带时间窗与同步约束的动态多技能劳动力调度与路径规划问题的优化框架
An Optimization Framework for a Dynamic Multi-Skill Workforce Scheduling and Routing Problem with Time Windows and Synchronization Constraints
AI总结：
本文针对按需家庭服务中带时间窗与同步约束的动态多技能劳动力调度与路径规划问题，提出实时优化框架和ALNS算法，并通过计算实验验证其有效性。
摘要：
本文研究按需家庭服务领域中固有的带时间窗与同步约束的动态多技能劳动力调度与路径规划问题（DWSRP-TW-SC）。在该问题中，新的服务请求（任务）实时出现，需要对服务团队的任务计划进行持续重新评估。这种重新评估涉及保持计划的一部分不变、确保团队与任务的兼容性、处理任务优先级，以及在任务需求超出单个团队能力时管理同步。为解决该问题，我们引入了一个实时优化框架，该框架在新任务到达或设定时间过去时触发。该框架以最小化所有任务的累计加权吞吐时间为目标，重新设计团队的路径。针对该框架的路径重新设计阶段，我们开发了一个数学模型和一个自适应大邻域搜索（ALNS）算法。我们进行了全面的计算研究，以评估所提出的基于ALNS的重新优化框架的性能，并考察重新优化策略、冻结期长度和不同动态程度的影响。我们的贡献为按需家庭服务中有效的动态劳动力管理提供了实用的见解和解决方案。
英文摘要：
This article addresses the dynamic multi-skill workforce scheduling and routing problem with time windows and synchronization constraints (DWSRP-TW-SC) inherent in the on-demand home services sector. In this problem, new service requests (tasks) emerge in real-time, necessitating a constant reevaluation of service team task plans. This reevaluation involves maintaining a portion of the plan unaltered, ensuring team-task compatibility, addressing task priorities, and managing synchronization when task demands exceed a team's capabilities. To address the problem, we introduce a real-time optimization framework triggered upon the arrival of new tasks or the elapse of a set time. This framework redesigns the routes of teams with the goal of minimizing the cumulative weighted throughput time for all tasks. For the route redesign phase of this framework, we develop both a mathematical model and an Adaptive Large Neighborhood Search (ALNS) algorithm. We conduct a comprehensive computational study to assess the performance of our proposed ALNS-based reoptimization framework and to examine the impact of reoptimization strategies, frozen period lengths, and varying degrees of dynamism. Our contributions provide practical insights and solutions for effective dynamic workforce management in on-demand home services.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
