Robust optimization model solving method for site selection problem

Abstract

The invention provides a robust optimization model solving method for a site selection problem, and belongs to the application technical field of the operational research and the optimization algorithm. The method comprises the following steps: firstly, establishing a robust convex optimization model for the site selection problem, and converting the robust convex optimization model into a corresponding parameter constraint model; then, converting the parameter constraint model into an approximate model, determining the number of the samples of the uncertainty set sample, distributing the constraint conditions to corresponding processes and constructing a weight matrix of the communication between the processes; and designing two distributed algorithms for no directional communication or directional communication according to the interprocess communication mode, and thereby obtaining the optimal result of the site selection problem through iterative solutions. The robust optimization model solving method for site selection problem can make more robust site selection decision under conditions of complex and uncertain demand composed of a large number of service objects, and benefitsfor which each application area produces good technical results due to optimized site selection, and minimizes the potential risks caused by improper site selection.

Description

technical field [0001] The invention belongs to the application technical field of operations research and optimization algorithms, and in particular relates to a robust optimization model solution method for site selection problems. Background technique [0002] The location problem is one of the classic problems in operations research. As an important long-term decision-making problem, site selection is widely used in production, life, and logistics, such as the site selection of factories, logistics centers, hospitals, and fire stations. Taking the site selection of a fire station as an example, in order to improve the fire rescue efficiency of the fire station, it is usually hoped that the fire station should be closer to the service objects with a higher probability of fire occurrence. However, the actual situation is that the probability of a fire in the service object is uncertain, and there may be other uncertain events in the distance from the fire station to the s...

AI Technical Summary

Problems solved by technology

[0005] The biggest challenge of using scene modeling to solve robust optimization problems is that obtaining a robust solution with sufficient accuracy requires generating a large number of constraints and thus requiring a long solution time

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