A multiobjective model for passenger train services planning: application to Taiwan's high-speed rail line

Chang, Ya Hui; Yeh, Chung‐Hsing; Shen, Ching Cheng

doi:10.1016/s0191-2615(99)00013-2

Cited by 196 publications

(129 citation statements)

References 23 publications

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“…Table 1 Table of For the routes that pass on parallel rail lines the intermediate station is shown. The number of sections, for which conditions (10), (11) and (12) are formed, is 31. The triangular fuzzy numbers for the passenger train capacity utilization coefficient of each assignment are determined taking into account the passenger train capacity utilization, which allows for covering the operating costs from the revenue.…”

Section: Resultsmentioning

confidence: 99%

“…In the field of railway transport the fuzzy linear optimization is used in [7][8][9][10][11]. A fuzzy optimization model is used for rescheduling high-speed railway timetable under unexpected interferences [7].…”

Section: Introductionmentioning

confidence: 99%

“…The railway section between Beijing and Zhengzhou in China is investigated in the research. In [11] is developed a multiobjective programming model for DOI: 10.22616/ERDev2017.16.N069 optimal allocation of passenger train services on an intercity high-speed rail line. The two objectives of the model are minimizing the operator's total operating cost and minimizing the passenger's total travel time loss.…”

Section: Introductionmentioning

confidence: 99%

“…In FLP, the uncertainty can be present in the objective function and/or in the constraints of the problem, and it can be represented by fuzzy sets. Many researchers have considered various types of the FLP problems and proposed several approaches for solving the FLP problems, [1][2][3][4][5][6].In the field of railway transport the fuzzy linear optimization is used in [7][8][9][10][11]. A fuzzy optimization model is used for rescheduling high-speed railway timetable under unexpected interferences [7].…”

mentioning

confidence: 99%

“…The railway section between Beijing and Zhengzhou in China is investigated in the research. In [11] is developed a multiobjective programming model for …”

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confidence: 99%

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Application of fuzzy linear programming method to examine routing of passenger trains

Stoilova¹

2017

Engineering for Rural Development

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Abstract. The aim of this study is to develop a methodology for selection of routing of different categories of intercity passenger trains taking into account of the uncertainty of the processes and using the fuzzy linear programming method. The fuzzy theory allows for the presentation of real data with uncertainty due to imprecise measurement. This research studies the fuzzy problems with fuzzy constraints of passengers and with fuzzy triangular numbers as coefficients of the technological matrix that presents how full a train is. As a criterion of the optimization minimum direct operating costs are used. The restrictive conditions of the model include: satisfying the demand for railway transport by sections of the railway network; capacity of the sections; required minimum frequency; available compositions in exploitation; integer and positive decision. The fuzzy linear problem has been reduced to parametric linear programming. Taking into account the parameter, the limits of amendment of the optimal number of trains depending on passenger flows have been defined. The application of the methodology makes it possible to study the impact of the fluctuations in passenger flows on the number of trains. The decision approach proposed in this paper was tested for Bulgarian rail network and proposed an organization of railway passenger transport. The approach presented here can also be used for solving the scheme of transportation for other types of transport.Keywords: fuzzy linear programming, passenger flows, routing, passenger trains capacity utilization coefficient. IntroductionThe development of an optimal transport plan of passenger trains on the railway network is related to a preliminary study of passenger flow, utilization of the train capacity and transport demand. Passenger flows are characterized by irregularity regarding hours, days of the week, months, and seasons. In many cases, passenger flows cannot be determined accurately, we cannot get enough real sample data to calculate the parameters by statistical ways. To deal with the problem in a mathematical way, these parameters can be treated as fuzzy variables. Fuzziness appears when the information is vague or is not clearly defined. The Fuzzy set theory allows for the description of real situations, taking into account the uncertainty of the processes. The fuzzy linear programming approach (FLP) is an extension of the linear programming (LP) approach that allows for the incorporation of the uncertainty factor in the construction of a mathematical model to increase the adequacy of the optimization. Fuzzy programming is more flexible and makes it possible to find solutions, which are more satisfactory for the real problem. In FLP, the uncertainty can be present in the objective function and/or in the constraints of the problem, and it can be represented by fuzzy sets. Many researchers have considered various types of the FLP problems and proposed several approaches for solving the FLP problems, [1][2][3][4][5][6].In the field of railway transport the fu...

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…The railway section between Beijing and Zhengzhou in China is investigated in the research. In [11] is developed a multiobjective programming model for …”

mentioning

confidence: 99%

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Application of fuzzy linear programming method to examine routing of passenger trains

Stoilova¹

2017

Engineering for Rural Development

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Development of efficient stop planning optimization process for high‐speed rail systems

Lai

Shih

Chen

2016

J of Advced Transportation

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The Taiwan High Speed Rail (THSR) has recently added three additional stations to its original network. Although the three additional stations can improve accessibility to the system, these new stations can present difficulties in the transportation planning process, particularly for planning of train stops. The additional stations may benefit some passengers, but may also lengthen the travel time for the other passengers. Therefore, the main challenge faced by THSR is finding an efficient way to design appropriate stopping patterns. Past studies on stop planning usually adopted meta-heuristics or decomposition methods to solve this complex problem. Although these solution techniques can improve solution efficiency, none of them can guarantee the optimality of the solution and capture the transfer movement of different stopping patterns. In this research, we proposed an innovative network structure to address complex stop planning problems for high-speed rail systems. Given its special network structure, two binary integer programming models were developed to simultaneously form and determine the optimal stopping patterns for real-world THSR stop planning problems. An optimization process was also developed to accurately estimate the station transfer time corresponding to the variation in stopping patterns and passenger flow. Results of the case studies suggest that the proposed binary integer programming models exhibit superior solution quality and efficiency over existing exact optimization models. Consequently, using this stop planning optimization process can help high-speed rail system planners in designing optimal stopping patterns that correspond to passenger demand.

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Multiobjective bilevel optimization for transportation planning and management problems

Yin

2002

J of Advced Transportation

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Many previous studies have formulated the decision-making problems in transportation system planning and management as single-objective bilevel optimization models. However, real-world decision-making processes always have several social concerns and thus multiple objectives need to be achieved simultaneously. In most cases, these objective functions conflict with each other and are also not simple enough to be combined into a single one. Therefore it is necessary to apply multiobjective optimization to generate nondominated or Pareto optimal alternatives. It can be foreseen that the multiobjective bilevel modeling approach can become a powerful, and possibly interactive, decision tool, allowing the decision-makers to learn more about the problem before committing to a final decision. Such multiobjective bilevel models are difficult to solve due to their intrinsic nonconvexity and multiple objectives. This paper consequently proposes a solution algorithm for the multiobjective bilevel models using genetic algorithms. The proposed algorithm is illustrated, using the numerical example taken from the previous study. It is found that the proposed algorithm is efficient to search simultaneously the Pareto optimal solutions.

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A multiobjective model for passenger train services planning: application to Taiwan's high-speed rail line

Cited by 196 publications

References 23 publications

Application of fuzzy linear programming method to examine routing of passenger trains

Application of fuzzy linear programming method to examine routing of passenger trains

Development of efficient stop planning optimization process for high‐speed rail systems

Multiobjective bilevel optimization for transportation planning and management problems

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