Grid-Based Market Optimization System for Store Site Selection
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Solution Overview
Problem
Conventional site selection methods for real estate planning, relying on classical optimization techniques like Monte Carlo methods, are inefficient in generating and ranking market plans due to the large number of variables and constraints involved, making it difficult to optimize store site selection and format arrangements across a geographic area.
Innovation Solution
A market optimization system that integrates Monte Carlo simulation and genetic algorithms using grid-based computation and scoring to solve multi-constraint optimization problems, considering geographic, demographic, and competitor data to predict optimal store locations and formats, and includes features like store remodeling and supply chain optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional Monte Carlo methods are used for site selection optimization, then the method is simple to implement, but the computational efficiency is poor and cannot handle large numbers of variables and constraints
Solution Approach 1:
The patent segments the market optimization problem into discrete grid cells that can be independently evaluated and combined. Each cell represents a potential store location with specific attributes, allowing the system to divide the complex continuous optimization problem into manageable discrete units that can be processed efficiently
Solution Approach 2:
The system dynamically adjusts the evaluation criteria and constraints based on the specific market conditions and organizational goals. The optimization process adapts to different scenarios by modifying weightings and parameters, enabling efficient handling of varying numbers of variables and constraints without requiring complete method redesign
2Measurement precision
If multi-constraint optimization techniques such as genetic algorithms are used, then large populations of possible solutions can be evaluated, but the computational complexity becomes infeasible when the search space is large
Solution Approach 1:
By segmenting the search space into grid cells with predefined attributes and constraints, the system reduces the complexity of evaluating each potential solution. Instead of evaluating entire market plans simultaneously, the system evaluates individual cells and their combinations, making the optimization process computationally feasible
Solution Approach 2:
The patent applies local quality by assigning specific attributes and constraints to individual grid cells based on their location characteristics. Each cell has tailored feasibility criteria and format options, allowing the system to evaluate solutions with appropriate local constraints without requiring complex global constraint management
3Ease of operation
If conventional site selection methods are used, then the analysis can focus on individual store locations, but the system cannot efficiently optimize the overall arrangement of stores across a geographic area
Solution Approach 1:
The grid-based system serves multiple functions: it can evaluate individual store locations, optimize overall market arrangements, analyze competitor impacts, and assess format combinations. The same grid cell structure and evaluation framework are used across all these different optimization scenarios, providing a universal platform that handles both simple and complex planning tasks
Data Source
AI summary
Systems and methods for ranking or optimizing market configurations are disclosed. Optimized configurations of stores and formats in a market bounded within a geographic area are produced. The geographic area is divided into cells, and the cell attributes are used to determine which store formats can be present. Market configurations for each cell are provided to forecasters for determining of a first fitness criteria. Market configurations are filtered based on the first fitness criteria. Impacts between market configurations are calculated. A genetic algorithm produces a plurality of market solutions. In embodiments, optimization tasks are performed in parallel.