A Generic Workforce Scheduling and Routing Problem with the Vehicle Sharing and Drop-off and Pick-up Policies

TL;DR

This paper presents a new workforce routing problem incorporating vehicle sharing, drop-off, and pick-up policies, solved with an ALNS algorithm, showing significant improvements in total flow time and cost savings.

Contribution

It introduces a novel generic workforce routing problem with vehicle sharing and drop-off/pick-up policies, along with a tailored ALNS solution approach.

Findings

ALNS reduces total flow time by up to 35% compared to UBA.

Drop-off and pick-up policies decrease flow time by up to 24%.

Cost savings are significant in urban-like small demand areas.

Abstract

This paper introduces a new generic problem to the literature of Workforce Scheduling and Routing Problem. In this problem, multiple workers are assigned to a shared vehicle based on their qualifications and customer demands, and then the route is formed so that a traveler may be dropped off and picked up later to minimize total flow time. We introduced a mixed-integer linear programming model for the problem. To solve the problem, an Adaptive Large Neighborhood Search (ALNS) algorithm was developed with problem-specific heuristics and a decomposition-based constructive upper bound algorithm (UBA). To analyze the impact of newly introduced policies, service area, difficulty of service, distribution of care, and number of demand nodes type instance characteristics are considered. The empirical analyses showed that the ALNS algorithm presents solutions with up to 35% less total flow time than the UBA. The implementation of the proposed drop-off and pick-up (DP) and vehicle sharing policies present up to 24% decrease in total flow time or provide savings on the total cost of service especially when the demand nodes are located in small areas like in urban areas.