Modeling and Algorithms of the Crew Rostering Problem with Given Cycle on High-Speed Railway Lines

This paper studies the modeling and algorithms of crew roster problem with given cycle on high-speed railway lines. Two feasible compilation strategies for work out the crew rostering plan are discussed, and then an integrated compilation method is proposed in this paper to obtain a plan with relatively higher regularity in execution and lower crew members arranged. The process of plan making is divided into two subproblems which are decomposition of crew legs and adjustment of nonmaximum crew roster scheme. The decomposition subproblem is transformed to finding a Hamilton chain with the best objective function in network which was solved by an improved ant colony algorithm, whereas the adjustment of nonmaximum crew rostering scheme is finally presented as a set covering problem and solved by a two-stage algorithm. The effectiveness of the proposed models and algorithms are testified by a numerical example.

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Optimizing Crew Rostering with Multilicense on High-Speed Railway Lines

Discrete Dynamics in Nature and Society ◽

10.1155/2014/910569 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10

Author(s):

Zhiqiang Tian

Keyword(s):

Integer Programming ◽

High Speed ◽

Ant Colony Algorithm ◽

Programming Model ◽

Working Time ◽

High Speed Railway ◽

Crew Rostering ◽

Crew Rostering Problem ◽

Time Standard ◽

Improved Ant Colony Algorithm

This paper studies the crew rostering problem with the consideration of multilicense of crewmembers. A 0-1 integer programming model is established to minimize the number of crewmembers used and to maintain the working time balance and the income balance of crewmembers. The method for calculating the reasonable cycle schemes is designed by considering the parameters of monthly working time standard and the number and the average working time of crew routes. The order for selecting the optimal cycle scheme is then determined with consideration of the connection relationships between crew routes. According to the characteristics of the problem and the requirements in application, this paper presents the design of an improved ant colony algorithm for solving the optimization model. The reasonableness of the model and the effectiveness of the algorithm are verified by a numerical example with 43 generated crew routes.

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Behavior of the Ant Colony Algorithm for the Set Covering Problem

Operations Research Proceedings 1999 ◽

10.1007/978-3-642-58300-1_38 ◽

2000 ◽

pp. 255-260 ◽

Cited By ~ 10

Author(s):

Dmitri Alexandrov ◽

Yuri Kochetov

Keyword(s):

Ant Colony Algorithm ◽

Ant Colony ◽

Set Covering ◽

Covering Problem ◽

Set Covering Problem

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Using Improved Ant Colony Algorithm to Investigate EMU Circulation Scheduling Problem

Discrete Dynamics in Nature and Society ◽

10.1155/2014/767429 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Yu Zhou ◽

Leishan Zhou ◽

Yun Wang

Keyword(s):

High Speed ◽

Ant Colony Algorithm ◽

Travel Demand ◽

Maintenance Cost ◽

High Speed Railway ◽

Scheduling Scheme ◽

Scheduling Model ◽

Traditional Approaches

High-speed railway is one of the most important ways to solve the long-standing travel difficulty problem in China. However, due to the high acquisition and maintenance cost, it is impossible for decision-making departments to purchase enough EMUs to satisfy the explosive travel demand. Therefore, there is an urgent need to study how to utilize EMU more efficiently and reduce costs in the case of completing a given task in train diagram. In this paper, an EMU circulation scheduling model is built based on train diagram constraints, maintenance constraints, and so forth; in the model solving process, an improved ACA algorithm has been designed. A case study is conducted to verify the feasibility of the model. Moreover, contrast tests have been carried out to compare the efficiency between the improved ACA and the traditional approaches. The results reveal that improved ACA method can solve the model with less time and the quality of each representative index is much better, which means that efficiency of the improved ACA method is higher and better scheduling scheme can be obtained.

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Design of Intelligent Building Scheduling System for Internet of Things and Cloud Computing

Scalable Computing Practice and Experience ◽

10.12694/scpe.v22i2.1883 ◽

2021 ◽

Vol 22 (2) ◽

Author(s):

Tiangang Wang ◽

Zhe Mi

Keyword(s):

Cloud Computing ◽

Internet Of Things ◽

Real Time ◽

High Speed ◽

Ant Colony Algorithm ◽

Resource Scheduling ◽

Ant Colony ◽

Local Optimum ◽

The Real ◽

Improved Ant Colony Algorithm

The cloud computing (CC) and Internet of Things (IoT) are widely utilized and provided for intelligent perception and on-demand utilization like industries and public areas. The full sharing, free circulation and various manufacturing resources allocation are investigated in manufacturing. In order to ensure the real-time and effectiveness of resource storage scheduling in Internet of things information system, there are many kinds and quantities of building equipment. An improved ant colony algorithm is presented to remove the shortcomings of the existing ant colony algorithm with slow speed and fall into local optimum. The improved ant colony algorithm is transplanted into cloud computing environment. The advantages of fast computing and high speed storage of cloud computing can realize the real-time resource scheduling of building equipment. The experimental results present that the improved ant colony algorithm can obviously improve the efficiency of resource scheduling in cloud computing environment.All the experiments are performed on the MATLAB.

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Crew Planning Optimization Model of High-Speed Railway

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.869-870.298 ◽

2013 ◽

Vol 869-870 ◽

pp. 298-304 ◽

Cited By ~ 1

Author(s):

Jin Mei Li ◽

Lei Nie

Keyword(s):

Genetic Algorithm ◽

High Speed ◽

Simulated Annealing Algorithm ◽

Set Covering ◽

High Speed Railway ◽

Planning Optimization ◽

Proposed Model ◽

Phase Setting ◽

Crew Planning ◽

Covering Model

Crew planning with complicated constraints is decomposed into two sequential phases: crew scheduling phase, crew rostering phase. Setting a dynamic model based on set covering model, Genetic Algorithm is adopted based on feasible solution range in search of optimal scheduling set with minimum time. Constructing a node-arc TSP network, it adopts Genetic Algorithm and Simulated Annealing Algorithm to create a work roster. Based on Wuhan-Guangzhou High-Speed Railway in China, the balance degree of crew planning is measured by crew working time entropy. The proposed model proves strong practical application.

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