Hierarchical clustering framework for facility location selection with practical constraints

Abstract

Facility location selection plays a critical role in the planning of logistics networks. It selects the addresses of facility nodes from a candidate set of locations to optimise multiple targets such as transportation efficiency and economic cost considering the practical constraints of the real world. Thus, it is often formulated as a combinational optimisation problem, which is solved by either mixed integer programing algorithms or heuristic methods. However, these approaches are limited by several issues such as a high computational cost and weak generalisation flexibility. In this work, a novel hierarchical clustering framework is proposed for facility location selection, which can flexibly support a wide variety of optimisation targets and the combinations of multiple practical constraints that are vital in the real logistics scenarios. Beyond the original hierarchical clustering algorithm, it incorporates a looking-forward mechanism that alleviates the 'greedy trap' by utilising global information. These advantages enable the proposed method to generate reliable solutions with high time efficiency. As demonstrated by the experimental results on real JD Logistics data, the proposed method outperforms the widely adopted GGA and VNS algorithms. It also has a much lower computation cost compared to the SCIP solver, while the quality of solutions are within an acceptable range. K E Y W O R D S intelligent transportation systems, learning (artificial intelligence), optimisation, pattern clusteringThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

AI and core electoral processes: Mapping the horizons

“…For usage in polling booth determination, the locations could be locations of voters. Figure byLin et al (2021).distance could still leave some voters with a significant distance to travel, to reach their polling booth. Recent advances in fair clustering (P and Abraham 2020; Jung, Kannan, and Lutz 2020; Abbasi, Bhaskara, and Venkatasubramanian 2021; Stepanov 2022) consider ensuring that no objects (voters) are left too disadvantaged in terms of distance to their cluster representative (polling booth).…”