This invention relates to a computer-implemented land planning system and method, such as that designed to generate at least one conceptual fit solution to a user-defined land development problem. The invention is equally applicable to the planning and development of single and multi-pad commercial, mixed use, and residential land sites.
The process used today by professional real estate developers, corporations, government entities and others to assess land for engineering feasibility, cost of developing, and investment purposes is time consuming, inaccurate, and expensive. Unfortunately, the current process is getting even more complex and expensive due to added bureaucratic complications with land use zoning, environmental protection requirements, extended permitting processes, as well as the availability and escalating cost of land in desirable areas. This problem affects a broad spectrum of land users including, for example, real estate developers (office/industrial, commercial, retail, residential), corporations which own and use real estate (public/private), and government entities (Federal, State, County, City).
For each of the above users, assessing the feasibility of a land site for development typically involves a land development team including one or more architects, engineers, and land planners. Many of these team members are engaged to layout and plan the intended uses on the site being considered. This initial planning process can take from 2 days to four weeks, and usually results in a single schematic drawing with limited information (e.g., will the site support the building footprints or building lots and the necessary streets and/or parking lots?). At this point, based largely on intuition and a “gut feeling” about the project, the developer will choose to contract for additional planning and engineering to more accurately assess the feasibility of the plan and the budget. This process can take 2 weeks to 16 weeks and usually results in only one option that is based on the designer's experience but is not optimized in any respect. This information is then used to estimate a more accurate budget. Often times value engineering is required to bring the design back within the original budget. This process takes 2 weeks to 6 weeks. The final budget is not generally determined until the end of the planning process—some 3-4 months after initial consideration of the land site.
The above planning process often must occur before the property is purchased, and requires substantial investment in legal fees and earnest money to hold the property for an extended length of time.
After this 4 week to 28-week process (average 16 weeks) and considerable expense and risk of lost opportunity, the developer must assess the risk of purchasing and developing the property based on one un-optimized design option. Unfortunately, the process outlined above is complicated even further by miscommunication and disconnect between the many groups involved, which often results in bad designs, bad budgets, disagreements, and bad projects.
The present applicant recognized that the land development industry needs a major paradigm shift, which is now possible through advances in mathematical modeling and computing hardware. One primary goal of the present invention is to fix the problems outlined above through a virtual engineering system that can produce many optimized alternatives for land development—including the planning, engineering, and budgeting of each potential solution. This computing process is generally achieved in a maximum 24-hour period.
Heuristic Strategy
The speed and effectiveness of the present invention is advanced using a heuristic mathematical optimization approach, such as evolutionary algorithms (with possible instantiations, such as genetic algorithms, evolution strategies, evolutionary programming, genetic programming, and combinations of the above and their components). For certain subtasks, mathematical programming approaches such as linear programming, mixed-integer programming, and branch-and-bound, are utilized as well.
Concisely stated, an evolutionary algorithm (or “EA”) is a programming technique that mimics biological evolution as a problem-solving strategy. Given a specific problem to solve, the input to the EA is a set of potential solutions to that problem, encoded in some fashion, and a metric called a fitness function that allows each candidate to be quantitatively evaluated. These candidates may be solutions already known to work, with the aim of the EA being to improve them, but more often they are generated at random.
From these initial candidate solutions, by a process called reproduction, copies are being made in such a way that better candidate solutions (according to their fitness measure) receive more copies on average while worse candidate solutions receive less copies on average. Alternatively, reproduction might not be fitness-based, but instead may select solutions completely at random from the parent population. These copies generated by reproduction enter the next generation of the algorithm, and are then subject to randomized modification processes known as mutation and crossover (also called recombination). After mutation and crossover (together often called “variation operators”), the newly created solutions are quantitatively evaluated again to determine their fitness values. After this step of fitness determination, a selection step can be added which—either deterministically or according to a fitness-based randomized process—selects better solutions from the offspring population to survive while discarding worse solutions. This selection step can be applied to offspring only, or to the union of parents and offspring. Afterwards, the process repeats. The expectation is that the average fitness of the population will increase each round, and so by repeating this process for hundreds or thousands of rounds, very good solutions to the problem can be discovered.