Decision support for the Technician Routing and Scheduling Problem

TL;DR

This paper introduces a matheuristic method for building optimal technician routing and scheduling scenarios that minimize combined operational and investment costs, demonstrated to improve task coverage and reduce travel time.

Contribution

The paper presents a novel holistic approach using column generation to generate optimal TRSP scenarios considering both operational and investment decisions.

Findings

Successfully suggests attractive technician scenarios

Reduces travel time by around 16% in real-life data

Enhances task servicing coverage

Abstract

The technician routing and scheduling problem (TRSP) consists of technicians serving tasks subject to qualifications, time constraints and routing costs. In the literature, the TRSP is solved either to provide actual technician plans or for performing what-if analyses on different TRSP scenarios. We present a method for building optimal TRSP scenarios, e.g., how many technicians to employ, which technician qualifications to upgrade, etc. The scenarios are built such that the combined TRSP costs (OPEX) and investment costs (CAPEX) are minimized. Using a holistic approach we can generate scenarios that would not have been found by studying the investments individually. The proposed method consists of a matheuristic based on column generation. To reduce computational time, the routing costs of a technician are approximated. The proposed method is evaluated on data from the literature and on real-life data from a telecommunication company. The evaluation shows that the proposed method successfully suggests attractive scenarios. The method especially excels in ensuring that more tasks are serviced but also reduces travel time with around 16% in the real-life instance. We believe that the proposed method could constitute an important strategic tool in field service companies and we propose future research directions to further its applicability.