Two-layer planning model and optimization algorithm for recycling bin layout and scheduling in urban residential areas
郭谦 1刘勇 1马良1
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作者信息
1. 上海理工大学管理学院,上海 200093
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摘要
针对城市居民区回收箱布局规划和路径优化问题,首先构建居民区回收箱数量与人口、回收频率、回收阈值的线性函数,并构建双层优化模型,回收总利润最大化作为上层目标,运输成本最小化作为下层目标.其次,为求解具有NP-hard特征的新模型,设计加入团体学习算子和自适应选择策略的人类学习优化算法,并与禁忌搜索算法嵌套构建混合人类学习算法(hybrid human learning optimization algorithm,HHLO).再次,采用不同规模算例,并将新算法与基本人类学习算法、遗传算法、自适应粒子群算法、红嘴蓝鹊算法进行对比分析,验证了模型的可行性和算法的有效性.最后,通过上海杨浦区某实例进行灵敏度分析,探讨回收箱容量、分时定价策略和分区定价策略对回收中心总利润与居民满意度的影响.
Abstract
In addressing the layout planning and path optimization problem of recycling bins in urban residential areas,this paper constructed a linear function to relate the number of recycling bins in residential areas to population,recycling frequen-cy,and recycling threshold.It developed a bi-level optimization model,with the upper-level objective of maximizing total re-cycling profit and the lower-level objective of minimizing transportation costs.To address the NP-hard nature of the model,it designed a human learning optimization algorithm incorporating group learning operators and adaptive selection strategies.This algorithm was combined with a tabu search algorithm to form the hybrid human learning optimization algorithm(HHLO).The new algorithm was compared with basic human learning algorithms,genetic algorithms,adaptive particle swarm algorithms and red-billed blue magpie optimization algorithms across various scale instances.The results validate the model's feasibility and the algorithm's effectiveness.A sensitivity analysis,using a case study in Shanghai's Yangpu District,examined how recy-cling bin capacity,time-based pricing strategies,and zone-based pricing strategies impacted the total profit of the recycling center and resident satisfaction.
关键词
回收箱布局/车辆调度/混合人类学习优化算法/双层规划
Key words
recycling bin layout/vehicle scheduling/hybrid human learning optimization/two-layer programming
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