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Journal articles on the topic 'Elastic Demand'

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Published: 10 March 2023
Last updated: 11 March 2023

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1

Sheble, Gerald. "Demand Is Very Elastic!" IEEE Power and Energy Magazine 9, no. 2 (March 2011): 14–20. http://dx.doi.org/10.1109/mpe.2011.940264.

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2

Ranjbari, Andisheh, Afshin Shariat Mohaymany, and S. M. Mahdi Amiripour. "Transit Network Design: The Necessity of Elastic Demand Consideration." Applied Mechanics and Materials 97-98 (September 2011): 1117–22. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.1117.

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Transit network design as the first and critical phase of public transportation planning is extremely sensitive to transit demand. An important characteristic of transit demand is elasticity or service-dependency, which means that any change in the service offered by the system is followed by a change in transit demand. Due to the complexity of transit network design problem (TNDP) researchers have usually assumed transit demand to be fixed rather than elastic; while ignoring this issue may result in inefficiency of system, dissatisfaction of users, and system failure, since the predicted amount of passengers would not use the transit system. This paper aims to demonstrate the necessity of elastic demand consideration in transit network design, and proposes a solution framework, which is composed of a preparation stage and an iterative procedure. A case study example is presented subsequently, to show the use of this solution method and further illustrates the necessity of considering this issue. Three cases of truly predicted demand (considering elastic demand), overestimated and underestimated demands (in the absence of elastic demand consideration) are defined, and the performance measures of these cases are compared to those in the base mode. The results show that elastic demand consideration leads to the optimal network, in which the system efficiently matches between supply and demand.
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3

Yang, Hongtai, Zhaolin Zhang, Wenbo Fan, and Feng Xiao. "Optimal Design for Demand Responsive Connector Service Considering Elastic Demand." IEEE Transactions on Intelligent Transportation Systems 22, no. 4 (April 2021): 2476–86. http://dx.doi.org/10.1109/tits.2021.3054678.

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4

Perl, Jossef, and Peng-Kuan Ho. "Public Facilities Location under Elastic Demand." Transportation Science 24, no. 2 (May 1990): 117–36. http://dx.doi.org/10.1287/trsc.24.2.117.

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5

Kuo, April, Elise Miller-Hooks, and Hani S. Mahmassani. "Freight train scheduling with elastic demand." Transportation Research Part E: Logistics and Transportation Review 46, no. 6 (November 2010): 1057–70. http://dx.doi.org/10.1016/j.tre.2010.05.002.

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6

Marbán, Sebastián, Ruben van der Zwaan, Alexander Grigoriev, Benjamin Hiller, and Tjark Vredeveld. "Dynamic pricing problems with elastic demand." Operations Research Letters 40, no. 3 (May 2012): 175–79. http://dx.doi.org/10.1016/j.orl.2012.01.005.

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7

Choudhury, Panchali Datta. "Multicast Traffic Grooming in Elastic Optical Network Under Dynamic Scenario." American Journal of Science & Engineering 2, no. 4 (March 1, 2022): 15–17. http://dx.doi.org/10.15864/ajec.2403.

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Elastic optical networks allow elastic allocation and de-allocation of optical resources to optimize network resources and reduce traffic demand blocking probability for dynamic traffic demands. The routing of dynamic traffic demands is a challenging task since the traffic demands are not predefined, they arrive and leave randomly. The approach presented here is a grooming, routing and spectrum allocation technique for multicast traffic demands in elastic optical networks for dynamic type of traffic demands. The simulation results show reduced blocking probability compared to existing approach.
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8

Singh, Alok Kumar, and Rohit Kapoor. "Estimating Demand Using Space Elastic Demand Model for Retail Assortment Planning." Global Business Review 17, no. 3 (May 22, 2016): 524–40. http://dx.doi.org/10.1177/0972150916630448.

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9

Rutz, William L., Martin Becker, and Frank E. Wicks. "Treatment of Elastic Demand in Generation Planning." IEEE Power Engineering Review PER-5, no. 11 (November 1985): 31–32. http://dx.doi.org/10.1109/mper.1985.5528363.

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10

Gonçalves, Ricardo, and Vasco Rodrigues. "Reference Pricing with Elastic Demand for Pharmaceuticals." Scandinavian Journal of Economics 120, no. 1 (December 28, 2017): 159–82. http://dx.doi.org/10.1111/sjoe.12207.

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11

Robenek, Tomáš, Shadi Sharif Azadeh, Yousef Maknoon, Matthieu de Lapparent, and Michel Bierlaire. "Train timetable design under elastic passenger demand." Transportation Research Part B: Methodological 111 (May 2018): 19–38. http://dx.doi.org/10.1016/j.trb.2018.03.002.

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12

Rutz, William, Martin Becker, and Frank Wicks. "Treatment Of Elastic Demand In Generation Planning." IEEE Transactions on Power Apparatus and Systems PAS-104, no. 11 (November 1985): 3092–97. http://dx.doi.org/10.1109/tpas.1985.318817.

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13

Gan, Xiaoying, Haoxiang Zhang, Gai Hang, Zhida Qin, and Haiming Jin. "Fast-Charging Station Deployment Considering Elastic Demand." IEEE Transactions on Transportation Electrification 6, no. 1 (March 2020): 158–69. http://dx.doi.org/10.1109/tte.2020.2964141.

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14

Puu, Tönu. "Hotelling's "Ice cream dealers" with elastic demand." Annals of Regional Science 36, no. 1 (February 1, 2002): 1–17. http://dx.doi.org/10.1007/s001680100062.

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15

Sobekova, Kristina, Michael R. Thomsen, and Bruce L. Ahrendsen. "Market trends and consumer demand for fresh berries." Applied Studies in Agribusiness and Commerce 7, no. 2-3 (September 30, 2013): 11–14. http://dx.doi.org/10.19041/apstract/2013/2-3/1.

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We present an analysis of markets for fresh strawberries, blueberries, blackberries and raspberries in the United States during 2008–2011. We use weekly panel data covering supermarket purchases in 52 cities. The primary goal is to estimate demand elasticities for fresh berries and thereby provide a better understanding of consumer behaviour in response to price changes and the nature of competition among these crops. We estimate fixed and random effects models for double log demand equations and a complete demand system, the Almost Ideal Demand System. The latter specification can be used to estimate demand relationships that conform to utility maximising behaviour. The elasticity estimates are very robust across the different specifications and estimation methods. This increases confidence in our findings and provides some assurance that choice of functional form or estimation method is not driving our results. We find that retail demands for all berry crops are in the elastic range and that the different berries are substitutes for one another. The demand for strawberries was the least elastic with an own price elasticity of –1.26 and blackberries were the most elastic with a demand elasticity of –1.88. Blackberry demand was also the most responsive to the prices of competing berry crops. The study provides clearer insight into markets for berries in the United States. In addition, it fills a gap in the present lack of up-to-date consumer demand elasticities for these crops and will be useful for growers, decision makers and consumers.
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16

Han, Tong, and Thomas I. Wahl. "China's Rural Household Demand for Fruit and Vegetables." Journal of Agricultural and Applied Economics 30, no. 1 (July 1998): 141–50. http://dx.doi.org/10.1017/s1074070800008129.

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AbstractA two-stage budgeting LES-LA/AIDS system is used to estimate rural household demand in China with special emphasis on changes in demand for fruit and vegetable commodities across different income groups. The own-price elasticity for food was found to be more elastic than that for clothing, housing, durable goods, and other items. Within the food group, price elasticities range from –1.042 to –0.019. Grain, with an expenditure elasticity of almost unity, is an important staple food for the average rural household. Vegetables are important nonstaple foods relative to fruits. Lower value vegetables are the most price elastic in the vegetable group. Fruits are more price elastic than vegetables, with grapes being the most price elastic. Different income groups share a common demand function.
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17

Mohammed, Rezgar, and Olga Murova. "Examining Demand Elasticities in the U.S. Differentiated Yogurt Market." Applied Economics and Finance 6, no. 6 (October 17, 2019): 69. http://dx.doi.org/10.11114/aef.v6i6.4504.

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This article applies the Quadratic Almost Ideal Demand system (QUAIDS) model to households’ weekly purchases of yogurt augmented with household characteristics to analyze consumer choices and estimate demand elasticities in the U.S. differentiated yogurt market after the introduction of Chobani brand in 2005. Results show that households with a college degree are more likely to purchase Chobani and Dannon brands rather than Yoplait and private labels. Except for Dannon, demand is price elastic, while the new brand of Chobani has a higher elastic demand compared to the Yoplait brand. Branded yogurts are expenditure elastic with the highest magnitude for Chobani among brands.
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18

Dökmeci, V. F. "Multiplant Location with Respect to Price-Elastic Demand." Environment and Planning A: Economy and Space 21, no. 9 (September 1989): 1169–78. http://dx.doi.org/10.1068/a211169.

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In this paper a model is developed to determine the optimal location of plants by taking into consideration the price elastic demand, production cost, and transportation cost. It is assumed that demand is distributed unevenly. The objective is to determine the location of maximum profit. Each basic function is interrelated with other functions, and the location of maximum profit is a balanced situation of price, demand, economies of scale, and transportation cost. This situation results in a complicated function, and the solution cannot be obtained by techniques of direct calculation. Therefore, a stepwise heuristic approach is used. First, the number of plants is chosen and the allocation of plants is made with respect to a criterion of minimum distance. Demand and thus optimum locations are calculated according to different prices. The location of maximum net profit is determined for this particular number of plants. This procedure is repeated for a different number of plants. The alternative which has the maximum profit is chosen as being the best system.
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19

Gomes, Rafael L., Luiz F. Bittencourt, and Edmundo R. M. Madeira. "Reliability-Aware Network Slicing in Elastic Demand Scenarios." IEEE Communications Magazine 58, no. 10 (October 2020): 29–34. http://dx.doi.org/10.1109/mcom.001.2000753.

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20

Sun, Chao, Lin Cheng, Senlai Zhu, and Zhaoming Chu. "Multiclass Stochastic User Equilibrium Model with Elastic Demand." Transportation Research Record: Journal of the Transportation Research Board 2497, no. 1 (January 2015): 1–11. http://dx.doi.org/10.3141/2497-01.

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21

LIU, Lijuan, Ning ZHANG, Di CHEN, and Jian LIU. "Capacity Deterioration with Elastic Demand in Road Segment." Journal of Transportation Systems Engineering and Information Technology 12, no. 1 (February 2012): 98–104. http://dx.doi.org/10.1016/s1570-6672(11)60187-6.

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22

García-Ródenas, Ricardo, María Luz López-García, Julio Alberto López-Gómez, and Luis Jiménez Linares. "Passenger Centric Train Timetabling Problem with elastic demand." Transportation Research Procedia 47 (2020): 465–72. http://dx.doi.org/10.1016/j.trpro.2020.03.122.

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23

Abbott, Harish, and Udatta S. Palekar. "Retail replenishment models with display-space elastic demand." European Journal of Operational Research 186, no. 2 (April 2008): 586–607. http://dx.doi.org/10.1016/j.ejor.2006.12.067.

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24

Beard, Rodney. "N-FIRM OLIGOPOLY WITH GENERAL ISO-ELASTIC DEMAND." Bulletin of Economic Research 67, no. 4 (May 15, 2013): 336–45. http://dx.doi.org/10.1111/boer.12009.

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25

Götz, Georg, and Anna Hammerschmidt. "R&D COOPERATION WITH UNIT-ELASTIC DEMAND." Bulletin of Economic Research 61, no. 2 (April 2009): 179–88. http://dx.doi.org/10.1111/j.1467-8586.2008.00292.x.

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26

Zou, Wenqian, and Shoshi Mizokami. "Incentive subsidy scheme design with elastic transport demand." Journal of Advanced Transportation 48, no. 8 (October 11, 2013): 927–41. http://dx.doi.org/10.1002/atr.1253.

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27

Lichtenberg, Frank R. "How elastic is the government's demand for weapons?" Journal of Public Economics 40, no. 1 (October 1989): 57–78. http://dx.doi.org/10.1016/0047-2727(89)90018-2.

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28

Stafford, Peter J., Timothy J. Sullivan, and Domenico Pennucci. "Empirical Correlation between Inelastic and Elastic Spectral Displacement Demands." Earthquake Spectra 32, no. 3 (August 2016): 1419–48. http://dx.doi.org/10.1193/020515eqs021m.

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Inelastic spectral displacement demand is arguably one of the most effective, simplified means of relating earthquake intensity to building damage. However, seismic hazard assessment is typically conducted using empirical ground-motion prediction equations (GMPEs) that only provide indications of elastic spectral response quantities, which an engineer subsequently relates to inelastic demands using empirical relationships such as the equal-displacement rule. An alternative approach is to utilize relationships for the inelastic spectral displacement demand directly within the seismic hazard assessment process. Such empirical relationships are developed in this work, as a function of magnitude, distance, building period, and yield strength coefficient, for four different hysteretic models that are representative of a wide range of possible structural typologies found in practice. The new relationships are likely to be particularly useful for performance-based seismic design and assessment.
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29

Zheng, Zhihao, and Shida Rastegari Henneberry. "An Analysis of Food Grain Consumption in Urban Jiangsu Province of China." Journal of Agricultural and Applied Economics 42, no. 2 (May 2010): 337–55. http://dx.doi.org/10.1017/s1074070800003497.

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The impacts of economic and demographic variables on the demand for food grain commodities in urban Jiangsu province of China are estimated, using both the QUAIDS and the AIDS models. Results show that the demands for wheat flour and coarse grains are price-elastic while the demands for rice and grain products are price-inelastic. Certain demographic variables show as having a significant impact on food grain demand. Finally, a decomposition of causes of changes in rice consumption over the period of 1995-2007 is performed.
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30

Price, C., J. B. Christensen, and S. K. Humphreys. "Elasticities of Demand for Recreation Site and for Recreation Experience." Environment and Planning A: Economy and Space 18, no. 9 (September 1986): 1259–63. http://dx.doi.org/10.1068/a181259.

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Substitution makes demand for recreation sites more elastic than that for recreation experiences. Assessment of site elasticity by direct and Clawson methods yielded elastic estimates empirically, whereas response to hypothetical increased costs of travel yielded inelastic demand for recreation experiences. It is important to use the appropriate figure in recreation evaluations.
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31

Zheng, Shiyuan, and Rudy R. Negenborn. "Price negotiation between supplier and buyer under uncertainty with fixed demand and elastic demand." International Journal of Production Economics 167 (September 2015): 35–44. http://dx.doi.org/10.1016/j.ijpe.2015.05.024.

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32

LI, YAN-LAI, HUI-JUN SUN, and JIAN-JUN WU. "DYNAMICS BEHAVIORS OF SCALE-FREE NETWORKS WITH ELASTIC DEMAND." International Journal of Modern Physics C 19, no. 08 (August 2008): 1305–13. http://dx.doi.org/10.1142/s0129183108012893.

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Many real-world networks, such as transportation networks and Internet, have the scale-free properties. It is important to study the bearing capacity of such networks. Considering the elastic demand condition, we analyze load distributions and bearing capacities with different parameters through artificially created scale-free networks. The simulation results show that the load distribution follows a power-law form, which means some ordered pairs, playing the dominant role in the transportation network, have higher demand than other pairs. We found that, with the decrease of perceptual error, the total and average ordered pair demand will decrease and then stay in a steady state. However, with the increase of the network size, the average demand of each ordered pair will decrease, which is particularly interesting for the network design problem.
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33

Ma, Jie, Qiang Meng, Lin Cheng, and Zhiyuan Liu. "General stochastic ridesharing user equilibrium problem with elastic demand." Transportation Research Part B: Methodological 162 (August 2022): 162–94. http://dx.doi.org/10.1016/j.trb.2022.06.001.

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34

Lee, Enoch, Xuekai Cen, and Hong K. Lo. "Zonal-based flexible bus service under elastic stochastic demand." Transportation Research Part E: Logistics and Transportation Review 152 (August 2021): 102367. http://dx.doi.org/10.1016/j.tre.2021.102367.

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35

Lam, William H. K., and Jing Zhou. "Optimal Fare Structure for Transit Networks with Elastic Demand." Transportation Research Record: Journal of the Transportation Research Board 1733, no. 1 (January 2000): 8–14. http://dx.doi.org/10.3141/1733-02.

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A bilevel model is presented to optimize the fare structure for transit networks with elastic demand under the assumption of fixed transit service frequency. It is known that the transit fare structure has significant effects on passengers' demand and route choice behavior. The transit operator therefore should predict passengers' response to changing fare charges. A bilevel programming method is developed to determine the optimal fare structure for the transit operator while taking passengers' response into account. The upper-level problem seeks to maximize the operator’s revenue, whereas the lower-level problem is a stochastic user equilibrium transit assignment model with capacity constraints. A heuristic solution algorithm based on sensitivity analysis is proposed. Finally, a numerical example is given together with some useful discussion.
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36

Tu, Qiang, Lin Cheng, Dawei Li, Jie Ma, and Chao Sun. "Traffic Paradox Under Different Equilibrium Conditions Considering Elastic Demand." PROMET - Traffic&Transportation 31, no. 1 (February 21, 2019): 1–9. http://dx.doi.org/10.7307/ptt.v31i1.2795.

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Traffic paradox is an important phenomenon which needs attention in transportation network design and traffic management. Previous studies on traffic paradox always examined user equilibrium (UE) or stochastic user equilibrium (SUE) conditions with a fixed traffic demand (FD) and set the travel costs of links as constants under the SUE condition. However, traffic demand is elastic, especially when there are new links added to the network that may induce new traffic demand, and the travel costs of links actually depend on the traffic flows on them. This paper comprehensively investigates the traffic paradox under different equilibrium conditions including the user equilibrium and the stochastic user equilibrium with a fixed and elastic traffic demand. Origin-destination (OD) mean unit travel cost (MUTC) has been chosen as the main index to characterize whether the traffic paradox occurs. The impacts of travelers’ perception errors and travel cost sensitivity on the occurrence of the traffic paradox are also analyzed. The conclusions show that the occurrence of the traffic paradox depends on the traffic demand and equilibrium conditions; higher perception errors of travelers may lead to a better network performance, and a higher travel cost sensitivity will create a reversed traffic paradox. Finally, several appropriate traffic management measures are proposed to avoid the traffic paradox and improve the network performance.
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37

Abdolrahimi, Kamaleddin. "A Production Decision Model for Time‐dependent Elastic Demand." International Journal of Operations & Production Management 12, no. 9 (September 1992): 59–77. http://dx.doi.org/10.1108/01443579210017196.

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38

Thorbecke, Willem. "How Elastic is East Asian Demand for Consumption Goods?" Review of International Economics 19, no. 5 (October 17, 2011): 950–62. http://dx.doi.org/10.1111/j.1467-9396.2011.00997.x.

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39

Direr, Alexis, and Rim Ennajar-Sayadi. "How price-elastic is the demand for retirement saving?" Geneva Papers on Risk and Insurance - Issues and Practice 44, no. 1 (November 4, 2018): 102–22. http://dx.doi.org/10.1057/s41288-018-0112-5.

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40

Gruber, Jonathan, and Michael Lettau. "How elastic is the firm’s demand for health insurance?" Journal of Public Economics 88, no. 7-8 (July 2004): 1273–93. http://dx.doi.org/10.1016/s0047-2727(02)00191-3.

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41

Rath, Kali P., and Gongyun Zhao. "Two stage equilibrium and product choice with elastic demand." International Journal of Industrial Organization 19, no. 9 (November 2001): 1441–55. http://dx.doi.org/10.1016/s0167-7187(99)00053-3.

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42

Zetina, Carlos Armando, Ivan Contreras, and Jean-François Cordeau. "Profit-oriented fixed-charge network design with elastic demand." Transportation Research Part B: Methodological 127 (September 2019): 1–19. http://dx.doi.org/10.1016/j.trb.2019.06.004.

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43

Canca, David, Encarnación Algaba, Eva Barrena, and Alejandro Zarzo. "Railway Rapid Transit Timetables with Variable and Elastic Demand." Procedia - Social and Behavioral Sciences 111 (February 2014): 538–48. http://dx.doi.org/10.1016/j.sbspro.2014.01.087.

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44

Lederer, Phillip J. "Location-Price Competition with Delivered Pricing and Elastic Demand." Networks and Spatial Economics 20, no. 2 (November 27, 2019): 449–77. http://dx.doi.org/10.1007/s11067-019-09484-3.

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45

Xu, Xiangdong, and Anthony Chen. "C-logit stochastic user equilibrium model with elastic demand." Transportation Planning and Technology 36, no. 5 (July 2013): 463–78. http://dx.doi.org/10.1080/03081060.2013.818275.

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46

Ibeas, Angel, Borja Alonso, Luigi dell’Olio, and Jose Luis Moura. "Bus Size and Headways Optimization Model Considering Elastic Demand." Journal of Transportation Engineering 140, no. 4 (April 2014): 04013021. http://dx.doi.org/10.1061/(asce)te.1943-5436.0000641.

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47

Chien, Steven I.-Jy, and Lazar N. Spasovic. "Optimization of grid bus transit systems with elastic demand." Journal of Advanced Transportation 36, no. 1 (September 2002): 63–91. http://dx.doi.org/10.1002/atr.5670360105.

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48

Urbiztondo, Santiago. "Investment without regulatory commitment: The case of elastic demand." Journal of Regulatory Economics 6, no. 1 (February 1994): 87–96. http://dx.doi.org/10.1007/bf01065392.

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49

Rasmusen, Eric. "A simple model of product quality with elastic demand." Economics Letters 29, no. 4 (January 1989): 281–83. http://dx.doi.org/10.1016/0165-1765(89)90201-2.

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50

Boonsaeng, Tullaya, Stanley M. Fletcher, and Carlos E. Carpio. "European Union Import Demand for In-Shell Peanuts." Journal of Agricultural and Applied Economics 40, no. 3 (December 2008): 941–51. http://dx.doi.org/10.1017/s1074070800002431.

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This paper analyzes the European Union (EU) import demand for in-shell peanuts from three sources: the United States, China, and the rest of the world. We find that peanuts from different sources are differentiated by EU consumers. The expenditure elasticity is elastic for U.S. in-shell peanuts, which is associated with their higher quality. The conditional own price elasticities are more elastic for U.S. and Chinese in-shell peanuts. These findings have at least two implications. First, U.S. producers and exporters should direct efforts to ensure that in-shell peanuts exported to the EU are of the best possible quality, and, second, promotion efforts should stress the quality of U.S peanuts as an advertising tool.
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