  We study a new variant of the vehicle routing problem, called the Mobile
Production Vehicle Routing Problem (MoP-VRP). In this problem, vehicles are
equipped with 3D printers, and production takes place on the way to the
customer. The objective is to minimize the weighted cost incurred by travel and
delay of service. We formulate a Mixed Integer Programming (MIP) model and
develop an Adaptive Large Neighbourhood Search (ALNS) heuristic for this
problem. To show the advantage of mobile production, we compare the problem
with the Central Production Vehicle Routing Problem (CP-VRP), where production
takes place in a central depot. We also propose an efficient ALNS for the
CP-VRP. We generate benchmark instances based on Vehicle Routing Problem with
Time Windows (VRPTW) benchmark instances, and realistic instances based on
real-life data provided by the Danish Company 3D Printhuset. Overall, the
proposed ALNS for both problems are efficient, and we solve instances up to 200
customers within a short computational time. We test different scenarios with
varying numbers of machines in each vehicle, as well as different production
time. The results show that these are the key factors that influence travel and
delay costs. The key advantage of mobile production is flexibility: it can
shorten the time span from the start of production to the delivery of products,
and at the same time lower delivery costs. Moreover, long-term cost estimations
show that this technology has low operation costs and thus is feasible in real
life practice.
