<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SSR</journal-id><journal-title-group><journal-title>Scientific and Social Research</journal-title></journal-title-group><issn>2661-4332</issn><eissn>2981-9946</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/ssr.v8i4.14836</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Multi-Objective Collaborative Optimization of Power Equipment Selection and Maintenance Path Considering Risk Priority and Spatiotemporal Penalties</title><url>https://artdesignp.com/journal/SSR/8/4/10.26689/ssr.v8i4.14836</url><author>ZhongZi’an,HuaKun,HuangXu,ChenShiyu,ChenRuiqi</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>8</volume><issue>4</issue><history><date date-type="pub"><published-time>2026-05-25</published-time></date></history><abstract>Aiming at the maintenance scheduling problem of power equipment such as Ring Main Units (RMUs) in distribution systems, this paper constructs a priority-based maintenance optimization model. The model integrates real-time risk indicators, historical maintenance records, and sudden extreme risk factors, determines equipment priority through a hierarchical scoring mechanism, and introduces soft time constraints on travel and maintenance time. Built in the form of a single-layer mixed-integer programming (MIP), the model’s objective function includes dual penalty terms for overtime and underload. The scheduling adaptability and response capability of the model in basic scenarios, updated history, and sudden risk situations are verified through three-stage simulation experiments.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Wang H, Pham H, 2006, Reliability and optimal maintenance. Springer, New York.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Jardine AKS, Lin D, Banjevic D, 2006, A Review on Machinery Diagnostics and Prognostics Implementing Condition-based Maintenance. Mechanical Systems and Signal Processing, 20(7): 1483–1510.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Tsang AHC, 1995, Condition-based Maintenance: Tools and Decision Making. Journal of Quality in Maintenance Engineering, 1(1): 4–18.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
