发表机构
Hacettepe University; University at Buffalo (SUNY)（哈杰泰佩大学; 纽约州立大学布法罗分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究退化触发作业到达的调度与路径规划问题，针对无人机森林消防场景提出混合整数规划及动态约束生成混合模型，通过实验验证其性能并开放代码保障可复现性。
AI 中文摘要
我们定义了一个相互交织的调度与路径规划问题，其中新作业因现有作业的退化而出现。具体而言，一旦作业到达潜在作业位置，就会开始一个时间窗口，在此窗口内可满足该作业的需求。需求在时间窗口内会退化，一旦超过特定阈值，就会触发新作业的到达。每个作业位置固有地拥有初始默认奖励，而某个位置存在未处理作业会逐渐降低该默认值。整体目标是最大化剩余总奖励。该问题的潜在动机与谚语“及时一针省九针”相符，且该问题具有实际应用意义。我们聚焦于空中森林消防背景下的该问题：每个着火区域都有指定的行动窗口；延迟干预会导致火势蔓延，降低该区域的价值并使其扩散至相邻区域。我们开发了一个混合整数规划模型，用于最大化野火威胁区域的价值保留，以及一个基于动态约束生成的混合模型，以提升模型的可扩展性。我们通过计算实验和案例研究评估了模型的性能与实用性。此外，我们提供模型代码库的开放访问权限，以确保研究的可复现性并鼓励进一步研究。
英文摘要
We define an intertwined scheduling and routing problem where new jobs appear due to the degradation of the existing jobs. Specifically, once a job arrives at a potential job location, a time window begins during which the demand of the job can be fulfilled. The demand degrades within the time window, and once it surpasses a particular threshold, it triggers the arrival of new jobs. Each job location inherently possesses an initial default reward, and the presence of an unprocessed job at a location gradually reduces this default value. The overall objective is to maximize the total remaining reward. The underlying motivation of this problem aligns with the proverb ``a stitch in time saves nine," and the problem itself carries practical implications. We focus on the problem in the context of aerial forest firefighting. Each ignited area has a designated action window; delaying intervention causes the fire to grow, diminishing the area's value and causing it to spread to adjacent areas. We develop a mixed-integer programming model that maximizes value retention in wildfire-threatened regions, and a hybrid model based on dynamic constraint generation to enhance the scalability of the model. We evaluate the performance and practicality of our models through computational experiments and a case study. Additionally, we ensure the study's reproducibility and encourage further research by providing open access to the codebase of our model.
Journal refEuropean Journal of Operational Research (2025)
DOI：10.1016/j.ejor.2025.09.019
