Dynamic Workforce Scheduling and Routing in a Smart City Using Temporal Batch Decomposition

Abstract

A major challenge for the service providers in smart cities operating in domains such as health, security, maintenance, etc. is to provide efficient assignments of personnel to handle the incidents related to their services. These incidents could be divided into two categories. The first category is called scheduled events like regular maintenance operations, whose information is available in prior. The second category is called dynamic events and consists of emergency events that occur unpredictably at any time which we call dynamic events. The goal is to assign a common set of personnel efficiently to both the categories simultaneously, to reduce average service time of each event. Typically, in large scenarios, heuristics like greedy algorithms are used to obtain solutions in real time to facilitate immediate handling of dynamic events. However, they are myopic and cannot deliver optimized solutions across a large horizon. We propose a method for large scenarios in real time that are more globally optimized as compared to greedy algorithms. The proposed method involves a) A newly developed Mixed Integer Linear Program formulation, which considers multiple independent events that need to be handled parallelly, and b) Decomposition of the large scenario into a queue of smaller batches of events based on their occurrence/requested time. This method handles dynamically occurring events immediately without having to recompute the schedule for the entire time horizon but instead do batchwise assignment. Proposed method was validated by comparing it with the baseline greedy method and a modified version of the baseline called Priority greedy method. The assignment of personnel by the proposed method resulted in a reduction in average service time of events for large scenarios compared to that of the other two methods and was able to provide solution in a reasonable time. The proposed method increases the efficiency of providing services by reducing the associated risk.