Algorithm for Two-Phase Facility Planning via Balanced Clustering and Integer Programming

TL;DR

This paper introduces a two-phase facility planning method combining balanced clustering with integer programming to efficiently determine facility locations and optimize transportation, demonstrating near-optimal solutions for complex planning scenarios.

Contribution

It proposes a novel two-phase approach integrating balanced clustering and integer programming for facility planning with static facilities and added waypoints.

Findings

The method achieves near-optimal solutions in facility planning scenarios.

Balanced clustering effectively groups facilities with equal cardinality.

Integer programming optimizes transportation in the second phase.

Abstract

We present a solution for a two-phase facility planning scenario where in the first phase, there is some flexibility in determining where the locations of facilities (or sources) should fall. And in the second phase, new waypoints (or sinks) are added, but the location of the facilities are static. This solution applies the use of balanced clustering - using a modified K-Means approach, ensuring the cardinality of each group to be equal. Subsequently, it is followed by an integer programming solution, to solve the Hitchcock Transportation Problem. We show that the final solution can justifiably approximate the near optimal solution, and be a successful guide for facility planning in this specific scenario.