The Maximal Accessibility Equality Problem (MAEP), originally introduced by Jin et al., addresses capacity adjustments for minimizing inequality in accessibility. It takes into account the match ratio between supply and demand, along with intricate spatial interactions.
The optimization objective of MAEP aims to minimize inequality in facility accessibility, with a focus on reducing variance across geographic areas. This problem can be formulated as either a Nonlinear Programming (NLP) or a Quadratic Programming (QP) task.
The top input table contains three types of columns: demand size (e.g., population), distance columns to the existing facilities (n) and new facilities (k). The bottom input table contains two columns: the ID and the capacity columns for existing facilities.
The result table comprises three columns: Facility IDs and their assigned capacities under two scenarios, denoted by the 'All' and 'Fixed' columns: Global Optimization: New capacity is allocated to both existing and new facilities by adding the specified new capacity to the total capacity of existing facilities. Local Optimization: New capacity is exclusively assigned to new facilities based on the specified new capacity, with no impact on the capacities of existing facilities.
To solve the MAEP problem, this node utilizes the cvxopt package.
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Each row in this table represents a demand location (m). It consists of three types of columns: demand size (e.g., population), distance columns to the existing facilities (n) and new facilities (k).
This table provides information regarding the supply capacity of each existing facility. The values in the Facility ID column must exactly match the column names for facilities in the demand table.
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