In this paper, we explore trade-offs between operational flexibility and operational complexity in periodic distribution
problems. We consider the gains from operational flexibility in terms of vehicle routing costs and customer service benefits,
and the costs of operational complexity in terms of implementation difficulty. Periodic distribution problems arise in a
number of industries, including food distribution, waste management and mail services. The period vehicle routing problem
(PVRP) is a variation of the classic vehicle routing problem in which driver routes are constructed for a period of time; the
PVRP with service choice (PVRP-SC) extends the PVRP to allow service (visit) frequency to become a decision of the
model. While introducing operational flexibility in periodic distribution systems can increase efficiency, it poses three
challenges: the difficulty of modeling this flexibility accurately; the computational effort required to solve the problem as
modeled with such flexibility; and the complexity of operationally implementing the resulting solution. This paper considers
these trade-offs between the system performance improvements due to operational flexibility and the resulting increases in
operational and computational complexity as they relate to periodic vehicle routing problems. In particular, increasing the
operational complexity of driver routes can be problematic in industries where some level of system regularity is required. As
discussed in the paper, recent work in the literature suggests that dispatching drivers consistently to the same geographic
areas results in driver familiarity and improved driver performance. Additionally, having the same driver visit a customer on
a continual basis can foster critical relationships. According to UPS, such driver-customer relationships are a key competitive
advantage in its package delivery operations, attributing 60 million packages a year to sales leads generated by drivers. In this
paper, we develop a set of quantitative measures to evaluate the trade-offs between flexibility and complexity
