Yibo Dang

With the continuous improvement and observable benefits of electric vehicles (EVs), major logistic companies are introducing more EVs into their conventional fleets. This gives rise to a new type of vehicle routing problem with mixed vehicles, where heterogeneous internal combustion vehicles (ICVs) and electric vehicles are considered in route planning. In addition, certain deliveries that are not efficient on any type of vehicles, are outsourced to third-party common carriers. In this paper, we define this problem as a mixed vehicle routing problem with common carriers (MVRPC). The objective of such problems is to minimize the transportation costs by considering routes with ICVs and EVs, the possibility of visiting recharging stations, outsourcing options, and drivers’ layover regulations. This variant of the vehicle routing problems has many practical applications, particularly in the design of long-haul transportation and last-mile delivery services. Effective MVRPC solutions play a key role in promoting the going Green image and optimally allocating resources. The problem has received limited attention in the literature likely because addressing all the needed aspects is especially challenging. To solve the large-scale problem, we develop a branch-and-cut pricing framework that relies on strong cuts and customized labeling algorithms. Numerical experiments highlight the effectiveness of our algorithm. This success can be attributed to tailored critical resources, dynamically bounded bidirectional labeling procedures, strong dominance criteria, and implementation strategies.

DHL Supply Chain North America moves more than 20 million packages each year. DHL transportation planners perform routing and cost-deduction tasks for many business projects. We refer to the associated planning problem as the Vehicle Routing Problem with Time Regulations and Common Carriers (VRPTRCC). Unlike ordinary vehicle routing problems, which use only a single type of transportation mode, our VRPTRCC applications include make–buy decisions because some of the package deliveries are ultimately subcontracted to organizations other than DHL. Time regulation means that the problem considers not only delivery-time windows, but also layover and driving-time restrictions. Our developed Network Mode Optimization Tool (NMOT) is an ant-colony optimization (ACO)-based program that aids DHL Supply Chain transportation analysts in identifying cost savings in the ground logistic network. By using the NMOT, DHL and its customers have saved millions of dollars annually. Also, the NMOT is help...