On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Shortest Path versus Multi-Hub Routing in Networks with Uncertain Demand

We study a class of robust network design problems motivated by the need to scale core networks to meet increasingly dynamic capacity demands. Past work has focused on designing the network to support all hose matrices (all matrices not exceeding marginal bounds at the nodes). This model may be too conservative if additional information on traffic patterns is available. Another extreme is the fixed demand model, where one designs the network to support peak point-to-point demands. We introduce a capped hose model to explore a broader range of traffic matrices which includes the above two as special cases. It is known that optimal designs for the hose model are always determined by single-hub routing, and for the fixed- demand model are based on shortest-path routing. We shed light on the wider space of capped hose matrices in order to see which traffic models are more shortest path-like as opposed to hub-like. To address the space in between, we use hierarchical multi-hub routing templates, a generalization of hub and tree routing. In particular, we show that by adding peak capacities into the hose model, the single-hub tree-routing template is no longer cost-effective. This initiates the study of a class of robust network design (RND) problems restricted to these templates. Our empirical analysis is based on a heuristic for this new hierarchical RND problem. We also propose that it is possible to define a routing indicator that accounts for the strengths of the marginals and peak demands and use this information to choose the appropriate routing template. We benchmark our approach against other well-known routing templates, using representative carrier networks and a variety of different capped hose traffic demands, parameterized by the relative importance of their marginals as opposed to their point-to-point peak demands

Vehicle routing under time-dependent travel times: the impact of congestion avoidance

Daily traffic congestions form major problems for businesses such\ud as logistical service providers and distribution firms. They cause\ud late arrivals at customers and additional hiring costs for the truck\ud drivers. The additional costs of traffic congestions can be reduced\ud by taking into account and avoid well-predictable traffic congestions\ud within off-line vehicle route plans. In the literature, various strategies\ud are proposed to avoid traffic congestions, such as selecting alternative routes, changing the customer visit sequences, and changing the\ud vehicle-customer assignments. We investigate the impact of these and\ud other congestion avoidance strategies in off-line vehicle route plans on\ud the performance of these plans in reality. For this purpose, we develop\ud a set of VRP instances on real road networks, and a speed model that\ud inhabits the main characteristics of peak hour congestion. The instances are solved for different levels of congestion avoidance using a\ud modified Dijkstra algorithm and a restricted dynamic programming\ud heuristic. Computational experiments show that 99% of late arrivals\ud at customers can be eliminated if traffic congestions are accounted for\ud off-line. On top of that, almost 70% of the extra duty times caused by\ud the traffic congestions can be eliminated by clever avoidance strategies