Distributors are faced with loading constraints in their route planning, e.g.,multi-dimensional packing constraints, unloading sequence constraints, stability constraints and axle weight limits. Ignoring these constraints impairs planning and induces last-minute changes resulting in additional costs. Developing vehicle routing models incorporating loading constraints is critical to more efficient route planning. The last couple of years has seen a huge increase of contributions to this field of research with almost 60 % of these being published after 2009. Our contribution is twofold. First, we overview the recent developments in the literature on all vehicle types in which loading constraints play a key role (trucks, airplanes, ships, and automated guided vehicles), using a state-of-the-art classification scheme to identify the loading constraints considered in each article. Second, we identify research gaps and opportunities for future research.

The capacitated vehicle routing problem with sequence-based pallet loading and axle weight constraints is an extension of the classical Capacitated Vehicle Routing Problem (CVRP). It integrates loading constraints in a routing problem and is based on a real-world transportation problem. The demand of the customers consists of pallets. These pallets may be placed in two horizontal rows inside the vehicle. Sequence-based loading is imposed which ensures that when arriving at a customer, no pallets belonging to customers served later on the route block the removal of the pallets of the current customer. Furthermore, the capacity of a truck is not only expressed in total weight and number of pallets but also consists of a maximum weight on the axles of the truck. Axle weight limits pose a challenge for transportation companies as they incur high fines in the event of non-compliance. WeighIn-Motion (WIM) systems on highways monitor axle weight violations of trucks while driving which increase the chances that axle weight violations are detected. Furthermore, trucks with overloaded axles represent a threat for traffic safety and may cause serious damage to the road surface. In this presentation, an Iterated Local Search (ILS) methodology is proposed to tackle the problem on realistic-size instances with networks consisting of 50, 75 and 100 customers. The effects of integrating axle weight restrictions in a CVRP on total routing costs are analyzed by comparing the results with those of the CVRP without axle weight restrictions. * Speaker sciencesconf.org:verolog2016:89144

In this article an Iterated Local Search algorithm for the capacitated vehicle routing problem with sequencebased pallet loading and axle weight constraints is presented. Axle weight limits impose a great challenge for transportation companies. Yet, the literature on the incorporation of axle weight constraints in vehicle routing models is very scarce. The effect of introducing axle weight constraints in a CVRP on total routing cost is analyzed. Results show that integrating axle weight constraints does not lead to a large cost increase. However, not including axle weight constraints in the planning process may induce major axle weight violations.

I also received a lot of support outside of work from my family, family-in-law and friends. I want to thank them for their genuine interest in my research, for sharing my enthusiasm when I published a paper or reached another milestone in my Ph.D., for offering me distraction from the Ph.D. and for cheering me up when necessary. A special word of thanks goes out to my parents and sisters, Leen and Tine, for all their heartfelt encouragements which often kept me going and for actively supporting me all my life. And finally, I would like to thank my soon to be husband, Wouter, for always believing in me, for putting up with me at times when I was totally stressed out, for your understanding, your support and your insights whenever I needed them. Thank you for everything. Hanne Pollaris Hasselt, March 2017 A Results insertion methods B Detailed results: ILS algorithm on small-size instances C Detailed results: minimization of total distance D Detailed results: minimization of total transport cost

A vehicle routing problem (VRP), using a mixed fleet of vehicles, with sequencebased pallet loading and axle weight constraints is introduced. The effect of the integration of axle weight constraints in a Fleet Size and Mix VRP is analyzed by comparing the problem with and without axle weight constraints. A vehicle fleet of 30-foot and 45-foot trucks, consisting of a tractor and a semi-trailer, is considered. Two scenarios are analyzed with different objective functions. In the first scenario, the objective aims to minimize total distance while in the second scenario the objective aims the minimization of total transport costs. An Iterated Local Search metaheuristic algorithm is used to solve the problem. The results indicate that the impact of axle weight constraints on the solution cost of a vehicle routing problem depends on the fleet composition. Therefore, decisions on the deployment of a mixed-size fleet may be influenced by the integration of axle weight constraints.

A vehicle routing problem (VRP) with sequence-based pallet loading and axle weight constraints is introduced in the study. An Iterated Local Search (ILS) metaheuristic algorithm is used to solve the problem. Like any metaheuristic, a number of parameters need to be set before running the experiments. Parameter tuning is important because the value of the parameters may have a substantial impact on the efficacy of a heuristic algorithm. While traditionally, parameter values have been set manually using expertise and experimentation, recently several automated tuning methods have been proposed. The performance of the routing algorithm is mostly improved by using parameter tuning, but no single best tuning method for routing algorithms exists. The tuning method, Iterated F-race, is chosen because it seems to be a very robust method and it has been shown to perform well on the ILS metaheuristic and other metaheuristics. The research aims at developing an algorithm, which performs well over a wide range of network sizes.