ON THE URBAN PLANNING OPTIMIZATION PROBLEMS VIA g-INVERSE GP METHOD.

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Title:	ON THE URBAN PLANNING OPTIMIZATION PROBLEMS VIA g-INVERSE GP METHOD.
Authors:	Nwachi, Christy C.¹ christynwachi@gmail.com, Amuji, Harrison O.² harrison.amuji@futo.edu.ng, Ugwuanyim, Geoffrey U.² geoffrey.ugwuanyim@luto.edu.ng, Iwu, Hycinth C.² hycinth.iwu@futo.edu.ng, Okeoma, Immaculata O.¹ ogechi.okeoma@futo.edu.ng, Okeke, Kenneth O.³ okekemkechukwu@fiito.edu.ng, Eke, Edward E.⁴ edwardeke280@gmail.com, Ezeanya, Nnaemeka C.⁵ nnaemeka.ezeanya@futo.edu.ng, Anya, Collins U.⁶ collins.anya@futo.edu.ng
Source:	Advances & Applications in Discrete Mathematics. Oct2025, Vol. 42 Issue 7, p643-655. 13p.
Subjects:	Urban planning, Geometric programming, Cost estimates, Cities & towns, Mathematical models, Mathematical optimization, Population forecasting
Abstract:	In this paper, we develop a g-inverse geometric programming model for urban planning optimization problems. The nature of this problem is complex because of the resulting rectangular exponent matrix with so many positive degrees of difficulty. This method has helped to solve such a complex problem and eliminate the use of condensation, which would require higher-order mathematics. From the solution to the modelled urban planning optimization problem, we were able to determine the optimal cost for urban planning for the five selected districts in Owerri to be 2.625 x 106. We also estimated the lower and upper bound populations of the districts in the next two years. Our model is robust and has demonstrated its capacity to handle such tasks of cost estimation and population estimation with less difficulty. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

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Abstract:	In this paper, we develop a g-inverse geometric programming model for urban planning optimization problems. The nature of this problem is complex because of the resulting rectangular exponent matrix with so many positive degrees of difficulty. This method has helped to solve such a complex problem and eliminate the use of condensation, which would require higher-order mathematics. From the solution to the modelled urban planning optimization problem, we were able to determine the optimal cost for urban planning for the five selected districts in Owerri to be 2.625 x 106. We also estimated the lower and upper bound populations of the districts in the next two years. Our model is robust and has demonstrated its capacity to handle such tasks of cost estimation and population estimation with less difficulty. [ABSTRACT FROM AUTHOR]
ISSN:	09741658
DOI:	10.17654/0974165825041