Dynamic Vehicle Routing Problem Research Articles

Dynamic Vehicle Routing Problem with Cooperative Strategy in Disaster Relief

In recent years, the growth of technology has brought about a new range of problems, referred to as dynamic vehicle routing problems. In these problems, a part of the orders are received in advance before departure of vehicles from depots, but some new orders will come in after the vehicle's departure. Although cooperative strategy has not received considerable attention in the literature, it could be a possibility in practice which can help reduce costs. Multiple vehicles in this strategy are allowed to travel and they can transfer goods between one another when they meet in demand points so as to better satisfy the late demands. A mixed integer nonlinear mathematical model is proposed for multi-vehicle routing problem considering product transshipment between vehicles in dynamic situations. A genetic algorithm is then developed to deal with the complexity of the problem. The experimental results show that cooperative strategy is an attractive possibility to reduce unsatisfied late received demands and costs.

A real-time Decision Support System for Big Data Analytic: A case of Dynamic Vehicle Routing Problems

Recently, the explosion of large amounts of traffic data has guided data scientists to create models with big data for a better decision-making. Big Data applications process and analyze this huge amounts of data (collected from a variety of heterogeneous data sources) that cannot be processed with traditional technologies. In this paper, Big Data frameworks are used for solving an optimization problem known as Dynamic Vehicle Routing Problem (DVRP). Hence, due to the NP-Hardness of the problem and to deal with a large size of data, we develop a parallel Spark Genetic Algorithm named (S-GA). This parallelism aims to take the advantage of Spark’s in-memory computing ability (as a master-slave distribution computing) and GA’s iterations operations. Parallel operations were used for fitness evaluation and genetic operations. Based on the parallel S-GA a decision support system is developed for the DVRP in order to generate the best routes. The experiments show that our proposed architecture is improved due to its capacity when coping with Big Data optimization problems by interconnecting components and deploying on different nodes of a cluster.

Redesigning Sample Transportation in Malawi Through Improved Data Sharing and Daily Route Optimization

Problem Definition: Healthcare systems in resource-limited settings rely on diagnostic networks in which medical samples (e.g. blood, sputum) and results need to be transported between geographically dispersed healthcare facilities and centralized laboratories. Due to lack of updated information, existing sample transportation (ST) systems typically operate fixed schedules which do not account for demand variability. Academic / Practical Relevance: We present an innovative approach for timely collection of information on transportation demand (samples and results) using low-cost technology based on feature phones and integrate it with a novel version of the Dynamic Multi-Period Vehicle Routing Problem to generate daily routes in response to this updated information. Methodology: We design the Optimized Sample Transportation (OST) system which comprises two components: a novel data sharing platform to monitor incoming sample volumes at healthcare facilities, and a comprehensive optimization model that generates daily transportation schedules in response to current demand at each facility. We implement OST in collaboration with Riders For Health, who operate the national ST system in Malawi. Results: Based on analysis of over 20,000 samples and results transported during July-October 2019, we show that the implementation of OST routes reduced average ST delays by approximately 25%. In addition, the proportion of unnecessary trips by ST couriers decreased by 55%. Managerial Implications: Results from our implementation demonstrate the practical feasibility of our novel approach for improving centralized ST operations in Malawi and its broader applicability to other resource-limited settings, particularly in sub-Saharan Africa.

Heuristic based on iterative exact method for multi-tour dynamic vehicle routing problem with overtime.

In the last two decades, the DVRP has received a very particular interest from researchers. Because of its great utility in modern transport systems, the DVRP is now a very motivating and still attractive line of research for new studies. The complexity of this problem comes from the fact that it belongs to the VRP family in addition to the dynamism of its components. In this article, a very little studied variant of the DVRP is dealt with. This is the multi-tour DVRP with overtime (MTDVRPOT). We treat this problem from a multi-objective point of view, based on a bi-objective mathematical modelling. The first objective is to minimise the total travelled distance, while the second objective is to minimise to maximal performed overtime. A heuristic based on the iterative exact method was proposed to solve this problem. Its principle is to divide the working day into time periods and resolve iteratively the mathematical models. Each mathematical model corresponds to one subproblem of one time period.

Memetic algorithm for multi-tours dynamic vehicle routing problem with overtime (MDVRPOT)

Memetic algorithm for multi-tours dynamic vehicle routing problem with overtime (MDVRPOT)

A Decentralized Coordination Algorithm for a Highly Dynamic Vehicle Routing Problem

The Dynamic Vehicle Routing Problem (DVRP) involves a combinatorial optimization problem where new customer demands become known over time, and old routes must be reconfigured to generate new routes while executing the current solution. We consider the high level of dynamism problem. An application of highly dynamic DVRP is the ambulance service where a patient contacts the service center, followed by an evaluation of case severity, and a visit by a practitioner/ ambulance is scheduled accordingly. This paper considers a variant of the DVRP and proposes a decentralized algorithm in which collaborators (Depot and Vehicle), both have only partial information about the entire system. The DVRP is modeled as a periodic re optimization of VRP using the proposed decentralized algorithm where collaborators exchange local information to achieve the best global objective for the current state of the system. We assume the existence of a dispatcher e.g., headquarter of the company who can communicate to vehicles in order to gather information and assigns the new visits to them. The effectiveness of the proposed decentralized coordination algorithm is further evaluated using benchmark data given in literature. The results show that the proposed method performed better than the compared algorithms which utilize the centralized coordination in 12 out of 21 benchmark problems.

Simulating Dynamic Vehicle Routing Problems with Athos

Complex routing problems, such as vehicle routing problems with additional constraints, are both hard to solve and hard to express in a form that is accessible to the human expert and at the same time processible by a computer system that is supposed to produce a solution of suf�?cient quality. The formulation must be formal enough to avoid ambiguities and also comprehensible enough to be created, discussed and shared by domain experts. In this paper, we present the domain speci�?c language Athos in which complex routing problems can be expressed in a computationally independent, human-readable form. Athos is then transformed into code that can be run in an adequate target platform. Suitable methods for solving problems are available and applied to the given problem. We present a case study in which we use a genetic algorithm to solve instances of a vehicle routing problem with time windows and demonstrate the end to end process to produce a solution in the Athos environment. Moreover, we show how the Athos system goes beyond optimisation of static routes and can be used as a tool to simulate the impact of traf�?c and congestion on the tours. We call this extended problem a dynamic vehicle routing problem with time windows.

General Method of Building a Real-Time Optimization Policy for Dynamic Vehicle Routing Problem

Abstract Currently, most of the policies for the dynamic demand vehicle routing problem are based on the traditional method for static problems as there is no general method for constructing a real-time optimization policy for the case of dynamic demand. Here, a new approach based on a combination of the rules from the static sub-problem to building real-time optimization policy is proposed. Real-time optimization policy is dividing the dynamic problem into a series of static sub-problems along the time axis and then solving the static ones. The static sub-problems’ transformation and solution rules include: Division rule, batch rule, objective rule, action rule and algorithm rule, and so on. Different combinations of these rules may constitute a variety of real-time optimization policy. According to this general method, two new policies called flexible G/G/m and flexible D/G/m were developed. The competitive analysis and the simulation results of these two policies proved that both are improvements upon the best existing policy.

Simulation-based Evaluation of Dynamic Vehicle Routing Problem Features for Algorithm Selection

Simulation-based Evaluation of Dynamic Vehicle Routing Problem Features for Algorithm Selection

Dynamic vehicle routing problem considering simultaneous dual services in the last mile delivery

PurposeThis paper aims to study the vehicle routing problem with dynamic customers considering dual service (including home delivery [HD] and customer pickup [CP]) in the last mile delivery in which three decisions have to be made: determine routes that lie along the HD points and CP facilities; optimize routes in real time, which mode is better between simultaneous dual service (SDS, HD points and CP facilities are served simultaneously by the same vehicle); and respective dual service (RDS, HD points and CP facilities are served by different vehicles)?Design/methodology/approachThis paper establishes a mixed integer linear programing model for the dynamic vehicle routing problem considering simultaneous dual services (DVRP-SDS). To increase the practical usefulness and solve large instances, the authors designed a two-phase matheuristic including construction-improvement heuristics to solve the deterministic model and dynamic programing to adjust routes to dynamic customers.FindingsThe computational experiments show that the CP facilities offer greater flexibility for adjusting routes to dynamic customers and that the SDS delivery system outperforms the RDS delivery system in terms of cost and number of vehicles used.Practical implicationsThe results provide managerial insights for express enterprises from the perspective of operation research to make decisions.Originality/valueThis paper is among the first papers to study the DVRP-SDS. Moreover, this paper guides the managers to select better delivery mode in the last mile delivery.

Logistical Planning for Electric Vehicles Under Time-Dependent Stochastic Traffic

For the benefit of global environmental preservation, electric vehicles (EVs) have been gradually accepted by people in the past few years. However, the technical problem of limited drivable range and long charging duration is still a major hurdle for the popularization of EVs, especially for commercial usage. In this paper, a dynamic electric vehicle routing problem (D-EVRP) model is designed for planning the itinerary for goods delivery by the utilization of EVs in logistics industry. To reflect the real situation, the D-EVRP considers a time-dependent stochastic traffic condition and captures the discharging/charging pattern of an EV using an analytical battery model. Its aim is to minimize the overall service duration, subject to a variety of the state-of-art constraints common in EV routing problems. Furthermore, to address the D-EVRP, a hybrid rollout algorithm (HRA), which incorporates a dedicated pre-planning strategy and a rollout algorithm, is also proposed. The effectiveness of the HRA and benefits of incorporating the analytical battery model are justified by extensive simulations using the real-world D-EVRP instances.

An iterative re-optimization framework for the dynamic vehicle routing problem with roaming delivery locations

Branch-and-price has established itself as an effective solution methodology for a wide variety of planning problems. We investigate its potential as a solution methodology for solving operational problems. Specifically, we explore its potential in the context of a dynamic variant of the vehicle routing problem with roaming delivery locations, in which customer itineraries may change during the execution of a planned delivery schedule, which, in turn, may cause the planned delivery schedule to become suboptimal or even infeasible. We propose an iterative solution framework in which an active delivery schedule is re-optimized whenever a customer itinerary update is revealed. We use a branch-and-price algorithm for solving the planning problem (to obtain an initial delivery schedule) as well as the re-optimization problems (to obtain modified delivery schedules). As the re-optimization problems are solved during the execution of the (active) delivery schedule, it is critical to produce solutions quickly. This is accomplished by re-using, suitably modified, columns generated during preceding branch-and-price solves. The results of our computational experiments demonstrate the viability of using branch-and-price for solving operational problems and the benefit (necessity) of re-using information from previous solves.

Dynamic Vehicle Routing in Presence of Random Recalls

Dynamic vehicle routing (DVR) problems involve a vehicle that seeks to service demands which are generated via a spatio-temporal stochastic process in a given environment. This letter introduces a DVR problem in which the vehicle needs to return to a central facility from time to time. We model the return events as a Poisson process with a known parameter. The problem parameters are the demand generation rate, the size of the environment and the recall rate. The goal is to design service policies for the vehicle in order to minimize the expected service time per demand. The contributions are as follows. We first provide a complete analysis of the regime of low demand arrival using a first-come-first-served policy. For the regime of high demand arrival, we derive a policy independent lower bound on the expected service time as a function of the problem parameters. We then adapt a well-known policy based on repeated computation of the Euclidean traveling salesperson tour through unserviced demands and provide an upper bound on the expected service time, quantifying the factor of optimality relative to the lower bound. We supplement the analysis with several insightful numerical simulations.

Dynamic Vehicle Routing Problem—Predictive and Unexpected Customer Availability

The Dynamic Vehicle Routing Problem (DVRP) is one of the most important problems in the area of enterprise logistics. DVRP problems involve these dynamics: the appearance of customers, travel times, service times, or vehicle availability. One of the most often considered aspects of the DVRP is the availability of customers, in which a part or all of the customers are revealed dynamically during the design or execution of the routes. A classification of the DVRP problem due to various elements causing dynamism is proposed. The aim of the paper is to distinguish dynamic VRP, which takes into account the dynamic appearance of customers to serve during the design or execution of the routes. In particular, the difference between the predictive and unexpected aspects of the customer’s availability is considered. Above all, the variant of customer’s availability which is predicted according to an appropriate general rule is modeled using the algebraic-logical meta-model (ALMM). It is a methodology which enables making collective decisions in successive process stages, not separately for individual vehicles. The algebraic-logical model of the dynamic vehicle routing problem with predicted consumer availability is proposed. The paper shows the possibilities of applying the ALMM approach to dynamic problems both with predicted and unexpected customer availability.

Optimization Heuristics for Dynamic Vehicle Routing Problems

Optimization Heuristics for Dynamic Vehicle Routing Problems

A metaheuristic approach to solve Dynamic Vehicle Routing Problem in continuous search space

Abstract This paper investigates a hypothesis that in case of hard optimization problems, selection of the proper search space is more relevant than the choice of the solving algorithm. Dynamic Vehicle Routing Problem is used as a test problem and the hypothesis is verified experimentally on the well-known set of benchmark instances. The paper compares Particle Swarm Optimization (PSO) and Differential Evolution (DE) operating in two continuous search spaces (giving in total four distinctive approaches) and a state-of-the art discrete encoding utilizing Genetic Algorithm (GA). The advantage of selected continuous search space over a discrete one is verified on the basis of quality of the final solution and stability of intermediate partial solutions. During stability analysis it has been observed that practical level of uncertainty, resulting from the dynamic nature of the problem, is lower than could be expected from a popular degree of dynamism measure. In order to challenge that issue, benchmark problems have been solved also with a higher level of dynamism and an empirical degree of dynamism has been proposed as an additional measure of the amount of uncertainty of the problem. The results obtained by both continuous algorithms outperform those of state-of-the-art algorithms utilizing discrete problem representation, while the performance differences between them (PSO and DE) are minute. Apart from higher numerical efficiency in solving DVRP, the use of continuous problem encoding proved its advantage over discrete encoding also in terms of intermediate solutions stability. Requests-to-vehicles assignment sequences generated by the proposed approach were approximately 40% more accurate with respect to the final solution than their counterparts generated with a discrete encoding. Analysis of these sequences of intermediate solutions for several benchmark sets resulted in designing a penalty term taking into account a specific dynamic nature of the optimized problem. An addition of this penalty term improved the above-mentioned stability of partial solutions by another 10%.

The +1 Method: Model-Free Adaptive Repositioning Policies for Robotic Multi-Agent Systems

Robotic multi-agent systems can efficiently handle spatially distributed tasks in dynamic environments. Problem instances of particular interest, and generality are the dynamic traveling repairman problem, and the dynamic vehicle routing problem. Operational policies for robotic fleets solving these two problems take decisions in an online setting with continuously arriving demands to optimize service level, and efficiency, and can be classified along several lines. First, some require a model of the demand, e.g., based on historical information, while others work model-free. Second, they are designed for different operating conditions from light to heavy system load. Third, they work in a time-invariant or time-varying setting. We present a novel class of model-free operational policies for time-varying demands, which have performance independent of the load factor, a combination of properties not achieved by other operational policies in the literature. The underlying principle of the introduced policies is to send available robots to recent service request locations. In simple terms, they rely on sending more than one robot for every service request arriving to the system. This leads to an advantage in scenarios where demand is non-uniformly distributed, and correlated in space, and time. We provide performance guarantees for both the time-invariant, and the time-varying cases as well as for correlated demand. We verify our theoretical results numerically. Finally, we apply our operational policy to the problem of mobility-on-demand fleet operation, and demonstrate that it outperforms model-based, and complex algorithms across all load ranges, despite its simplicity.

Adaptive State Space Partitioning for Dynamic Decision Processes

With the rise of new business processes that require real-time decision making, anticipatory decision making becomes necessary to use the available resources wisely. Dynamic real-time problems occur in many business fields, for example in vehicle routing applications with stochastic customer service requests expecting a fast response. For anticipatory decision making, offline simulation-based optimization methods like value function approximation are promising solution approaches. However, these methods require a suitable approximation architecture to store the value information for the problem states. In this paper, an approach is proposed that finds and adapts this architecture iteratively during the approximation process. A computational proof of concept is presented for a dynamic vehicle routing problem. In comparison to conventional architectures, the proposed method is able to improve the solution quality and reduces the required architecture size significantly.

Collective decision making in dynamic vehicle routing problem

The paper presents a collective decision making in dynamic vehicle routing problem. In contrast to the static problem, a part or all of the customers’ companies are revealed dynamically during the design or execution of the routes. The problem is modelled using the algebraic-logical meta-model (ALMM) methodology, which enables making collective decisions in successive process stages, not separately for individual vehicles. ALMM is considered to be the rule according to which the availability of companies is determined. The steps and schematics of the general algorithm that take into account the dynamic appearance of new companies are shown. The proposed approach belongs to trajectory-based metaheuristics methods.

A Fuzzy Receding Horizon Control Strategy for Dynamic Vehicle Routing Problem

The receding horizon control (RHC) combining with the various intelligent algorithms is a common method for the dynamic vehicle routing problem (DVRP). However, the traditional RHC only considers the objects within each time window while making route plan, and can't make adjustment according to the situations of the objects near the window. In order to deal with this problem, a fuzzy receding horizon control strategy (FRHC) is proposed. By combining the RHC and the membership function theory, the relationship between objects and time window is redefined. And the travel routes are planned by the genetic algorithm (GA) for each fuzzy time window. Finally, ten instances are selected from the DVRP standard test library to verify the proposed strategy. The experimental results show that when comparing with the RHC strategy, the FRHC can reduce the distance, the waiting time of all customers and the number of waiting customers dramatically. The FRHC combines with the GA (FRHC-GA) method is also reasonable and effective.