all necessities at least expense.
1.5.1 Future Research
The Research extended to deal with Inventory models to manage EOQ model. In this case, there is a connected assistance for stock expense which is represented in dollars as per undertaking data per time unit. That is the expense brought about for having the foreseen assignment or data prepared to be utilized in a feature of the administration. As clarified before, basically “storing” these foreseen tasks or data permits to keep forever their ideal level should have been prepared for use without waiting for “reorder” level, as there is no compelling reason to build up a boundary to decide the second where administration undertakings and data ought to be foreseen again in the administration cycle, ensuring the proper assistance arrangement to customer, improving quality, customization, speed or cost and to keep up adequate supply of crude materials and control investment and give proposal for future work.
1.6 Conclusions
EOQ Inventory model engages to keep a track on the nonappearance of substance due to imprudence and theft. There is a more prominent possibility of carelessness and stealing if inventory has not been done in the right way. Company Inventory model will be helpful if those concepts are implemented and it shows an indication of what stage of sales to count on. EOQ models are utilized to choose the ideal inventory policy when the demand is deterministic and the top-quality ordering or manufacturing amount are prompted by using Parameters of prices. These models are used to the Inventory part size that cut-off points Inventory extraordinary cost. Mathematical assessment and generation have shown that it uses the resources and even more gainfully achieves the most extraordinary advantages and can improve shopper reliability. In association’s Inventory the executive circumstance will be more apex. The main goal is to limit selling cost and process duration cost out of all business benefit and these were represented by using mathematical models. In this three-methodologies Brownian movement are established by Trapezoidal Rule. These results are showed in Brownian Path and found by the outside measure. Subsequently it is Fractals.
References
1. Makoena Sebatjane, Olufemi, Adetunji, Economic order quantity model for growing items with imperfect quality, Volume 6, 100088 (2019).
2. Mohamed Hassan Dhodi, The Effect of Information Technology on Inventory Management for the Manufacturing Companies in Mogadishu, European Journal of Logistics, Purchasing and Supply Chain Management, Vol.6 No.3, pp. 20-29, June (2018).
3. Zohreh Momeni and Amir Azizi, Current Order and Inventory Models in Manufacturing Environments, Feb 02, Paper Id.: IJMPERDFEB2018129 (2018).
4. Rakesh Kumar, Economic Order Quantity (EOQ) Model, Global Journal of Finance and Economic Management. ISSN 2249-3158 Volume 5, Number 1 pp. 1-5 (2016).
5. Naser Ghasemi and Behrouz Afshar Nadjafi, EOQ Models with Varying Holding Cost, Hindawi Publishing Corporation Journal of Industrial Mathematics Volume Article ID 743921, 7 pages (2013).
6. Ganesh Prasad Shukla and Prashant Kumar Jangde, Determination of Economic Order Quantity and Reorder Point Inventory Control Model for Xyz Retail Enterprises, ISSN (Print): 2393-8374, (Online): 2394-0697, Volume 4, Issue 12 (2017).
7. Jose L. Gonzalez and Daniel González, Analysis of an Economic Order Quantity and Reorder Point Inventory Control Model for Company XYZ, March 10, (2010).
8. Iyengar S.R.K., and Jain R.K.,Numerical Methods, ISBN (13) 978-81-224-2707-3, 2009.
9. Rudi Abdullah, Samsul Bahari Bahar, Asrianti Dja’wa, La Ode Dedi Abdullah Inventory Control Analysis Using Economic Order Quantity Method volume 436 2019.
10. A guide to Brownian motion and related stochastic processes Jim Pitman and Marc Yor, [v1] Tue, 27 Feb 01:36:17 UTC (111 KB), 2018.
11. Jörn Dunkel, Peter Hänggi, Relativistic Brownian motion, Physics Reports 471 (2009) 1–73.
12. Toshio Yanagida, Masahiro Ueda, Tsutomu Murata, Seiji Esaki a, Yoshiharu Ishii, Brownian motion, fluctuation and life, BioSystems 88 (2007) 228–242.
13. Luczkaa J., Talknerb P., Hanggi P., Diffusion of Brownian particles governed by fluctuating friction. Physica A 278 (2000) 18–31.
14. Julien Berestycki,ÉricBrunet, Simon. Harris, Piotr Miłos, Branching Brownian motion with absorption and the all-time minimum of branching Brownian motion with drift. Journal of Functional Analysis 273 (2017) 2107-2143.
15. Huerta-Cuellar G., Jiménez-López E., Campos-Cantón., Pisarchik, A.N., An approach to generate deterministic Brownian motion, Commun Nonlinear SciNumerSimular 19(2014) 2740-2746.
16. Camella Burja, Vasile Burja, Analysis Model for Inventory Management, Annals of the University of Petrosani, Economics, 10(1), 43-50, (2010).
17. Aref Gholami and Abolfaz Mirzazadeh, An inventory model with controllable lead time and ordering cost, log-normal-distributed demand, and gamma-distributed available capacity. Cogent Business & Management 5: 1469182(2018).
18. Mehmood Khan, Mohamad Y. Jaber, Maurice Bonney, An economic order quantity (EOQ) for items with imperfect quality and inspection. Int. J. Production Economics 133(2011)113-118.
19. Christopher J. Bishop and Yuval Peres, Fractals in Probability and Analysis, 2017.
20. Mandelbrot B. B. The Fractals Geometry of Nature, Freeman, San Francisco, CA, 1982.
1 *Corresponding author: [email protected]
2
Ill-Posed Resistivity Inverse Problems and its Application to Geoengineering Solutions
Satyendra Narayan*
Department of Applied Computing, Faculty of Applied Science and Technology, Sheridan Institute of Technology and Advanced Learning, Oakville, Ontario, Canada
Abstract
The most important physical properties to study ill-posed inverse problems in physical sciences are electrical conductivity, magnetic permeability, density, wave-velocity, elasticity parameters/modulus, and dielectric permittivity. This paper attempts electrical conductivity of the earth materials and describes some innovative approaches which have been used to solve ill-posed resistivity inverse problems encountered in mapping and monitoring geo-environmental problems.
The paper begins with an overview of the present state of knowledge about electrical resistivity methods for mapping and monitoring in-situ processes that cannot be accessed directly. The current study indicates that a generalized mathematical approach has not been developed to investigate the sensitivity of resistivity measurements to changes in resistivity at depth. Therefore, the paper also presents a generalized mathematical formulation for sensitivity analysis and describes sensitivity of resistivity measurements. Reciprocity and perturbation analysis form the basis for the mathematical formulation, which has been extended further towards introducing multi-dimensional resistivity inversion useful for mapping and monitoring in-situ processes. A generalized multi-dimensional mathematical technique is described herein for computing numerical response over the one-dimensional (1-D), two-dimensional (2-D) and three-dimensional (3-D) resistivity models excited by a three-dimensional (3-D) point source. These problems also described as 1-D/3-D, 2-D/3-D and 3-D/3-D inverse problems in the scientific literature.
Keywords: Resistivity inversion, resistivity inverse problems, sensitivity of resistivity measurements, multi dimensional resistivity inversion, in-situ resistivity monitoring,