Applying the Intelligent Decision Heuristic to Solve Large Scale Technician and Task Scheduling Problems

Abstract

Scheduling personnel to complete tasks is a complex combinatorial optimisation problem. In large organisations, finding quality solutions is of paramount importance due to the costs associated with staffing. In this paper we have generated and solved a set of novel large scale technician and task scheduling problems. The datasets include complexities such as priority levels, precedence constraints, skill requirements, teaming and outsourcing. The problems are considerably larger than those featured previously in the literature and are more representative of industrial scale problems, with up to 2500 jobs. We present our data generator and apply two heuristics, the intelligent decision heuristic and greedy heuristic, to provide a comparative analysis.

Robust Optimization Approaches for Routing and Scheduling of Multi-Skilled Teams under Uncertain Job Skill Requirements

“…Following these initial contributions, an increasing number of extensions has been presented. For instance, such features as routing of teams (Kovacs et al, 2012), multi-period planning (Zamorano and Stolletz, 2017), employee preferences for performing a specific job (Fırat et al, 2016), alternative heuristic solution methods (Khalfay et al, 2017), decomposition techniques (Anoshkina and Meisel, 2019) as well as team consistency and rescheduling (Anoshkina and Meisel, 2020) have been studied. A more detailed description of these studies is provided by Anoshkina and Meisel (2020).…”