Andreas Stenger

The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations

Driven by new laws and regulations concerning the emission of greenhouse gases, carriers are starting to use electric vehicles for last-mile deliveries. The limited battery capacities of these vehicles necessitate visits to recharging stations during delivery tours of industry-typical length, which have to be considered in the route planning to avoid inefficient vehicle routes with long detours. We introduce the electric vehicle-routing problem with time windows and recharging stations E-VRPTW, which incorporates the possibility of recharging at any of the available stations using an appropriate recharging scheme. Furthermore, we consider limited vehicle freight capacities as well as customer time windows, which are the most important constraints in real-world logistics applications. As a solution method, we present a hybrid heuristic that combines a variable neighborhood search algorithm with a tabu search heuristic. Tests performed on newly designed instances for the E-VRPTW as well as on benchmark instances of related problems demonstrate the high performance of the heuristic proposed as well as the positive effect of the hybridization.

An Adaptive Variable Neighborhood Search Algorithm for a Vehicle Routing Problem Arising in Small Package Shipping

In this paper, we investigate a routing problem arising in the last-mile delivery of small packages. The problem, called Multi-Depot Vehicle Routing Problem with Private fleet and Common carriers MDVRPPC, is an extension of the Multi-Depot Vehicle Routing Problem MDVRP where customers can either be served by the private fleet based at self-owned depots or by common carriers, i.e., subcontractors. We develop an effective Variable Neighborhood Search algorithm based on the use of cyclic-exchange neighborhoods that incorporates an adaptive mechanism to bias the random shaking step. The approach is successfully used to solve MDVRPPC as well as closely related problems, such as the MDVRP and the single-depot VRP with Private fleet and Common carriers VRPPC, obtaining high quality solutions within short computing time. Our extensive testing on these problems shows the positive impact of the adaptive mechanism with respect to a standard VNS algorithm.

An adaptive VNS algorithm for vehicle routing problems with intermediate stops

There are numerous practical vehicle routing applications in which vehicles have to stop at certain facilities along their routes to be able to continue their service. At these stops, the vehicles replenish or unload their cargo or they stop to refuel. In this paper, we study the vehicle routing problem with intermediate stops (VRPIS), which considers stopping requirements at intermediate facilities. Service times occur at these stops and may depend on the load level or fuel level on arrival. This is incorporated into the routing model to respect route duration constraints. We develop an adaptive variable neighborhood search (AVNS) to solve the VRPIS. The adaptive mechanism guides the shaking step of the AVNS by favoring the route and vertex selection methods according to their success within the search. The performance of the AVNS is demonstrated on test instances for VRPIS variants available in the literature. Furthermore, we conduct tests on newly generated instances of the electric vehicle routing problem with recharging facilities, which can also be modeled as VRPIS variant. In this problem, battery electric vehicles need to recharge their battery en route at respective recharging facilities.

Location routing for small package shippers with subcontracting options

We present a location routing model that considers the requirements of real-world small package shippers. It integrates the choice between self-operating and subcontracting a depot. Furthermore, we consider the relocation costs which occur when established networks are restructured. We present a hybrid heuristic solution method based on simulated annealing for the location phase and variable neighborhood search for the routing. Through extensive numerical studies we show the competitiveness of our solution method on standard benchmarks and analyze the influence of subcontractors on the network design of a small package shipper.

A note on the time travel approach for handling time windows in vehicle routing problems

Abstract In this paper, we identify two cases in which the proposition for calculating time window penalties presented in Nagata, Y., Braysy, O. and Dullaert, W. A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows, Computers & Operations Research 2010;37(4): 724–37 yields incorrect results. We derive the corrected proposition and use numerical studies to show that a significant proportion of the evaluations performed by a Tabu Search for VRPTW falls under the two incorrect cases. Moreover, we demonstrate that the incorrect time window handling has a significant negative impact on the solution quality of the Tabu Search.

The Prize-Collecting Vehicle Routing Problem with Single and Multiple Depots and Non-Linear Cost

In this paper, we propose a new routing problem to model a highly relevant planning task in small package shipping. We consider the Prize-Collecting Vehicle Routing Problem with Non-Linear cost in its single and multi-depot version, which integrates the option of outsourcing customers to subcontractors instead of serving them with the private fleet. Thereby, a lower bound on the total customer demand to be served by the private fleet guarantees a high utilization of the fleet capacity. To represent the practical situation, where a discount is given by a subcontractor if larger amounts of packages are outsourced, subcontracting costs follow a non-linear function. The considered problem is NP-hard and we propose an Adaptive Variable Neighborhood Search algorithm to solve instances of realistic size. We propose new benchmark sets for the single and the multi-depot problem, which are adapted from test instances of the capacitated VRP and the closely related Multi-Depot VRP with Private fleet and Common carrier. In numerical studies, we investigate the performance of our algorithm on the newly generated test instances and on standard benchmark problems of related problems. Moreover, we study the effect of different cost functions and different values of the minimal demand to be served by the private fleet on the routing solutions obtained.

Territory-Based Vehicle Routing in the Presence of Time-Window Constraints

Territory-based routing approaches TBRAs are commonly used to achieve high service consistency, e.g., in the small package shipping industry, but their drawback is a decline in routing flexibility. Consequently, a high percentage of time-definite deliveries, as common in the small package shipping sector, should have a significant negative effect on the solution quality of TBRAs. To the best of our knowledge, no study exists on the magnitude of this effect and the factors that influence it. Therefore, we develop a two-phase TBRA and use it i to investigate the design requirements of a TBRA for successfully handling time windows, and ii to study the influence of time window constraints on the performance of such an approach. We find that the consideration of geographical aspects in the districting is paramount for generating high-quality territories, whereas explicitly incorporating time window characteristics and historical demand data does not lead to a perceptible improvement of the solution quality. Moreover, the efficiency and feasibility forfeits of our TBRA in comparison to daily route reoptimization RR are larger if time windows are present. However, significantly higher consistency improvements compared to RR are achieved for time-constrained problems. This is due to the fact that RR solutions to time-definite problems exhibit lower consistency and thus a higher potential for improvement by using a TBRA, which constitutes an important insight for practitioners.

Ant Colony Optimization for a stochastic vehicle routing problem with driver learning

Motivated by an industry project with a small package shipping company in France, we study a vehicle routing problem with stochastic travel and service times that considers the influence of driver familiarity with routes and customers on routing efficiency. Our approach forgoes any fixing of delivery areas thus maintaining routing flexibility. Driver specific travel and service times give drivers incentives to stay in familiar areas. Following common practice, we consider delivery deadlines instead of time windows. To solve the routing problem, we develop an Ant Colony Optimization (ACO) method due to its robustness when dealing with stochastic problem parameters. Our ACO approach includes a new indicator value to deal with customers that are hard to integrate into tours because of delivery restrictions. Numerical studies show that our algorithm is able to trade off between driver learning and routing flexibility and performs strongly for most types of test instances.

Graph Sparsification for the Vehicle Routing Problem with Time Windows

The Vehicle Routing Problem with Time Windows (VRPTW) is one of the most important and widely studied NP-hard combinatorial optimization problems in the operations research literature. The problem calls for the determination of a minimum-cost set of routes for a fleet of identical vehicles with limited capacities to serve a set of customers that have a given demand and an associated time window in which they can be visited. Due to its computational complexity, VRPTW can only be solved by exact methods for instances of moderate size [2]. However, a large number of successful metaheuristic solution methods have been proposed, which are able to produce high-quality solutions for reasonably-sized instances in limited time. For an extensive review, the reader is referred to [5, 3].

Electronic Transportation Marketplaces: How Can Green-IS Help to Promote Sustainable Logistics?

Electronic transportation marketplaces (ETMs) are inter-organizational information systems that provide an economic platform for the exchange of logistics services between shippers, carriers, and freight forwarders. Despite the fact that some of these marketplaces have already been in operation for over twenty years they have not achieved any significant influence in the logistics services domain until now. In a survey of logistics platforms that are active in Germany we identify the tendencies in development, success factors, and shortcomings of the current ETMs. The survey also shows that there is a significant lack of elaborated IS in the ETM domain. Current ETMs do not serve as integrated trading places, but rather as simple blackboards for the exchange of information. As a result, the missing functionalities and the lack of service quality make the current ETMs unattractive and keep liquidity in the transportation services markets low. A suitable way to deal with this problem is to offer extended functionalities, such as integrated tour planning, tracking-and-tracing, route pricing, etc. in order to make the ETMs more appealing for the users.