Multi-shift Single-Vehicle Routing Problem Under Fuzzy Uncertainty

This research considers the single-vehicle routing problem (VRP) with multi-shift and fuzzy uncertainty. In such a problem, a company constantly uses one vehicle to fulfill demand over a scheduling period of several work shifts. In our case, a crew executes maintenance jobs in different sites. The working team runs during different work shifts, but recurrently returns to the depot by the end of the shift (overtime avoidance). The goal consists in minimizing the number of work shifts (makespan). We observe the impact of uncertainty in travel and maintenance processing time on the overtime avoidance constraint. We realize an Artificial Immune Heuristic to get optimal solutions considering both makespan and overtime avoidance. First, we present a Pareto-based framework to evaluate the uncertainty influence. Then, we show a numerical real case study to survey the problem. In particular, a case study scenario has been created on the basis of the environmental changes in travel and processing times observed in Italy during the Covid-19 lockdown period (started on March 9, 2020). Results present important improvements are obtained with the proposed approach.