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Journal articles on the topic 'Pricing – Computer simulation'

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Published: 4 June 2021

Last updated: 7 February 2022

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1

Chung, Doo-Shik, Hyeon Jo, and Soung-Hie Kim. "Dynamic Pricing for User Created Contents : Computer Modeling and Simulation." Journal of the Korea Contents Association 12, no. 6 (June 28, 2012): 56–67. http://dx.doi.org/10.5392/jkca.2012.12.06.056.

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2

Aly Dia, El Hadj. "Simulation of Lévy processes and option pricing." Journal of Computational Finance 17, no. 2 (December 2013): 41–69. http://dx.doi.org/10.21314/jcf.2013.260.

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3

Stamatopoulos, Nikitas, Daniel J. Egger, Yue Sun, Christa Zoufal, Raban Iten, Ning Shen, and Stefan Woerner. "Option Pricing using Quantum Computers." Quantum 4 (July 6, 2020): 291. http://dx.doi.org/10.22331/q-2020-07-06-291.

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We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a quadratic speedup compared to classical Monte Carlo methods. The options that we cover include vanilla options, multi-asset options and path-dependent options such as barrier options. We put an emphasis on the implementation of the quantum circuits required to build the input states and operators needed by amplitude estimation to price the different option types. Additionally, we show simulation results to highlight how the circuits that we implement price the different option contracts. Finally, we examine the performance of option pricing circuits on quantum hardware using the IBM Q Tokyo quantum device. We employ a simple, yet effective, error mitigation scheme that allows us to significantly reduce the errors arising from noisy two-qubit gates.

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4

GUPTA, APARNA, SHIVKUMAR KALYANARAMAN, and LINGYI ZHANG. "A SPOT PRICING FRAMEWORK FOR PRICING INTRA-DOMAIN ASSURED BANDWIDTH SERVICES." International Journal of Information Technology & Decision Making 04, no. 01 (March 2005): 35–58. http://dx.doi.org/10.1142/s021962200500143x.

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The Internet today offers primarily a best-effort service. Research and technology development efforts are currently underway to allow the provisioning of better than best-effort quality of service assurances. In this article, we develop a spot pricing framework for expected bandwidth guaranteed Internet service contracts on a single domain. A nonlinear pricing model that maximizes the total surplus with cost constraints forms the basis for the framework. The spot price process is obtained for different demand profiles of the customer-base and demand arrival characteristics. Simulation modeling and analysis is employed to implement the pricing framework and analyze the price process under different market conditions and changes due to network failures. The framework when implemented at access or exchange points of different service provider domains will provide assured bandwidth for inter-domain traffic.

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5

James, Lancelot, Dohyun Kim, and Zhiyuan Zhang. "Exact simulation pricing with Gamma processes and their extensions." Journal of Computational Finance 17, no. 2 (December 2013): 3–39. http://dx.doi.org/10.21314/jcf.2013.259.

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6

Han, Gyu-Sik, Bo-Hyun Kim, and Jaewook Lee. "Kernel-based Monte Carlo simulation for American option pricing." Expert Systems with Applications 36, no. 3 (April 2009): 4431–36. http://dx.doi.org/10.1016/j.eswa.2008.05.004.

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7

Jang, Hanbyeol, Sangkwon Kim, Junhee Han, Seongjin Lee, Jungyup Ban, Hyunsoo Han, Chaeyoung Lee, Darae Jeong, and Junseok Kim. "Fast Monte Carlo Simulation for Pricing Equity-Linked Securities." Computational Economics 56, no. 4 (November 11, 2019): 865–82. http://dx.doi.org/10.1007/s10614-019-09947-2.

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8

Boyle, Phelim P., Adam W. Kolkiewicz, and Ken Seng Tan. "An improved simulation method for pricing high-dimensional American derivatives." Mathematics and Computers in Simulation 62, no. 3-6 (March 2003): 315–22. http://dx.doi.org/10.1016/s0378-4754(02)00248-3.

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9

Panini, R., and R. P. Srivastav. "Option pricing with Mellin transnforms." Mathematical and Computer Modelling 40, no. 1-2 (July 2004): 43–56. http://dx.doi.org/10.1016/j.mcm.2004.07.008.

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10

EBRAHIM, MUHAMMED-SHAHID. "PRICING ASSET BACKED ISLAMIC FINANCIAL INSTRUMENTS." International Journal of Theoretical and Applied Finance 03, no. 01 (January 2000): 59–83. http://dx.doi.org/10.1142/s0219024900000048.

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The social impact of bankruptcies and loan defaults result in structural impairment of the economy. This paper presents a judicious methodology for pricing durable asset backed financing facilities while reducing their risk of default. Although the framework of the study is that of an Islamic banking system, it can also be implemented by conventional intermediaries. Both credit as well as hybrid (quasi-equity) facilities in the form of Bai' Bithman Ajil (BBA)/Ijara wal-'Iqtina and Decreasing Mudharabah (DM) instruments are discussed using computer simulation. These are applied to the cases of automobile financing and home mortgages. Placing additional assets as Rehn (security) has the capacity to improve the financing ratio and/or the term to maturity of both kinds of vehicles.

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11

Lin, Li, Xiangyue Chen, Yiying Lou, Weijian Zhang, and Ru Zhang. "Task Pricing Optimization Model of Crowdsourcing Platform." Business and Management Studies 4, no. 3 (July 31, 2018): 44. http://dx.doi.org/10.11114/bms.v4i3.3384.

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In this paper, we established a task pricing optimization model by the Logistic and anti-resolve thought to work out the problem of unequal spatial distribution and overall low of the task completion rate in the crowdsourcing platforms. Combining with the actual application information, we use scatter diagram, contour map, etc. to make a qualitative study and find that the reason why some of the tasks are not accepted is because the enterprise failed to take the total task quotas around the task into consideration while pricing the task. Then, combined with the influencing factors of traditional pricing model and results of qualitative analyses, the optimization model of crowdsourcing platform is built. Next, we select an ending project in an app of “make money” in China as the example to evaluate the effectiveness of our model. We applied the method of computer simulation to solve the model, and we find that, under the new pricing plan, the task completion rate has been significantly improved, which proves the conclusion of our qualitative analysis and the validity of the optimal model.

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12

Nørgaard, Jacob, Tamás Kerekes, and Dezso Séra. "Case Study of Residential PV Power and Battery Storage with the Danish Flexible Pricing Scheme." Energies 12, no. 5 (February 28, 2019): 799. http://dx.doi.org/10.3390/en12050799.

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The economic viability of renewable energy generation is vital for sustainability. Ensuring that optimal operation is always achieved, using energy management systems and control algorithms, is essential in this endeavor. Here, a new real-time pricing scheme, the Danish flexible pricing scheme, illustrates how residential PV and battery systems can optimize the electricity bill of households, without changing consumption behavior or providing grid services in exchange. This means that the only addition is PV production, storage, and control. A case study is constructed from Danish household consumption data, irradiance measurements, and recorded spot prices. With the input data, the pricing scheme, and the energy flow, simulation models are computed in MATLAB, thereby validating the algorithmic potential and finding the best strategy for charging and discharging the energy storage unit. Different methods are compared to list the viable options and evaluate them, based on the economic feasibility for the household. Furthermore, a discussion of the system implementation is also included to highlight technical difficulties, co-integration opportunities, short-comings, and advantages present in the case study. In conclusion, it is possible to make renewable energy generation, and storage, viable for a Danish residential household under the new pricing scheme.

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13

Lombardi, Claudio, Luís Picado-Santos, and Anuradha M. Annaswamy. "Model-Based Dynamic Toll Pricing: An Overview." Applied Sciences 11, no. 11 (May 23, 2021): 4778. http://dx.doi.org/10.3390/app11114778.

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In this paper, we review some of the most recent research regarding design, simulation, implementation and evaluation of dynamic tolling schemes. Analyzing the structure of the reviewed studies, we identify the common elements and the differences in the approaches chosen by different authors, presenting an overview of the methods for price definition and of the simulation techniques as well as a discussion on the newest technology applications in the field. Optimization revealed to be the dominant price definition method, while control-based algorithms are notably employed for managed lanes toll pricing schemes. Regarding traffic and driver behavior simulation we observed a great variety of solutions throughout the reviewed papers, with a prevalence of macroscopic models for the former and logit models for the latter. Still few papers include models for externalities quantification, while AI paradigms are gaining importance in the field.

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14

Gilbert, François, Patrice Marcotte, and Gilles Savard. "Logit network pricing." Computers & Operations Research 41 (January 2014): 291–98. http://dx.doi.org/10.1016/j.cor.2013.05.010.

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15

Ghasemalipour, Sara, and Behrouz Fathi-Vajargah. "Fuzzy simulation of European option pricing using mixed fractional Brownian motion." Soft Computing 23, no. 24 (March 5, 2019): 13205–13. http://dx.doi.org/10.1007/s00500-019-03862-2.

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16

Goard, J., P. Broadbridge, and G. Raina. "Tractable forms of the bond pricing equation." Mathematical and Computer Modelling 40, no. 1-2 (July 2004): 151–72. http://dx.doi.org/10.1016/j.mcm.2003.09.034.

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17

Liu, Zheng, Sijing Chen, Bin Hu, Min Zhou, and Yuanjun Zhao. "Research on Staged Pricing Model and Simulation of Intelligent Urban Transportation." IEEE Access 7 (2019): 141404–13. http://dx.doi.org/10.1109/access.2019.2944000.

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18

Hu, Yonghong, Qiang Li, Zongyan Cao, and Jue Wang. "Parallel simulation of high-dimensional American option pricing based on CPU versus MIC." Concurrency and Computation: Practice and Experience 27, no. 5 (April 11, 2014): 1110–21. http://dx.doi.org/10.1002/cpe.3275.

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19

Wang, Wenjie, and Lei Xie. "Dynamic Optimal Pricing of Ridesharing Platforms under Network Externalities with Stochastic Demand." Complexity 2021 (August 9, 2021): 1–16. http://dx.doi.org/10.1155/2021/6442797.

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Ridesharing two-sided platforms link the stochastic demand side and the self-scheduling capacity supply side where there are network externalities. The main purpose of this paper is to establish the optimal pricing model of ridesharing platforms to dynamically coordinate uncertain supply and stochastic demand with network externalities in order to maximize platforms’ revenue and social welfare. We propose dynamic pricing strategies under two demand scenarios that minimize order loss in the surge demand period and maximize social welfare in the declining demand period. The numerical simulation results show that dynamic pricing strategies could stimulate the supply to reduce delayed orders in the surge demand scenario and adjust the demand to maximize social welfare under declining demand scenario. Additionally, we further find that the direct network externalities positively influence the platforms’ revenue, and the indirect network externalities have a negative effect on social welfare in the declining demand scenario, and a higher wage ratio cannot enhance the platforms’ revenue.

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20

Singh, Sunil Kumar, and Deo Prakash Vidyarthi. "A Pricing Model for Effective Radio Spectrum Utilization." International Journal of Mobile Computing and Multimedia Communications 10, no. 4 (October 2019): 41–65. http://dx.doi.org/10.4018/ijmcmc.2019100104.

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Mobile users expect uninterrupted radio services whether operating in a host network or a foreign network. To support this, the cooperation of various mobile service providers becomes very important as they can share their available but unused resources among the mobile users. It has become possible for the mobile users to churn and leave the current service provider, if not happy with the offered services. This, eventually, may affect the revenue severely of the individual service provider besides defaming it. This work proposes a model on service pricing based on service providers' cooperation that utilizes the channels effectively and minimizes the call block and call drop. A penalty, on the service provider, is incorporated in the pricing which encourages a service provider to give utmost care to its users. A simulation experiment was carried out to study the performance of the proposed model, indicating the effectiveness of the model.

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21

RADONJIC DJOGATOVIC, V., and M. DJOGATOVIC. "A Novel Simulation Model for Pricing Different QoS Levels in IP Networks." Advances in Electrical and Computer Engineering 20, no. 1 (2020): 27–34. http://dx.doi.org/10.4316/aece.2020.01004.

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22

Vargas, Johandre, Carlos Jaime Franco, and Maritza Jimenez. "Electricity Pricing for Renewable Markets- A Simulation Approach for the Colombian Case." IEEE Latin America Transactions 19, no. 12 (December 2021): 1995–2002. http://dx.doi.org/10.1109/tla.2021.9480140.

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23

Smith, Harrison B., Amy Pielow, Adithya Jayakumar, Matteo Muratori, B. J. Yurkovich, Ramteen Sioshansi, Ashok Krishnamurthy, Giorgio Rizzoni, and Matthew C. Roberts. "User-Steered Energy Generation and Consumption Multimodel Simulation for Pricing and Policy Development." Computing in Science & Engineering 16, no. 2 (March 2014): 22–33. http://dx.doi.org/10.1109/mcse.2013.48.

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24

SHI, Feng, Yan CHEN, Hengxin LI, and Lianbo DENG. "Class of Comprehensive Optimization of Congested Road-Use Pricing and Parking Pricing." Journal of Transportation Systems Engineering and Information Technology 9, no. 1 (February 2009): 74–79. http://dx.doi.org/10.1016/s1570-6672(08)60046-x.

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25

Bandalouski, Andrei M., Natalja G. Egorova, Mikhail Y. Kovalyov, Erwin Pesch, and S. Armagan Tarim. "Dynamic pricing with demand disaggregation for hotel revenue management." Journal of Heuristics 27, no. 5 (June 7, 2021): 869–85. http://dx.doi.org/10.1007/s10732-021-09480-2.

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AbstractIn this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

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26

Piao, Ri, Deok-Joo Lee, and Taegu Kim. "Real-Time Pricing Scheme in Smart Grid Considering Time Preference: Game Theoretic Approach." Energies 13, no. 22 (November 23, 2020): 6138. http://dx.doi.org/10.3390/en13226138.

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Unbalanced power demand across time slots causes overload in a specific time zone. Various studies have proved that this can be mitigated through smart grid and price policy, but research on time preference is insufficient. This study proposed a real-time pricing model on a smart grid through a two-stage Stackelberg game model based on a utility function that reflects the user’s time preference. In the first step, the suppliers determine the profit-maximizing price, and then, the users decide the electricity usage schedule according to the given price. Nash equilibrium and comparative analysis of the proposed game explain the relationship between time preference, price, and usage. Additionally, a Monte Carlo simulation demonstrated the effect of the change in time preference distribution. The experimental results confirmed that the proposed real-time pricing method lowers peak-to-average ratio (PAR) and increases overall social welfare. This study is meaningful in that it presents a pricing method that considers both users’ and suppliers’ strategies with time preference. It is expected that the proposed method would contribute to a reduction in the need for additional power generation facilities through efficient operation of the smart grid.

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27

Tompaidis, Stathis, and Chunyu Yang. "Pricing American-style options by Monte Carlo simulation: alternatives to ordinary least squares." Journal of Computational Finance 18, no. 1 (September 2014): 121–43. http://dx.doi.org/10.21314/jcf.2014.279.

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28

Joseph, Shibily, and E. A. Jasmin. "Demand response program for smart grid through real time pricing and home energy management system." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (October 1, 2021): 4558. http://dx.doi.org/10.11591/ijece.v11i5.pp4558-4567.

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Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real time price is determined after considering price elasticity of various classes of consumers and their load profiles. A real time clustering algorithm suitable for big data of smart grid is devised for the segmentation of consumers. This paper is novel in its design for real time pricing and modelling and automatic scheduling of appliances for home energy management. Simulation results showed that this new real time pricing method is suitable for DR programs to reduce the peak load of the system as well as reducing the energy expenditure of houses, while ensuring profit for the retailer.

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29

Lu Hong, Li Yabin, Xing Xiaohui, and Li Lin. "Construction and Simulation of the Pricing Model of Medical Service in China's Public Hospitals." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 10 (May 31, 2013): 38–45. http://dx.doi.org/10.4156/aiss.vol5.issue10.5.

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30

Zhou, Yanglin, Lin Cheng, Song Ci, Yang Yang, and Shiqian Ma. "A User-Oriented Pricing Design for Demand Response in Smart Grid." Wireless Communications and Mobile Computing 2019 (September 10, 2019): 1–12. http://dx.doi.org/10.1155/2019/8694016.

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Demand response (DR) programs are designed to affect the energy consumption behavior of end-users in smart grid. However, most existing pricing designs for DR programs ignore the influence of end-users’s diversity and personal preference. Thus, in this paper, we investigate an incentive pricing design based on the utility maximization rule with consideration of end-users’ preference and appliances’ operational patterns. In particular, the utility company determines the pricing policy by trading off the budget revenue and social obligation, while each end-user aims to maximize their own utility profits with high satisfaction level by scheduling multiclass appliances. We formulate the conflict and cooperative relationship between the utility company and end-users as a Stackelberg game, and the equilibrium points are obtained by the backward induction method, which exists and is unique. At the equilibrium, the utility company adopts real-time pricing (RTP) scheme to coordinate end-users to fulfill the benefit of themselves, i.e., under such price, end-users automatically maximize overall utility profits of the overall system. We propose a distributed algorithm and an adaptive pricing scheme for the utility company and end-users to jointly achieve the best performance of the entire system. Finally, extensive simulation results based on real operation data show the effectiveness of the proposed scheme.

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31

Nichols, K. B., M. A. Venkataramanan, and K. W. Ernstberger. "Product line selection and pricing analysis: Impact of genetic relaxations." Mathematical and Computer Modelling 42, no. 13 (December 2005): 1397–410. http://dx.doi.org/10.1016/j.mcm.2005.02.006.

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32

Gallego-Ayala, Jordi. "Selecting irrigation water pricing alternatives using a multi-methodological approach." Mathematical and Computer Modelling 55, no. 3-4 (February 2012): 861–83. http://dx.doi.org/10.1016/j.mcm.2011.09.014.

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33

Schumacher, N. "Binomial option pricing with nonidentically distributed returns and its implications." Mathematical and Computer Modelling 29, no. 10-12 (May 1999): 121–43. http://dx.doi.org/10.1016/s0895-7177(99)00097-7.

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34

Xiaoqiang, Zhang, Ma Lang, and Zhang Jin. "Dynamic pricing for passenger groups of high-speed rail transportation." Journal of Rail Transport Planning & Management 6, no. 4 (January 2017): 346–56. http://dx.doi.org/10.1016/j.jrtpm.2017.01.001.

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35

Chakravarty, Saswata, Sindhu Padakandla, and Shalabh Bhatnagar. "A simulation-based algorithm for optimal pricing policy under demand uncertainty." International Transactions in Operational Research 21, no. 5 (December 30, 2013): 737–60. http://dx.doi.org/10.1111/itor.12064.

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36

Monsef, Hassan, and Bin Wu. "Real-time pricing program in a smart grid environment." SIMULATION 89, no. 4 (March 28, 2013): 513–23. http://dx.doi.org/10.1177/0037549712470732.

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37

Zhu, Qinwen, Grégoire Loeper, Wen Chen, and Nicolas Langrené. "Markovian Approximation of the Rough Bergomi Model for Monte Carlo Option Pricing." Mathematics 9, no. 5 (March 3, 2021): 528. http://dx.doi.org/10.3390/math9050528.

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The recently developed rough Bergomi (rBergomi) model is a rough fractional stochastic volatility (RFSV) model which can generate a more realistic term structure of at-the-money volatility skews compared with other RFSV models. However, its non-Markovianity brings mathematical and computational challenges for model calibration and simulation. To overcome these difficulties, we show that the rBergomi model can be well-approximated by the forward-variance Bergomi model with wisely chosen weights and mean-reversion speed parameters (aBergomi), which has the Markovian property. We establish an explicit bound on the L2-error between the respective kernels of these two models, which is explicitly controlled by the number of terms in the aBergomi model. We establish and describe the affine structure of the rBergomi model, and show the convergence of the affine structure of the aBergomi model to the one of the rBergomi model. We demonstrate the efficiency and accuracy of our method by implementing a classical Markovian Monte Carlo simulation scheme for the aBergomi model, which we compare to the hybrid scheme of the rBergomi model.

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38

Kayahan, Burç, and Thanasis Stengos. "Testing the capital asset pricing model with Local Maximum Likelihood methods." Mathematical and Computer Modelling 46, no. 1-2 (July 2007): 138–50. http://dx.doi.org/10.1016/j.mcm.2006.12.014.

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39

Ciurlia, P., and A. Gheno. "A model for pricing real estate derivatives with stochastic interest rates." Mathematical and Computer Modelling 50, no. 1-2 (July 2009): 233–47. http://dx.doi.org/10.1016/j.mcm.2008.12.005.

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40

Company, Rafael, Lucas Jódar, and José-Ramón Pintos. "Numerical analysis and computing for option pricing models in illiquid markets." Mathematical and Computer Modelling 52, no. 7-8 (October 2010): 1066–73. http://dx.doi.org/10.1016/j.mcm.2010.02.037.

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41

Kahn, Edward. "Regulation by Simulation: The Role of Production Cost Models in Electricity Planning and Pricing." Operations Research 43, no. 3 (June 1995): 388–98. http://dx.doi.org/10.1287/opre.43.3.388.

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42

Postigo-Boix, Marcos, and Jose L. Melus-Moreno. "A Proposal for Pricing Substitute Guaranteed Services." IEEE Communications Letters 15, no. 1 (January 2011): 100–102. http://dx.doi.org/10.1109/lcomm.2010.01.101336.

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43

Zhang, Yao, Zhiming Zheng, Lijia Xie, and Xiao Zhang. "DRDP: A DDoS-Resilient Data Pricing Mechanism." IEEE Communications Letters 20, no. 9 (September 2016): 1752–55. http://dx.doi.org/10.1109/lcomm.2016.2589261.

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44

Mondal, Washim Uddin, and Goutam Das. "Economics of TWDM PONs With Nonlinear Pricing." IEEE Communications Letters 23, no. 5 (May 2019): 822–25. http://dx.doi.org/10.1109/lcomm.2019.2909891.

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45

Chang, Woan Sun, and Robert Simon. "An Analysis of a Unified Pricing Model for Multiservice Networks." SIMULATION 82, no. 5 (May 2006): 331–44. http://dx.doi.org/10.1177/0037549706068839.

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46

Asgher, Urooj, Muhammad Rasheed, Ameena Al-Sumaiti, Atiq Rahman, Ihsan Ali, Amer Alzaidi, and Abdullah Alamri. "Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources." Energies 11, no. 12 (December 14, 2018): 3494. http://dx.doi.org/10.3390/en11123494.

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Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort..

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47

Wu, Qingtong. "Research on Pricing Strategy of Online and Offline Supply Chain Based on Channel Preference in the Context of New Retail." Complexity 2021 (September 2, 2021): 1–9. http://dx.doi.org/10.1155/2021/5211642.

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In recent years, brick-and-mortar retail has continuously encountered setbacks in the context of the rapid development of the Internet, and many brick-and-mortar stores cannot withstand losses and were closed down. E-commerce also seems to be able to intuitively understand the needs and preferences of consumers. With the continuous competition of online retail, the undifferentiated production of online retail has slowed down the development of e-commerce. The rise of the new retail model has promoted the production of high-quality products, which has greatly stimulated supply and demand. The new retail model is able to make better use of existing human and material resources and maximize the use of resources in today’s era of rapid technological changes. Online and offline network competition channels also exert different competitive advantages for different consumers. This paper studies the competition between physical retail and e-commerce retail and combines centralized decision-making and decentralized decision-making for analysis. It also calculates the relative optimal pricing price of e-commerce retail through numerical simulation calculations. Although the best pricing price is obtained after a series of calculations, it is still necessary to comprehensively consider and analyze multiple factors rationally to promote the long-term development of the enterprise. Although supply chain pricing strategies can solve certain problems in market sales to a certain extent, comprehensive analysis and scientifically formulating corporate development strategies are the guarantee of sustainable business operations.

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Sharma, Abhijit, Arvind Shah, Monish Chatterjee, and Uma Bhattacharya. "CAC DPLB MCN: A Distributed Load Balancing Scheme in Multimedia Mobile Cellular Networks." Foundations of Computing and Decision Sciences 41, no. 4 (November 1, 2016): 261–96. http://dx.doi.org/10.1515/fcds-2016-0015.

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Abstract The problem of non-uniform traffic demand in different cells of a cellular network may lead to a gross imbalance in the system performance. Thus, the users in hot cells may suffer from low throughput. In this paper, an effective and simple load balancing scheme CAC_DPLB_MCN is proposed that can effectively reduce the overall call blocking. This model considers dealing with multi-media traffic as well as time-varying geographical traffic distribution. The proposed scheme uses the concept of cell-tiering thereby creating fractional frequency reuse environment. A message exchange based distributed scheme instead of centralized one is used which help the proposed scheme be implemented in a multiple hot cell environment also. Furthermore, concept of dynamic pricing is used to serve the best interest of the users as well as for the service providers. The performance of the proposed scheme is compared with two other existing schemes in terms of call blocking probability and bandwidth utilization. Simulation results show that the proposed scheme can reduce the call blocking significantly in highly congested cell with highest bandwidth utilization. Use of dynamic pricing also makes the scheme useful to increase revenue of the service providers in contrast with compared schemes.

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Company, Rafael, Lucas Jódar, and José-Ramón Pintos. "A numerical method for European Option Pricing with transaction costs nonlinear equation." Mathematical and Computer Modelling 50, no. 5-6 (September 2009): 910–20. http://dx.doi.org/10.1016/j.mcm.2009.05.019.

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Hu, Feihu, Xuan Feng, and Hui Cao. "A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing." Energies 11, no. 12 (November 22, 2018): 3258. http://dx.doi.org/10.3390/en11123258.

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This paper establishes a short-term decision model, based on robust optimization, for an electricity retailer to determine the electricity procurement and electricity retail prices. The electricity procurement process includes purchasing electricity from generation companies and from the spot market. The selling prices of electricity for the customers are based on time-of-use (TOU) pricing which is widely employed in modern electricity market as a demand response program. The objective of the model is to maximize the expected profit of the retailer through optimizing the electricity procurement strategy and electricity pricing scheme. A price elasticity matrix (PEM) is adopted to model the demand response. Also, uncertainty in spot prices is modeled using a robust optimization approach, in which price bounds are considered instead of predicted values. Using a robust optimization approach, the retailer can adjust the level of robustness of its decisions through a robust control parameter. A case study is presented to illustrate the performance of the model. The simulation results demonstrate that the developed model is effective in increasing the expected profit of the retailer and flattening the load profiles of customers.

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