Column generation for the truck and trailer routing problem with time windows
نویسندگان
چکیده
Motivated by the field sta routing and scheduling problem of an Austrian infrastructure service provider, which involves the planning of subroutes, we study the truck and trailer routing problem (TTRP) [2]. In the TTRP, two types of customers are considered: customers that can be visited by a truck pulling a trailer (denoted as vehicle customers) and customers that can only be visited by a truck alone (denoted as truck customers). Each customer has a given demand. In order to serve the customers, three di erent kinds of routes can be planned: routes that are carried out by a single truck (denoted as truck routes); routes that are carried out by a truck pulling a trailer (denoted as vehicle routes) without any intermediate decoupling; and routes carried out by a truck pulling a trailer involving truck-only subroutes. Each such subroute starts and ends at a vehicle customer. At this customer the trailer is decoupled and then re-coupled again at the end of the subroute. The truck has a capacity of Qtruck and the trailer a capacity of Qtrailer. Shifting loads from the trailer to the truck before departing on the next truck-only subroute is possible. The objective is to minimize the total routing cost which corresponds to the total distance traveled by the available vehicle fleet. An important aspect is that a trailer may not be picked up by a truck di erent from the one which decoupled it. Furthermore, each customer may only be visited once. This implies that a decoupling point cannot be used more than once: whenever a vehicle customer is used to park the trailer, it has to be served. However, several consecutive subroutes may start and end at the same vehicle customer.
منابع مشابه
Parallel computation framework for optimizing trailer routes in bulk transportation
We consider a rich tanker trailer routing problem with stochastic transit times for chemicals and liquid bulk orders. A typical route of the tanker trailer comprises of sourcing a cleaned and prepped trailer from a pre-wash location, pickup and delivery of chemical orders, cleaning the tanker trailer at a post-wash location after order delivery and prepping for the next order. Unlike traditiona...
متن کاملAcceleration of Lagrangian Method for the Vehicle Routing Problem with Time Windows
The analytic center cutting plane method (ACCPM) is one of successful methods to solve nondifferentiable optimization problems. In this paper ACCPM is used for the first time in the vehicle routing problem with time windows (VRPTW) to accelerate lagrangian relaxation procedure for the problem. At first the basic cutting plane algorithm and its relationship with column generation method is clari...
متن کاملBranch-and-Price for the Truck and Trailer Routing Problem with Time Windows
Motivated by a situation faced by infrastructure service providers operating in urban areas with accessibility restrictions, we study the truck and trailer routing problem with time windows (TTRPTW). In this problem the vehicle fleet consists of trucks and trailers which may be decoupled. A set of customers has to be served and some of the customers can only be accessed by the truck without the...
متن کاملA simulated annealing heuristic for the truck and trailer routing problem with time windows
In this study, we consider the application of a simulated annealing (SA) heuristic to the truck and trailer routing problem with time windows (TTRPTW), an extension of the truck and trailer routing problem (TTRP). TTRP is a variant of the well-known well-studied vehicle routing problem (VRP). In TTRP, some customers can be serviced by either a complete vehicle (that is, a truck pulling a traile...
متن کاملHeuristic column generation for the truck and trailer routing problem ⋆
We present a heuristic column generation for the truck and trailer routing problem (TTRP) in which the routes of the local optima of a GRASP/VNS are used as columns of a set-partitioning formulation of the TTRP. This approach outperforms the previous state-of-the-art methods and improves the best-known solutions for several test instances from the literature.
متن کامل