An efficient computational approach for railway booking problems

You, Peng-Sheng

doi:10.1016/j.ejor.2006.12.049

Cited by 73 publications

(38 citation statements)

References 17 publications

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“…Voss and Feng (2002), Jiang et al (2007), Yisu et al (2008) and You (2008) report some of this applications. This paper describes an application of such method in solving the TNDP.…”

Section: The Psomentioning

confidence: 94%

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Application of particle swarm optimization to transportation network design problem

Babazadeh

Poorzahedy

Nikoosokhan

2011

Journal of King Saud University - Science

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Transportation network design problem (TNDP) aims to choose from among a set of alternatives (e.g., set of new arcs) which minimizes an objective (e.g., total travel time), while keeping consumption of resources (e.g., budget) within their limits. TNDP is formulated as a bilevel programming problem, which is difficult to solve on account of its combinatorial nature. Following a recent, heuristic by ant colony optimization (ACO), a hybridized ACO (HACO) has been devised and tested on the network of Sioux Falls, showing that the hybrid is more effective to solve the problem. In this paper, employing the heuristic of particle swarm optimization (PSO), an algorithm is designed to solve the TNDP. Application of the algorithm on the Sioux Falls test network shows that the performance of PSO algorithm is comparable with HACO.

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“…Voss and Feng (2002), Jiang et al (2007), Yisu et al (2008) and You (2008) report some of this applications. This paper describes an application of such method in solving the TNDP.…”

Section: The Psomentioning

confidence: 94%

“…The main strength of PSO is its fast convergence, which compares favourably with many metaheuristics like GA and SA (Abraham et al, 2006). Moreover, it may be easily implemented, and requires few parameter settings and computational memory (You, 2008).…”

Section: The Psomentioning

confidence: 99%

Application of particle swarm optimization to transportation network design problem

Babazadeh

Poorzahedy

Nikoosokhan

2011

Journal of King Saud University - Science

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“…According to the literature review (Table ), three‐fourths of the papers consider rail as a passenger transport (Terabe and Ongprasert, ; Bharill and Rangaraj, ; Cote et al., ; Sibdari et al., ; You, ; Cirillo et al., ; Du and Yin, ; Dutta and Ghosh, ; Sato and Sawaki, ; Hetrakul and Cirillo, , 2015; Qian and Shuai, ; Zhu et al., ; Šelmić et al., ), and one‐fourth of the papers consider rail as a freight transport (Kraft et al., ; Kraft, ; Li and Tayur, ; Crevier et al., ; Bilegan et al., ).…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent sleeping car berth inventory control system in the case of variable capacity of railcars

Macura

Šelmić

Teodorović

2017

Int Trans Operational Res

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Managing ticket reservations in passenger rail transport is a complex task. A hybrid algorithm is developed as a decision support tool. The aim of the proposed algorithm is to provide real-time decisions on seat inventory allocation taking into account relevant information from the past. This paper considers the revenue management application in passenger rail transport with variable capacity of sleeping cars. Uncertainty of capacity availability is embedded in the developed algorithm through a payoff table, while an Artificial Neural Network is used as a tool for making real-time decisions. The algorithm is tested on hypothetical data.

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“…Feng and Chang (2008) also used revenue management modelling as a decision-support tool to enhance profit and management performance of liner shipping agencies. In other related industries, the slot allocation issue was also discussed by Madas and Zografos (2006), You (2008) and Zhang, et al (2011); the models and optimization methods for slot allocation in other fields are similar to those in the shipping industry.…”

Section: I T E R At U R E R E V I E W a N D O B J E C T I V Ementioning

confidence: 99%

Container Slot Co-Allocation Planning with Joint Fleet Agreement in a Round Voyage for Liner Shipping

Chen

Yahalom

2013

J. Navigation

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In the liner shipping market carriers share container slots to offer better service and realize economies of scale. This paper studies slot co-allocation planning for a joint fleet in a round trip for a shipping alliance in the liner shipping industry. In particular, a conceptual model is developed based on joint fleet and slot co-allocation management. The factors affecting slot co-allocation planning are explored in detail. A large-scale integer programming model is formulated to guide carriers in an alliance in pursuing an optimal slot co-allocation strategy. In contrast to the existing research, this approach leads to a more accurate representation of the situation for cooperative services in the liner shipping market. Extensive numerical experiments based on a true Asia-Europe cooperative route of COSCO and HANJIN show that the proposed model can be efficiently solved by LINGO11.0 for the case study. The computational results suggest that the mechanism and model can be used to benefit carriers in making better decisions in shipping cooperation services. K E Y WO R D S1. Slot co-allocation.2. Joint fleet. 3. Shipping alliance. 4. Liner shipping.

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An efficient computational approach for railway booking problems

Cited by 73 publications

References 17 publications

Application of particle swarm optimization to transportation network design problem

Application of particle swarm optimization to transportation network design problem

Intelligent sleeping car berth inventory control system in the case of variable capacity of railcars

Container Slot Co-Allocation Planning with Joint Fleet Agreement in a Round Voyage for Liner Shipping

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