs the infeasible gene fragments to optimize the search scope of the solution space and enhance the efficiency of GA. To investigate the algorithm’s efficiency, a real case study was employed. Results show that the proposed model and algorithm lead to considerable improvement compared to the original planning: (i) Compared with the classical metaheuristic algorithms (GA, PSO, TS), the improved genetic algorithm can reduce the objective value by 4.47%; and (ii) the optimized crew scheduling plan reduces three crew units and increases the average utilization of crew unit working time by 6.20% compared with the original plan.

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