Simulation-based integrated optimization of nesting policy and booking limits for revenue management

Luo, Yongji; Yan, Haifeng; Zhang, Shoushuai

doi:10.1016/j.cie.2020.106864

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…When the new solution is not accepted, keep the old solution unchanged and cool down. when the current temperature is lower than the end temperature, end the algorithm and get the final solution [ 27 , 28 ].…”

Section: Optimization Of Neural Network Algorithm Based On High-order Simulated Annealingmentioning

confidence: 99%

[Retracted] Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High‐Order Simulated Annealing Neural Network Algorithm

Zhang

Zhong

2021

Computational Intelligence and Neuroscience

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With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.

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Section: Optimization Of Neural Network Algorithm Based On High-order Simulated Annealingmentioning

confidence: 99%

[Retracted] Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High‐Order Simulated Annealing Neural Network Algorithm

Zhang

Zhong

2021

Computational Intelligence and Neuroscience

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“…Additionally, different distributions for the demand were attempted in their study. Luo et al ( 2020 ) proposed a generalized nesting policy (GNP) that can enrich the family of nesting policies. A mathematical model for the nesting control under GNP was suggested, in which the nesting policy and booking limits were both taken as the decision variables.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When determining different fare levels in an airplane, the limited seats should be distributed to predetermined fare levels as efficiently as possible, since any unsold seats or opportunity costs resulting from selling too many low-fare seats are undesirable. In the literature, there are many optimization models such as dynamic programming (Kunnumkal and Topaloğlu, 2010 ; Selçuk and Avşar, 2019 ), linear programming (Möller et al, 2004 ; Liu and van Ryzin, 2008 ), bid price control (Akan and Ata, 2009 ; Talluri and van Ryzin, 1998 ), and nested booking limits (van Ryzin and Vulcano, 2008 ; Luo et al, 2020 ). Since the problem space is enormous and human-related aspects are involved, algorithms are becoming more complicated to tackle complex problems arising in the ARM.…”

Section: Introductionmentioning

confidence: 99%

EMSRtrc: relaxation of booking limits by total revenue control for expected marginal seat revenue

Ertuğrul

Şahin

2022

J Ambient Intell Human Comput

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The Covid-19 pandemic has negatively affected life worldwide and caused catastrophic loss of life. It has also been harming the economic activities of businesses, and airline companies are among the sectors most affected by this situation. One of the goals for survival in such a situation is to make the best in airline revenue management (ARM). The most helpful model for ARM is Expected Marginal Seat Revenue (EMSR), widely used in the literature and industry. In this study, the simple and effective models that simulated EMSRa, EMSRb, and EMSRc were developed, called EMSR Total Revenue Control (EMSRtrc). The proposed three models aim to keep the simplicity of the original EMSR models while creating a new perspective and methodology. The developed EMSRtrc models were tested with numerical examples and compared with EMSRa, EMSRb, and EMSRc models proposed previously in the literature. Numerical examples show that the developed EMSRtrc models perform better than the EMSRa, b, and c. For the minimum revenue category, the developed EMSRtrc models exhibit outstanding performance. The results show that the proposed models guarantee a higher minimum revenue of 9.900, 11.619, and 2.537%, respectively. The EMSRtrc models have generated higher revenue and achieved a higher load factor rate of up to 98% simultaneously. Considering the third numerical example, the approximate number of empty seats is 1.3 for the EMSRtrc-(a), 2.65 for the EMSRtrc-(b), and 7.85 for the EMSRtrc-(c). The overall results demonstrate that the proposed model is an effective tool for ARM.

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“…When the train offers multiple fare classes, a nesting control can avoid the situation in which more profitable bookings are rejected in favor of less profitable products. The virtual nesting controls (e.g., Smith and Penn, 1988; Bertsimas and de Boer, 2005; Luo et al., 2020) developed for airlines are not feasible for railways because these methods produce leg‐based allocations, which cannot meet the “one‐seat‐one‐ticket” restriction of the passenger railway. Therefore, a train usually sets nested allocation that is simply nested by fare classes.…”

Section: Traditional Railway Booking Control Strategiesmentioning

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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Simulation-based integrated optimization of nesting policy and booking limits for revenue management

Cited by 6 publications

References 30 publications

[Retracted] Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High‐Order Simulated Annealing Neural Network Algorithm

[Retracted] Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High‐Order Simulated Annealing Neural Network Algorithm

EMSRtrc: relaxation of booking limits by total revenue control for expected marginal seat revenue

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

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