Computerized systems and methods for optimizing building construction

A system for constructing a building that comprises computationally optimizing a plurality of development options, the system comprising:

• a processor, a user interface, a database, a location module, a project module, a predesign module, a computational optimization engine, a revenue module, a cost module, and a financial module, each of which is stored on a non-transitory computer readable storage medium, is operably connected to the processor, and has instructions for execution on the processor;
• wherein,
• the location module is configured with instructions for executing a selection of a lot of land to obtain data from the database relevant to the lot;
• the project module is configured with instructions for executing an identification of the plurality of development options for the lot;
• the project module configured with instructions for executing
• an extracting of zoning data from the database for the lot, the zoning data including zoning restrictions;
• and,
• an identifying of each development option data structure, Di, in the plurality of development options;
• the predesign module is configured with instructions for executing an assembling of a construction model, CMi, for each Di with the predesign module, wherein the assembling comprises architecting a development scheme substructure of data, (Dx)i, for each Di, the architecting including
• creating a list of restriction parameters obtained from the database for each Di, each restriction parameter in the list used in defining a respective (Dx)i;
• creating a series of shapes for each Di, the series of shapes composing a respective shape data structure, Si, where a substructure of data, (Sx)i, of the respective shape data structure, Si, defines the shape corresponding to the respective (Dx)i;
• creating a series of floor plans for each Si, the series of floor plans composing a respective floor plan data structure, FPi, where a substructure of data, (FPx)i, of the respective floor plan data structure, FPi, defines the floor plan corresponding to the respective (Dx)i;
• creating a series of building structures for each Di, the series of building structures composing a respective building structure data structure, BSi, where a substructure of data, (BSx)i, of the respective building structure data structure, BSi, defines the building structure corresponding to the respective (Dx)i;
• and,
• creating a series of construction material and labor lists for each BSi, the series of construction materials and labor lists composing a respective construction materials and labor list data structure, MLi, where a substructure of data, (MLx)i, of the respective materials and labor list data structure, MLi, defines the construction material and labor list corresponding to the respective (Dx)i;
• wherein, the CMi is a compilation of the Si, the FPi, the BSi, and the MLi;
• i is an integer ranging from 1 to I, where I consists of the number of development options in the plurality of development options;
• and, x is an integer ranging from 10 to X, where X consists of a substantial number of development schemes corresponding to the respective Di;
• the computational optimization engine is configured with instructions for executing a transforming of the CMi into a respective optimization model, OMi, for the respective Di;
• wherein,
• the OMi is configured to include a series of functions having (i) a series of revenue-related parameters from the CMi generating a total revenue, Ri, over a time, Ti;
• and, (ii) a series of cost-related parameters from the CMi generating a total development cost, DCi, over the time, Ti;
• wherein the Ti ranges from 0 to tin months;
• wherein, the series of functions comprises:
• one or more constraint functions, (CFz)i, where z is an integer ranging from 1 to Z, Z including one or more zoning restrictions for Di;
• and,
• an objective function, OFi, Pi=Ri-DCi, where the Pi is a profit margin;
• and,
• the transforming comprises:
• establishing a defined revenue domain by executing instructions in the revenue module that set a relationship between the total revenue, Ri, and the series of revenue-related parameters from the construction model, CMi, for the respective Di;
• establishing a defined cost domain by executing instructions in the cost module that set a relationship between the total development cost, DCi, and, a series of cost-related parameters from the construction model, CMi, for the respective Di;
• and,
• maximizing the Pi by optimizing (i) the series of revenue-related parameters to identify an Ropti, the optimized Ri for the respective Di;
• and, (ii) the series of construction-cost-related parameters to identify a DCopti, the optimized DCi for the respective Di;
• wherein, the optimizing comprises:
• (i) assigning the OMi as a type of mathematical model, the assigning including determining whether the constraint functions, (CFz)i, are linear, non-linear, discrete, or a combination thereof;
• and, determining whether the objective function, OFi, is linear, non-linear, or discrete;
• (ii) matching an optimization technique to the type of mathematical model assigned;
• and, (iii) instructing the computational optimization engine to solve for the Ropti and the DCopti;
• wherein,
• the computational optimization engine is further configured with instructions for executing a calculating of a land valuation, LVi, with for the respective Di;
• wherein, the calculating comprises:
• establishing a minimum return value, MRV, with the financial module;
• where, the MRV is a measure of financial return defined by a select, return calculation function, RCF, which is a function of the Ropti, the DCopti, the LVi, and an RCPy;
• where, RCPy is a set of one or more return calculation parameters for the respective RCF, where y is the number of return calculation parameters in RCP and is an integer ranging from 0 to Y;
• selecting the RCF, the selecting including setting the RCPy for use in the RCF;
• determining a maximum land value, LVmaxi, for the respective Di, the determining including maximizing the LVi subject to the RCF (the Ropti, the DCopti, the LVi, the RCPy)≧the MRV;
• where, the LVmaxi is a maximum price to pay for the lot of land to generate the MRV;
• and,
• repeating the determining of the LVmaxi for each Di;
• and,
• the display device displays an interactive report comprising the LVmaxi for each Di;
• wherein the system is used to identify and construct the development option corresponding to the DCopti, the optimized DCi for the respective development option data structure Di.

A method for creating a system for constructing a building that comprises computationally optimizing a plurality of development options, the method comprising:

• assembling a computer system with a processor, a display device, a database, a location module, a project module, a predesign module, an computational optimization engine, a revenue module, a cost module, and a financial module, each of which is stored on a non-transitory computer readable storage medium, is operably connected to the processor, and has instructions for execution on the processor;
• wherein,
• configuring the location module with instructions for executing a selection of a lot of land from the database;
• configuring the project module with instructions for executing an identification of the plurality of development options for the lot;
• the project module configured with instructions for executing
• an extracting of zoning data from the database for the lot, the zoning data including zoning restrictions;
• and,
• an identifying of each development option data structure, Di, in the plurality of development options;
• configuring the predesign module with instructions for executing an assembling of a construction model, CMi, for each Di with the predesign module, wherein the assembling comprises architecting a development scheme substructure of data, (Dx)i, for each Di, the architecting comprising:
• creating a list of restriction parameters obtained from the database for each Di, each restriction parameter in the list used in defining a respective (Dx)i;
• creating a series of shapes for each Di, the series of shapes composing a respective shape data structure, Si, where a substructure of data, (Sx)i, of the respective shape data structure, Si, defines the shape corresponding to the respective (Dx)i;
• creating a series of floor plans for each Si, the series of floor plans composing a respective floor plan data structure, FPi, where a substructure of data, (FPx)i, of the respective floor plan data structure, FPi, defines the floor plan corresponding to the respective (Dx)i;
• creating a series of building structures for each Di, the series of building structures composing a respective building structure data structure, B Si, where a substructure of data, (BSx)i, of the respective building structure data structure, BSi, defines the building structure corresponding to the respective (Dx)i;
• and,
• creating a series of construction material and labor lists for each BSi, the series of construction materials and labor lists composing a respective construction materials and labor list data structure, MLi, where a substructure of data, (MLx)i, of the respective materials and labor list data structure, MLi, defines the construction material and labor list corresponding to the respective (Dx)i;
• wherein, CMi is a compilation of the Si, the FPi, the BSi, and the MLi;
• i is an integer ranging from 1 to I, where I consists of the number of development options in the plurality of development of options;
• and, x is an integer ranging from 10 to X, where X consists of a substantial number of development schemes corresponding to the respective Di;
• storing the construction model, CMi, in a construction model data structure;
• configuring the computational optimization engine with instructions for executing a transforming of the CMi into a respective optimization model, OMi, for the respective Di;
• wherein,
• the OMi is configured to include a series of functions having (i) a series of revenue-related parameters from the CMi generating a total revenue, Ri, over a time, Ti;
• and, (ii) a series of cost-related parameters from the CMi generating a total development cost, DCi, over the time, Ti;
• wherein the Ti ranges from 0 to tin months;
• wherein, the series of functions comprises:
• one or more constraint functions, (CFz)i, where z is an integer ranging from 1 to Z, Z including one or more zoning restrictions for the Di;
• and,
• an objective function, OFi, Pi=Ri-DCi, where the Pi is a profit margin;
• and,
• the transforming comprises:
• establishing, using the processor, a defined revenue domain by executing instructions in the revenue module that set a relationship between the total revenue, Ri, and the series of revenue-related parameters from the construction model, CMi, for the respective Di;
• establishing, using the processor, a defined cost domain by executing instructions in the cost module that set a relationship between the total development cost, DCi, and, a series of cost-related parameters from the construction model, CMi, for the respective Di;
• and,
• maximizing, using the processor, the Pi by optimizing (i) the series of revenue-related parameters to identify Ropti, the optimized Ri for the respective Di;
• and, (ii) the series of construction-cost-related parameters to identify DCopti, the optimized DCi for the respective Di;
• wherein, the optimizing comprises:
• (i) assigning the OMi as a type of mathematical model, the assigning including determining whether the constraint functions, (CFz)i, are linear, non-linear, discrete, or a combination thereof;
• and, determining whether the objective function, OFi, is linear, non-linear, or discrete;
• (ii) matching an optimization technique to the type of mathematical model assigned;
• and, (iii) instructing the computational optimization engine to solve for Ropti and DCopti;
• wherein,
• the configuring of the computational optimization engine further comprises a configuring of the computational optimization engine with instructions for executing a calculating of a land valuation, LVi, with for the respective Di;
• wherein, the calculating comprises:
• establishing, using the processor, a minimum return value, MRV, with the financial module;
• where, the MRV is a measure of financial return defined by a select, return calculation function, RCF, which is a function of Ropti, DCopti, LVi, and RCPy;
• where, RCPy is a set of one or more return calculation parameters for the respective RCF, where y is the number of return calculation parameters in RCP and is an integer ranging from 0 to Y;
• selecting, using the processor, the RCF, the selecting including setting the RCPy for use in the RCF;
• determining, using the processor, a maximum land value, LVmaxi, for the respective Di, the determining including maximizing the LVi subject to the RCF (the Ropti, the DCopti, the LVi, the RCPy)≧the MRV;
• where, the LVmaxi is a maximum price to pay for the lot of land to generate the MRV;
• and,
• repeating, using the processor, the determining of the LVmaxi for each Di;
• storing the optimization model, OMi, in a optimization model data structure;
• and,
• providing an interactive report on the display device operable to display the LVmaxi for each Di;
• and
• detecting user interaction with the interactive report generated on the display device and, based on the detected user interaction, generating a second interactive report.
• wherein, the system is created for identifying and constructing the development option corresponding to the DCopti, the optimized DCi for the respective development option data structure Di.