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Journal articles on the topic 'Dependent demand'

Author: Grafiati

Published: 28 June 2021

Last updated: 17 February 2022

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1

Drezner, Zvi, and George O. Wesolowsky. "Allocation of demand when cost is demand-dependent." Computers & Operations Research 26, no. 1 (January 1999): 1–15. http://dx.doi.org/10.1016/s0305-0548(98)00052-5.

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2

Zhao, Yingxue, Tsan-Ming Choi, T. C. E. Cheng, Suresh P. Sethi, and Shouyang Wang. "Buyback contracts with price-dependent demands: Effects of demand uncertainty." European Journal of Operational Research 239, no. 3 (December 2014): 663–73. http://dx.doi.org/10.1016/j.ejor.2014.06.008.

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3

Walski, Tom, Devin Blakley, Matthew Evans, and Brian Whitman. "Verifying Pressure Dependent Demand Modeling." Procedia Engineering 186 (2017): 364–71. http://dx.doi.org/10.1016/j.proeng.2017.03.230.

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4

Mishra, N., SP Mishra, Srichandan Mishra, J. Panda, and UK Misra. "INVENTORY MODEL OF DETERIORATING ITEMS FOR LINEAR HOLDING COST WITH TIME DEPENDENT DEMAND." Mathematical Journal of Interdisciplinary Sciences 4, no. 1 (September 1, 2015): 29–36. http://dx.doi.org/10.15415/mjis.2015.41004.

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5

Janda, K., J. Mikolášek, and M. Netuka. "Complete almost ideal demand system approach to the Czech alcohol demand." Agricultural Economics (Zemědělská ekonomika) 56, No. 9 (September 21, 2010): 421–34. http://dx.doi.org/10.17221/117/2009-agricecon.

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Tax interventions into alcohol beverages market are an important and recently discussed tool of the Czech fiscal policy. The impact of any such measure would be strongly dependent on the microeconomic behavior of the consumers. The aim of this paper is to provide a reliable set of income, own-price, and cross-price elasticities of demand for the key alcohol beverages based on the Almost Ideal Demand System model applied on the most relevant Czech data set of the Household Budget Statistics.

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6

Verbas, I. Ömer, Hani S. Mahmassani, and Kuilin Zhang. "Time-Dependent Origin–Destination Demand Estimation." Transportation Research Record: Journal of the Transportation Research Board 2263, no. 1 (January 2011): 45–56. http://dx.doi.org/10.3141/2263-06.

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7

Gershwin, Stanley B., Bariş Tan, and Michael H. Veatch. "Production control with backlog-dependent demand." IIE Transactions 41, no. 6 (April 6, 2009): 511–23. http://dx.doi.org/10.1080/07408170801975040.

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8

Breitmoser, Yves, and Jonathan H. W. Tan. "Reference dependent altruism in demand bargaining." Journal of Economic Behavior & Organization 92 (August 2013): 127–40. http://dx.doi.org/10.1016/j.jebo.2013.06.001.

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9

Baker, RC, and Timothy L. Urban. "Single-period inventory dependent demand models." Omega 16, no. 6 (January 1988): 605–7. http://dx.doi.org/10.1016/0305-0483(88)90034-5.

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10

Pratama, Y. N. A., M. Darmawan, R. D. Astanti, T. J. Ai, and D. C. Gong. "Inventory Policy for Dependent Demand Where Parent Demand Has Decreasing Pattern." International Journal of Industrial Engineering and Engineering Management 1, no. 1 (June 30, 2019): 17. http://dx.doi.org/10.24002/ijieem.v1i1.2293.

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When a product reaches its maturity in its life cycle, some innovations have to be put in that product in order to lengthen its life cycle. Otherwise, that product will be perceived as obsolete. It might affect the demand of that product i.e. the demand become decreasing. Based on the observation that we conducted over two smart phone brands, the phenomena that the demand has declining pattern really happened in the real situation. In addition, the observation shows that the product life cycle is getting shorter. This implies that the manufacturer has to deal with decreasing demand more often. A case study is presented in this paper, in which manufacturer experienced final product with decreasing demand pattern. Some lot sizing techniques, such as Lot for Lot, Silver Meal 1, Silver Meal 2, Least Unit Cost, Part Period Balancing, and Incremental, are tested to solve the inventory policy for both final product (parent) and its components (child). It is concluded that a company should not consider only one component or one level whenever deciding the inventory policy, i.e. production lot size. It is shown by the case study that the best lot sizing technique for a particular parent of product whenever the company only consider the parent is different with the best lot sizing technique whenever the company consider the parent and its child. For the case presented, it is shown that the smallest total cost of parent and child is most likely occurred whenever Silver Meal 2 lot sizing technique is applied in the parent with decreasing demand pattern.

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11

Datta, T. K., K. Paul, and A. K. Pal. "Demand promotion by upgradation under stock-dependent demand situation – a model." International Journal of Production Economics 55, no. 1 (June 1998): 31–38. http://dx.doi.org/10.1016/s0925-5273(98)00033-4.

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12

Nockowska-Rosiak, Magdalena, Piotr Hachuła, and Ewa Schmeidel. "Stability of equilibrium points of demand-inventory model with stock-dependent demand." Journal of Difference Equations and Applications 22, no. 10 (July 11, 2016): 1490–500. http://dx.doi.org/10.1080/10236198.2016.1208188.

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13

Hachuła, Piotr, Magdalena Nockowska-Rosiak, and Ewa Schmeidel. "Neimark-Sacker Bifurcation in Demand-Inventory Model with Stock-Level-Dependent Demand." Discrete Dynamics in Nature and Society 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8162865.

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An analysis of dynamics of demand-inventory model with stock-level-dependent demand formulated as a three-dimensional system of difference equations with four parameters is considered. By reducing the model to the planar system with five parameters, an analysis of one-parameter bifurcation of equilibrium points is presented. By the analytical method, we prove that nondegeneracy conditions for the existence of Neimark-Sacker bifurcation for the planar system are fulfilled. To check the sign of the first Lyapunov coefficient of Neimark-Sacker bifurcation, we use numerical simulations. We give phase portraits of the planar system to confirm the previous analytical results and show new interesting complex dynamical behaviours emerging in it. Finally, the economical interpretation of the system is given.

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14

Polatoglu, Hakan, and Izzet Sahin. "Optimal procurement policies under price-dependent demand." International Journal of Production Economics 65, no. 2 (April 2000): 141–71. http://dx.doi.org/10.1016/s0925-5273(98)00240-0.

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15

Drezner, Zvi, and G. O. Wesolowsky. "Facility location when demand is time dependent." Naval Research Logistics 38, no. 5 (October 1991): 763–77. http://dx.doi.org/10.1002/1520-6750(199110)38:5<763::aid-nav3220380510>3.0.co;2-a.

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16

Walski, Thomas, Michael Havard, Bruce Yankelitis, Jon Youells, and Brian Whitman. "Pressure‐dependent demand under pressure‐deficient conditions." AWWA Water Science 1, no. 2 (March 2019): e1135. http://dx.doi.org/10.1002/aws2.1135.

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17

Skouri, K. "An EOQ model with backlog-dependent demand." Operational Research 18, no. 2 (December 7, 2016): 561–74. http://dx.doi.org/10.1007/s12351-016-0279-0.

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18

Gu, Yiquan, and Tobias Wenzel. "Transparency, price-dependent demand and product variety." Economics Letters 110, no. 3 (March 2011): 216–19. http://dx.doi.org/10.1016/j.econlet.2010.11.041.

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19

Aloqeili, Marwan. "Characterizing demand functions with price dependent income." Mathematics and Financial Economics 8, no. 2 (February 15, 2013): 135–51. http://dx.doi.org/10.1007/s11579-013-0098-5.

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20

Dang, Quang-Vinh, Won-Young Yun, and Herbert Kopfer. "Positioning empty containers under dependent demand process." Computers & Industrial Engineering 62, no. 3 (April 2012): 708–15. http://dx.doi.org/10.1016/j.cie.2011.11.021.

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21

Li, Hui-Chieh. "Optimal delivery strategies considering carbon emissions, time-dependent demands and demand–supply interactions." European Journal of Operational Research 241, no. 3 (March 2015): 739–48. http://dx.doi.org/10.1016/j.ejor.2014.09.022.

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22

Rojas, Fernando, and Victor Leiva. "Inventory management in food companies with statistically dependent demand." Academia Revista Latinoamericana de Administración 29, no. 4 (November 7, 2016): 450–85. http://dx.doi.org/10.1108/arla-12-2015-0336.

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Purpose The objective of this paper is to propose a methodology based on random demand inventory models and dependence structures for a set of raw materials, referred to as “components”, used by food services that produce food rations referred to as “menus”. Design/methodology/approach The contribution margins of food services that produce menus are optimised using random dependent demand inventory models. The statistical dependence between the demand for components and/or menus is incorporated into the model through the multivariate Gaussian (or normal) distribution. The contribution margins are optimised by using probabilistic inventory models for each component and stochastic programming with a differential evolution algorithm. Findings When compared to the non-optimised system previously used by the company, the (average) expected contribution margin increases by 18.32 per cent when using a continuous review inventory model for groceries and uniperiodic models for perishable components (optimised system). Research limitations/implications The multivariate modeling can be improved by using (a) other non-Gaussian (marginal) univariate probability distributions, by means of the copula method that considers more complex statistical dependence structures; (b) time-dependence, through autoregressive time-series structures and moving average; (c) random modelling of lead-time; and (d) demands for components with values equal to zero using zero-inflated or adjusted probability distribution. Practical implications Professional management of the supply chain allows the users to register data concerning component identification, demand, and stock levels to subsequently be used with the proposed methodology, which must be implemented computationally. Originality/value The proposed multivariate methodology allows it to describe demand dependence structures through inventory models applicable to components used to produce menus in food services.

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23

Zhang, Z., S. Li, and J. Zhou. "Estimate Load-dependent Service Demand for Modern CPU." Information Technology Journal 12, no. 4 (April 1, 2013): 632–39. http://dx.doi.org/10.3923/itj.2013.632.639.

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24

Kim, Eungab. "Stochastic vendor managed replenishment with demand dependent shipment." European Journal of Operational Research 152, no. 3 (February 2004): 723–44. http://dx.doi.org/10.1016/s0377-2217(02)00698-7.

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25

Chierici, Alessandro, Roberto Cordone, and Roberto Maja. "The demand-dependent optimization of regular train timetables." Electronic Notes in Discrete Mathematics 17 (October 2004): 99–104. http://dx.doi.org/10.1016/j.endm.2004.03.017.

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26

Simonov, Andrey, Jean-Pierre Dubé, Günter Hitsch, and Peter Rossi. "State-Dependent Demand Estimation with Initial Conditions Correction." Journal of Marketing Research 57, no. 5 (August 14, 2020): 789–809. http://dx.doi.org/10.1177/0022243720941529.

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The authors analyze the initial conditions bias in the estimation of brand choice models with structural state dependence. Using a combination of Monte Carlo simulations and empirical case studies of shopping panels, they show that popular, simple solutions that misspecify the initial conditions are likely to lead to bias even in relatively long panel data sets. The magnitude of the bias in the state dependence parameter can be as large as a factor of 2–2.5. The authors propose a solution to the initial conditions problem that samples the initial states as auxiliary variables in a Markov chain Monte Carlo procedure. The approach assumes that the joint distribution of prices and consumer choices is in equilibrium, which is plausible for the mature consumer packaged goods products commonly used in empirical applications. In Monte Carlo simulations, the approach recovers the true parameter values even in relatively short panels. Finally, the authors propose a diagnostic tool that uses common, biased approaches to bound the values of the state dependence and construct a computationally light test for state dependence.

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27

Buetefisch, Cathrin M., Kate Pirog Revill, Linda Shuster, Benjamin Hines, and Michael Parsons. "Motor demand-dependent activation of ipsilateral motor cortex." Journal of Neurophysiology 112, no. 4 (August 15, 2014): 999–1009. http://dx.doi.org/10.1152/jn.00110.2014.

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The role of ipsilateral primary motor cortex (M1) in hand motor control during complex task performance remains controversial. Bilateral M1 activation is inconsistently observed in functional (f)MRI studies of unilateral hand performance. Two factors limit the interpretation of these data. As the motor tasks differ qualitatively in these studies, it is conceivable that M1 contributions differ with the demand on skillfulness. Second, most studies lack the verification of a strictly unilateral execution of the motor task during the acquisition of imaging data. Here, we use fMRI to determine whether ipsilateral M1 activity depends on the demand for precision in a pointing task where precision varied quantitatively while movement trajectories remained equal. Thirteen healthy participants used an MRI-compatible joystick to point to targets of four different sizes in a block design. A clustered acquisition technique allowed simultaneous fMRI/EMG data collection and confirmed that movements were strictly unilateral. Accuracy of performance increased with target size. Overall, the pointing task revealed activation in contralateral and ipsilateral M1, extending into contralateral somatosensory and parietal areas. Target size-dependent activation differences were found in ipsilateral M1 extending into the temporal/parietal junction, where activation increased with increasing demand on accuracy. The results suggest that ipsilateral M1 is active during the execution of a unilateral motor task and that its activity is modulated by the demand on precision.

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28

Fei, Wenan, and Harris Schlesinger. "Precautionary Insurance Demand With State-Dependent Background Risk." Journal of Risk & Insurance 75, no. 1 (March 2008): 1–16. http://dx.doi.org/10.1111/j.1539-6975.2007.00245.x.

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29

Naidu, G. Mohan, and K. V. S. Sarma. "A Stochastic Inventory Model with Quality-Dependent Demand." Calcutta Statistical Association Bulletin 47, no. 3-4 (September 1997): 215–22. http://dx.doi.org/10.1177/0008068319970307.

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This paper deals with an inventory model in which the demand rate is influenced by the quality of the material received . The case considered is a situation in which the consumption rate is adjusted whenever the incoming material does not have the desired quality but still usable. This leads to uncertainity in the inventory cycle and may create unplanned shortages. The model takes into account differential prices of the material based on quality. The behaviour of the optimal order level and the optimum cost has been studied as a function of the probability with which good quality material can be received. Numerical illustrations are given in support of the theoretical results.

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30

Zhang, Jing Jiang, Teng Fei Nie, and Shao Fu Du. "Optimal emission-dependent production policy with stochastic demand." International Journal of Society Systems Science 3, no. 1/2 (2011): 21. http://dx.doi.org/10.1504/ijsss.2011.038931.

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31

Arcelus, F. J., T. P. M. Pakkala, and G. Srinivasan. "The Instant Obsolescence Problem With Price-Dependent Demand." INFOR: Information Systems and Operational Research 44, no. 4 (December 2006): 248–66. http://dx.doi.org/10.1080/03155986.2006.11732751.

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32

LAU, AMY HING-LING, and HON-SHIANG LAU. "The Newsboy Problem With Price-Dependent Demand Distribution." IIE Transactions 20, no. 2 (June 1988): 168–75. http://dx.doi.org/10.1080/07408178808966166.

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33

Sun, Zhengwei, Andrea C. Hupman, and Ali E. Abbas. "The value of information for price dependent demand." European Journal of Operational Research 288, no. 2 (January 2021): 511–22. http://dx.doi.org/10.1016/j.ejor.2020.05.057.

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34

Berman, Oded, and David Perry. "An EOQ model with state-dependent demand rate." European Journal of Operational Research 171, no. 1 (May 2006): 255–72. http://dx.doi.org/10.1016/j.ejor.2004.08.035.

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35

Önal, Mehmet, and Erinç Albey. "Economic lot sizing problem with inventory dependent demand." Optimization Letters 14, no. 8 (January 10, 2020): 2087–106. http://dx.doi.org/10.1007/s11590-020-01532-z.

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36

Okyay, H. K., F. Karaesmen, and S. Özekici. "Newsvendor models with dependent random supply and demand." Optimization Letters 8, no. 3 (February 17, 2013): 983–99. http://dx.doi.org/10.1007/s11590-013-0616-7.

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37

Yu, Ying, Ji Zhu, and Chenwei Wang. "A newsvendor model with fuzzy price-dependent demand." Applied Mathematical Modelling 37, no. 5 (March 2013): 2644–61. http://dx.doi.org/10.1016/j.apm.2012.06.008.

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38

ZHANG, ALEX X. "DEMAND FULFILLMENT RATES IN AN ASSEMBLETO- ORDER SYSTEM WITH MULTIPLE PRODUCTS AND DEPENDENT DEMANDS." Production and Operations Management 6, no. 3 (January 5, 2009): 309–24. http://dx.doi.org/10.1111/j.1937-5956.1997.tb00433.x.

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39

Tripathi, R. P. "EOQ Model with Time Dependent Demand Rate and Time Dependent Holding Cost Function." International Journal of Operations Research and Information Systems 2, no. 3 (July 2011): 79–92. http://dx.doi.org/10.4018/joris.2011070106.

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In this paper the authors consider the continuous deterministic, infinite horizon, single item inventory system within the setting of a retailer sector in which the demand rate for an item is time dependent. The parameter of the replenishment cost is kept constant, but the carrying cost per unit is allowed to vary. The optimal policies are found, and decision rules and classical EOQ model have been obtained by considering two different models. Numerical examples are given to illustrate the proposed models.

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40

Khara, Barun, Jayanta Kumar Dey, and Shyamal Kumar Mondal. "An inventory model under development cost-dependent imperfect production and reliability-dependent demand." Journal of Management Analytics 4, no. 3 (July 3, 2017): 258–75. http://dx.doi.org/10.1080/23270012.2017.1344939.

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41

Kim, Youngtae, and Muzaffer Uysal. "Time-Dependent Analysis for International Hotel Demand in Seoul." Tourism Economics 4, no. 3 (September 1998): 253–63. http://dx.doi.org/10.1177/135481669800400304.

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The primary objective of this study is to develop an empirical model that accounts for factors affecting the demand for international tourist hotels, and that enables demand estimation and projection in the context of the Seoul metropolitan area. International hotel demand for a metropolitan area such as Seoul is hypothesized as a function of the price of hotel rooms, the number of events such as international conventions, seminars and exhibitions; and total trade volume of the destination country. The dependent variable is the number of bed nights occupied by foreign guests in a given period. The study discusses a procedure for correctly estimating a time-series regression model. Consideration is given to the possibility that errors in the model are generated by a time-dependent process more than by a first-order autoregressive process. The findings of the study revealed that own price, the number of events and the trade volume directly affected demand. Some factors were proven to affect the demand indirectly through adjustment of price in the short run. Further the study revealed that demand is a function of the entire market mechanism, which is the inter-relationship of demand and price among different market segments.

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42

Derbel, Mouna, Wafik Hachicha, and Awad M. Aljuaid. "Sensitivity Analysis of the Optimal Inventory-Pooling Strategies According to Multivariate Demand Dependence." Symmetry 13, no. 2 (February 17, 2021): 328. http://dx.doi.org/10.3390/sym13020328.

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Inventory-pooling (IP) is an effective tool to mitigate demand uncertainty and variability, to reduce operational costs, and consequently to increase the profit. The major assumptions of the previous works in literature on IP include the following: (1) Independents demand, which satisfy the typical normal independent and identically distributed (iid) random variables; (2) dependents (correlated) symmetric demands, which follows to a multivariate normal distribution. The effect of the dependent asymmetric demand is not yet studied. The aim of this paper is to consider this more realistic case. Indeed, the contribution of this paper is twofold. Firstly, it analyzes both the sensitivity of dependence structure and the levels of skewness of distributions on IP policies in terms of optimal total cost and demand satisfaction constraint. Secondly, both symmetric and asymmetric demand distributions are modeled using various beta distribution and the dependance between demands are modeled using various copulas. A newsvendor problem inspired by the literature, with two decentralized locations and two centralized locations, is considered the empirical study. For each dependance situation, three IP models are considered: inventory centralization, regular transshipments, and independent systems. The results suggest divergences in the decisions in about 9% of cases. Bad choice of marginal distributions given that the copula is appropriate can lead to divergences that vary between 2.2% and 4%, depending on whether the demand distributions are symmetric or asymmetric.

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43

Amirgholy, Mahyar, and Eric J. Gonzales. "Demand responsive transit systems with time-dependent demand: User equilibrium, system optimum, and management strategy." Transportation Research Part B: Methodological 92 (October 2016): 234–52. http://dx.doi.org/10.1016/j.trb.2015.11.006.

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44

Khmelnitsky, E., and G. Singer. "A Stochastic Inventory Control Problem with Reputation-Dependent Demand." IFAC Proceedings Volumes 42, no. 4 (2009): 1689–93. http://dx.doi.org/10.3182/20090603-3-ru-2001.0131.

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45

Lukinskiy, Valery, and Vladislav Lukinskiy. "Evaluation of Stock Management Strategies Reliability at Dependent Demand." Procedia Engineering 178 (2017): 53–56. http://dx.doi.org/10.1016/j.proeng.2017.01.060.

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46

Sajadieh, Mohsen S., Anders Thorstenson, and Mohammad R. Akbari Jokar. "An integrated vendor–buyer model with stock-dependent demand." Transportation Research Part E: Logistics and Transportation Review 46, no. 6 (November 2010): 963–74. http://dx.doi.org/10.1016/j.tre.2010.01.007.

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47

Bruner, Natalie R., and Matthew W. Johnson. "Demand curves for hypothetical cocaine in cocaine-dependent individuals." Psychopharmacology 231, no. 5 (November 12, 2013): 889–97. http://dx.doi.org/10.1007/s00213-013-3312-5.

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48

Hua, G. W., T. C. E. Cheng, Y. Zhang, and J. L. Zhang. "Carbon-Constrained Perishable Inventory Management with Freshness-Dependent Demand." International Journal of Simulation Modelling 15, no. 3 (September 15, 2016): 542–52. http://dx.doi.org/10.2507/ijsimm15(3)co12.

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49

Stavrulaki, Euthemia. "Inventory decisions for substitutable products with stock-dependent demand." International Journal of Production Economics 129, no. 1 (January 2011): 65–78. http://dx.doi.org/10.1016/j.ijpe.2010.09.002.

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50

Devangan, Lokendra, R. K. Amit, Peeyush Mehta, Sanjeev Swami, and Kripa Shanker. "Individually rational buyback contracts with inventory level dependent demand." International Journal of Production Economics 142, no. 2 (April 2013): 381–87. http://dx.doi.org/10.1016/j.ijpe.2012.12.014.

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