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

Macura, Dragana; Šelmić, Milica; Teodorović, Dušan

doi:10.1111/itor.12465

Cited by 2 publications

(2 citation statements)

References 38 publications

(44 reference statements)

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“…Macura et al. (2019) suggested a solution for the capacity allocation problem of the railway industry. Linear integer programming and an artificial neural network were applied to derive a solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal seat allocation strategy for e‐sports gaming center

Kwag

Lee

2020

Int Trans Operational Res

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This paper investigates the seat allocation issues for an e‐sports gaming center where people generally visit in groups to play games while eating. According to the data analysis, as the number of customers in a group gets larger, the revenue per person tends to get higher. The data analysis to identify the information, such as revenue and the stay time of customers is conducted, and operations research is applied to derive the optimal seat allocation strategy. Through a numerical experiment with actual data, it is discovered that small groups of customers can also be prioritized in the same way as groups with larger numbers due to the fluctuating revenue generated by each group of customers during every timeslot. Furthermore, the opportunity cost that occurs by blocking customers even if there are available seats to accommodate the larger groups of customers who provide bigger revenue at various timeslots is considered.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal seat allocation strategy for e‐sports gaming center

Kwag

Lee

2020

Int Trans Operational Res

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“…The problem was formulated as a nonlinear integer programming model and solved by a heuristic method. Macura et al (2019) studied the inventory control problem of sleeping rail trains with variable capacity. They first obtained the solution by a deterministic allocation model and then used an artificial neural network to generate online rejection/acceptation decisions.…”

Section: Introductionmentioning

confidence: 99%

Hybrid nesting control strategy for passenger railway with one‐seat‐one‐ticket restriction

Luo

Zhang

Yan

et al. 2022

Int Trans Operational Res

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Nesting control is one of the most prevalent quantity‐based controls for the revenue management problem. A popular nesting control strategy for multi‐resource problem is the virtual nesting control, which sets nested booking limits on each resource. However, this control was originally developed for the airline and cannot be directly used in the passenger railway with one‐seat‐one‐ticket restriction. Therefore, this paper develops a new nesting control that is applicable to the railway. The proposed control can nest the capacity over different fare classes and origin‐destination pairs, which overcomes the shortcomings of existing railway booking‐limit controls. Numerical experiments are conducted in various scenarios to evaluate the performance. The results show that the hybrid nesting control outperforms the others in all situations. In addition, the revenue improvement increases with the randomness of demand and discount percentage.

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

Cited by 2 publications

References 38 publications

Optimal seat allocation strategy for e‐sports gaming center

Optimal seat allocation strategy for e‐sports gaming center

Hybrid nesting control strategy for passenger railway with one‐seat‐one‐ticket restriction

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