Dynamic Vehicle Routing Research Articles

A review of dynamic vehicle routing problems

A number of technological advances have led to a renewed interest in dynamic vehicle routing problems. This survey classifies routing problems from the perspective of information quality and evolution. After presenting a general description of dynamic routing, we introduce the notion of degree of dynamism, and present a comprehensive review of applications and solution methods for dynamic vehicle routing problems.

Dynamic vehicle routing with anticipation in disaster relief

Pickup and delivery problems (PDP), where locations may both receive and send goods, are an extension of the classical vehicle routing problem. This paper considers the application of a routing and scheduling problem for forwarding agencies handling less-than-truckload freight in disasters. The approach evaluates the benefits of dynamic optimization anticipating varying travel times (i.e., the availability of connections in this case) as well as unknown orders (i.e., the integration of demand regions on short-notice) in the specific environment of emergencies. The objective is to avoid delays and increase equipment utilization. We model a multi-stage mixed integer problem which is able to operate under variable demand and transport conditions.

An event-driven optimization framework for dynamic vehicle routing

The real-time operation of a fleet of vehicles introduces challenging optimization problems. In this work, we propose an event-driven framework that anticipates unknown changes arising in the context of dynamic vehicle routing. The framework is intrinsically parallelized to take advantage of modern multi-core and multi-threaded computing architectures. It is also designed to be easily embeddable in decision support systems that cope with a wide range of contexts and side constraints. We illustrate the flexibility of the framework by showing how it can be adapted to tackle the dynamic vehicle routing problem with stochastic demands.

Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems

In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.

A Microsimulation Based Analysis of Exact Solution of Dynamic Vehicle Routing with Soft Time Windows

This paper presents a microsimulation-based evaluation of an exact solution approach for the soft time windows variant of the Vehicle Routing Problem (VRP) that also considers penalties on the late arrival. The exact solution approach of the Dynamic Vehicle Routing and scheduling Problem with Soft Time Windows (D-VRPSTW) is based on the column generation (Dantzig-Wolfe decomposition) scheme; whereas VISSIM has been used to simulate the traffic network under normal as well as under traffic incident conditions. Evaluations shows that the D-VRPSTW helps in avoiding additional cost as well as lateness for the freight carriers, caused due to an unexpected change in travel times along the roads.

Exact Solution for Vehicle Routing Problem with Soft Time Windows and Dynamic Travel Time

The traffic conditions in urban areas change with time due to varying congestion levels and incidents, resulting in unexpected variations of travel time on the infrastructure links. These conditions lead to the formulation of the Dynamic Vehicle Routing and scheduling Problem with Soft Time Windows (D-VRPSTW) with dynamic travel time in the city logistics field. This paper presents a column generation-based exact solution approach for the D-VRPSTW. The performance evaluation on a test instance under different dynamic travel time events shows that the D-VRPSTW solutions based on the updated travel times result in significant cost savings as compared to the static version.

Reactive and Proactive Routing Strategies with Real-Time Traffic Information

This paper deals with routing policies that can be followed by fleet managers in order to minimize the economical and computational cost of providing service under an urban context with real-time traffic information. In particular, we study the dynamic vehicle routing problem with time windows where an initial routing plan can be modified in order to fulfill new customer orders that are received during the execution of a plan. Several routing policies are examined to identify a suitable strategy to assign new customer orders while taking into account the computational effort as well as previous knowledge available about demand. In the computational tests conducted in this paper, a dynamic traffic simulation model has been used to emulate the current traffic conditions providing, at each time interval, estimates of the traffic state on each link of the road network.

A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.

A dynamic vehicle routing problem with multiple delivery routes

This paper considers a vehicle routing problem where each vehicle performs delivery operations over multiple routes during its workday and where new customer requests occur dynamically. The proposed methodology for addressing the problem is based on an adaptive large neighborhood search heuristic, previously developed for the static version of the problem. In the dynamic case, multiple possible scenarios for the occurrence of future requests are considered to decide about the opportunity to include a new request into the current solution. It is worth noting that the real-time decision is about the acceptance of the new request, not about its service which can only take place in some future routes (a delivery route being closed as soon as a vehicle departs from the depot). In the computational results, a comparison is provided with a myopic approach which does not consider scenarios of future requests.

Dynamic Vehicle Routing for Robotic Systems

Recent years have witnessed great advancements in the science and technology of autonomy, robotics, and networking. This paper surveys recent concepts and algorithms for dynamic vehicle routing (DVR), that is, for the automatic planning of optimal multivehicle routes to perform tasks that are generated over time by an exogenous process. We consider a rich variety of scenarios relevant for robotic applications. We begin by reviewing the basic DVR problem: demands for service arrive at random locations at random times and a vehicle travels to provide on-site service while minimizing the expected wait time of the demands. Next, we treat different multivehicle scenarios based on different models for demands (e.g., demands with different priority levels and impatient demands), vehicles (e.g., motion constraints, communication, and sensing capabilities), and tasks. The performance criterion used in these scenarios is either the expected wait time of the demands or the fraction of demands serviced successfully. In each specific DVR scenario, we adopt a rigorous technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. First, we establish fundamental limits on the achievable performance, including limits on stability and quality of service. Second, we design algorithms, and provide provable guarantees on their performance with respect to the fundamental limits.

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Distributed Algorithms for Environment Partitioning in Mobile Robotic Networks

A widely applied strategy for workload sharing is to equalize the workload assigned to each resource. In mobile multiagent systems, this principle directly leads to equitable partitioning policies whereby: 1) the environment is equitably divided into subregions of equal measure; 2) one agent is assigned to each subregion; and 3) each agent is responsible for service requests originating within its own subregion. The current lack of distributed algorithms for the computation of equitable partitions limits the applicability of equitable partitioning policies to limited-size multiagent systems operating in known, static environments. In this paper, first we design provably correct and spatially distributed algorithms that allow a team of agents to compute a convex and equitable partition of a convex environment. Second, we discuss how these algorithms can be extended so that a team of agents can compute, in a spatially distributed fashion, convex and equitable partitions with additional features, e.g., equitable and median Voronoi diagrams. Finally, we discuss two application domains for our algorithms, namely dynamic vehicle routing for mobile robotic networks and wireless ad hoc networks. Through these examples, we show how one can couple the algorithms presented in this paper with equitable partitioning policies to make these amenable to distributed implementation. More in general, we illustrate a systematic approach to devise spatially distributed control policies for a large variety of multiagent coordination problems. Our approach is related to the classic Lloyd algorithm and exploits the unique features of power diagrams.

Self-organizing maps in population based metaheuristic to the dynamic vehicle routing problem

We consider the dynamic vehicle routing problem (dynamic VRP). In this problem, new customer demands are received along the day. Hence, they must be serviced at their locations by a set of vehicles in real time. The approach to address the problem is a hybrid method which manipulates the self-organizing map (SOM) neural network into a population based evolutionary algorithm. The method, called memetic SOM, illustrates how the concept of intermediate structure, also called elastic net or adaptive mesh concept, provided by the original SOM can naturally be applied into a dynamic setting. The experiments show that the heuristic outperforms the approaches that were applied to the Kilby et al. 22 problems with up to 385 customers. It performs better with respect to solution quality than the ant colony algorithm MACS-VRPTW, a genetic algorithm, and a multi-agent oriented approach, with a computation time used roughly 100 times lesser.

An object-oriented evaluation framework for dynamic vehicle routing problems under real-time information

The dynamic vehicle routing problems (DVRP) is an extension of vehicle routing problems (VRP) in order to consider possible variations of travel times in the network. In this research, a two-stage framework for solving dynamic vehicle routing problem is proposed. In the first stage, the sweep method is adopted in vehicle assignment. In the second stage, a tabu search algorithm is implemented to improve routes under real-time information. The framework is implemented in an object-oriented approach and possible benefit from real-time information is illustrated through numerical simulation. The simulation-assignment model, DynaTAIWAN is applied in numerical simulation to evaluate real-time routing strategies in a traffic network. Numerical experiments are conducted in a 50 Nodes Network and a Taichung City. The results show that positive benefits could be achieved through utilization of real-time information with careful design.

An improved LNS algorithm for real-time vehicle routing problem with time windows

This paper studies the dynamic vehicle routing problem with hard time windows (DVRPTW). The main study course of this problem was briefly reviewed. The solving strategy and algorithm of the problem are put forward. First of all, DVRPTW problem is decomposed into a series of static VRPTW. When and how to decompose the DVRP is the issue, that must be addressed. An event-trigger mechanism has been proposed and used to decompose the DVRPTW into a series of system delay-snapshots. The trigger event to be adopted is a new request arrival during the stable operation. And each snapshot is regarded as a static VRPTW. Whether each static VRPTW can quickly and efficiently be solved within a given time or a shorter time, i.e. the solving time is another issue for the DVRPTW. In the solving process, how to merge the latest requirement to the current solution is the third issue that must be solved. An improved large neighborhood search (LNS) algorithm is proposed to solve the static problem. Utilizing the remove–reinsert process of the LNS, the latest request nodes are regarded as a part of the removed nodes; these nodes can be inserted into the original or current solution in good time in the reinsertion process; meanwhile, its computing speed is high and effective for it does not need to resolve primal problem each time. Computational results of a large number of test problems, which cited from Solomon's static benchmarks and Lacker’s dynamic data set, show that our method is superior to other methods in most instances.

Efficient Intelligent Optimized Algorithm for Dynamic Vehicle Routing Problem

In order to solve the dynamic vehicle routing problem (DVRP) containing both dynamic network environment and real-time customer requests, an efficient intelligent optimized algorithm called IOA is proposed in this paper, which takes advantages of both global searching ability of evolutionary algorithms and local searching capability of ant colony algorithm. The proposed IOA incorporates ant colony algorithm for exploration and evolutionary algorithm for exploitation, and uses real-time information during the optimization process. In order to discuss the performance of the proposed algorithm, a mixed integral programming model for DVRP is formulated, and benchmark functions are constructed. Detailed simulation results and comparisons with the existed work show that the proposed IOA algorithm can achieve a higher performance gain, and is well suited to problems containing dynamic network environment and real-time customer requests.

Exact Solution for Vehicle Routing Problem with Soft Time Windows and Dynamic Travel Time

The traffic conditions in urban areas change with time due to varying congestion levels and incidents, resulting in unexpected variations of travel time on the infrastructure links. These conditions lead to the formulation of the Dynamic Vehicle Routing and scheduling Problem with Soft Time Windows (D-VRPSTW) with dynamic travel time in the city logistics field. This paper presents a column generation-based exact solution approach for the D-VRPSTW. The performance evaluation on a test instance under different dynamic travel time events shows that the D-VRPSTW solutions based on the updated travel times result in significant cost savings as compared to the static version.

Dynamic Vehicle Routing for Translating Demands: Stability Analysis and Receding-Horizon Policies

We introduce a problem in which demands arrive stochastically on a line segment, and upon arrival, move with a fixed velocity perpendicular to the segment. We design a receding horizon service policy for a vehicle with speed greater than that of the demands, based on the translational minimum Hamiltonian path (TMHP). We consider Poisson demand arrivals, uniformly distributed along the segment. For a fixed segment width and fixed vehicle speed, the problem is governed by two parameters; the demand speed and the arrival rate. We establish a necessary condition on the arrival rate in terms of the demand speed for the existence of any stabilizing policy. We derive a sufficient condition on the arrival rate in terms of the demand speed that ensures stability of the TMHP-based policy. When the demand speed tends to the vehicle speed, every stabilizing policy must service the demands in the first-come-first-served (FCFS) order; and the TMHP-based policy becomes equivalent to the FCFS policy which minimizes the expected time before a demand is serviced. When the demand speed tends to zero, the sufficient condition on the arrival rate for stability of the TMHP-based policy is within a constant factor of the necessary condition for stability of any policy. Finally, when the arrival rate tends to zero for a fixed demand speed, the TMHP-based policy becomes equivalent to the FCFS policy which minimizes the expected time before a demand is serviced. We numerically validate our analysis and empirically characterize the region in the parameter space for which the TMHP-based policy is stable.

Online vehicle routing and scheduling with dynamic travel times

The developments in mobile communication technologies are a strong motivation for the study of dynamic vehicle routing and scheduling problems. In particular, the planned routes can be quickly modified to account for the occurrence of new customer requests, which might imply diverting a vehicle away from its current destination. In this paper, a previously developed problem-solving approach for a vehicle routing problem with dynamic requests and dynamic travel times is extended to account for more sophisticated communication means between the drivers and the central dispatch office. Computational results are reported to empirically demonstrate the benefits of this extension.

Dynamic Vehicle Routing Problem and its Algorithm Analysis

Based on systematically illustrating the current research of dynamic vehicle routing problem and one-off optimization strategies, the paper takes dynamic vehicle routing problem with time window as the study object, proposed a new INTER-SQM dynamic programming strategy to optimize the traffic path of moving vehicle, which highlights the robustness of INTER-SQM strategy, the corresponding ant colony optimization is presented also. Random test compares the operating performance of various optimization strategies in different strong dynamic degrees scenario, discusses the characteristics of the INTER-SQM strategy and points out the research direction of dynamic vehicle routing problem.