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This article addresses the dynamic multi-skill workforce scheduling androuting problem with time windows and synchronization constraints (DWSRP-TW-SC)inherent in the on-demand home services sector. In this problem, new servicerequests (tasks) emerge in real-time, necessitating a constant reevaluation ofservice team task plans. This reevaluation involves maintaining a portion ofthe plan unaltered, ensuring team-task compatibility, addressing taskpriorities, and managing synchronization when task demands exceed a team'scapabilities. To address the problem, we introduce a real-time optimizationframework 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 thecumulative weighted throughput time for all tasks. For the route redesign phaseof this framework, we develop both a mathematical model and an Adaptive LargeNeighborhood Search (ALNS) algorithm. We conduct a comprehensive computationalstudy to assess the performance of our proposed ALNS-based reoptimizationframework and to examine the impact of reoptimization strategies, frozen periodlengths, and varying degrees of dynamism. Our contributions provide practicalinsights and solutions for effective dynamic workforce management in on-demandhome services.

An Optimization Framework for a Dynamic Multi-Skill Workforce Scheduling and Routing Problem with Time Windows and Synchronization Constraints