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We study a new variant of the vehicle routing problem, called the MobileProduction Vehicle Routing Problem (MoP-VRP). In this problem, vehicles areequipped with 3D printers, and production takes place on the way to thecustomer. The objective is to minimize the weighted cost incurred by travel anddelay of service. We formulate a Mixed Integer Programming (MIP) model anddevelop an Adaptive Large Neighbourhood Search (ALNS) heuristic for thisproblem. To show the advantage of mobile production, we compare the problemwith the Central Production Vehicle Routing Problem (CP-VRP), where productiontakes place in a central depot. We also propose an efficient ALNS for theCP-VRP. We generate benchmark instances based on Vehicle Routing Problem withTime Windows (VRPTW) benchmark instances, and realistic instances based onreal-life data provided by the Danish Company 3D Printhuset. Overall, theproposed ALNS for both problems are efficient, and we solve instances up to 200customers within a short computational time. We test different scenarios withvarying numbers of machines in each vehicle, as well as different productiontime. The results show that these are the key factors that influence travel anddelay costs. The key advantage of mobile production is flexibility: it canshorten 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 estimationsshow that this technology has low operation costs and thus is feasible in reallife practice.

The Mobile Production Vehicle Routing Problem: Using 3D Printing in Last Mile Distribution