Real-world Vehicle Routing Problem Research Articles

Uncertainty and Dynamicity in Real-World Vehicle Routing (Student Abstract)

Interest in vehicle routing problems (VRP) with stochastic and dynamic elements has grown in the past decade. Despite numerous contributions in this area, the handling of uncertainties and dynamic changes in complex VRPs received little attention. Based on our experience from industrial practice, we discuss why accounting for uncertainties and dynamic changes is crucial for the applicability of the produced routing plans. Then, we first identify and justify the best-suited direction to address dynamicity and uncertainties in real-world VRPs. Second, we outline the key concepts and ideas of our approach to finally demonstrate that it is realistic to implement them efficiently.

Investigating various solution techniques for real-world vehicle routing problems

Operations research (OR) is a field of study that employs analytical techniques to improve decision-making. The field of OR can help you make better decisions in a variety of fields, including manufacturing, transportation, project management, supply chain management, etc. Vehicle Routing Problem (VRP) is a well-studied application within the field of OR. The primary goals of the vehicle routing problem are to find the optimal route plan to minimize distribution costs and travel times while maximizing customer satisfaction and fleet capacity utilization [1] (Figure 1). Researchers have used VRP in a variety of fascinating applications, including green vehicle routing, electric vehicle routing, unmanned aerial vehicle routing

Route Duration Prediction in a Stochastic and Dynamic Vehicle Routing Problem with Short Delivery Deadlines

Abstract We are facing a real-world vehicle routing problem where orders arrive dynamically over the day at an online store and have to be delivered within short time. Stochastic information in form of the expected number and weight of orders and the traffic congestion level is available upfront. The goal is to predict the average time needed to deliver an order for a given time and day. This information is desirable for both routing decisions in the short horizon and planning vehicle drivers’ shifts with just the right capacity prior to the actual day. We compare a white box linear regression model and a neural network based black box model on historic route data collected over three months. We employ a hourly data aggregation approach with sampling statistics to estimate the ground truth and features. The weighted mean square error is used as loss function to favor samples with less uncertainty. A mean validation R2 score over 10 x 5-fold cross-validations of 0.53 indicates a substantial amount of unexplained variance. Both predictors are slightly optimistic and produce median standardized absolute residuals of about one.

Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm

With the development of information and telecommunication technology and the wide adoption of smartphones, consumers gradually change their purchase pattern toward online shopping. They can order products from their smartphones at any moment from any place, and the volume and variety of products delivered to consumers are increasing explosively. Companies in this industry need to set up the operational strategies to accommodate the increasing demand for delivery and return of products, and their focus should be the real-world vehicle routing problems with an additional consideration of the dynamic orders placed over time. This study proposes a waiting strategy for the vehicle routing problem with simultaneous pickup and delivery. This strategy implements an index called the rerouting indicator, which functions as a decision-making threshold to determine the rerouting point for real-time demands. For the most real-world-cases with complex problems, this study proposes a genetic algorithm to solve and validate its accuracy and performance by comparing the computational results. The significance and application of the waiting strategy are validated through several experiments, and the appropriate discretion by a decision maker can demonstrate the value of the proposed strategy.

До класифiкацiї задач маршрутизацiї транспортних засобiв

The essence of the problems considered consists in developing routes for a group of heterogeneous vehicles based in a specific place or places (depot) that have to deliver goods to customers according to their volume of orders. The classic vehicle routing problem belongs to combinatorial optimization problems, which can be represented as a graph with the set of vertices representing both the depot and the delivery point, i.e. customers, and the set of edges corresponding to the paths. In this problem, the following data are given: matrix of edges weights between vertices, determined by the value/length of paths; thenumber of vehicles involved in the delivery of the goods; volumes of goods to be delivered to customers at each delivery point.The topicality of the vehicle routing problem has led to the emergence of numerous studies conducted over the last decades and relevant publications. While composing the list of scientific publications cited in the article, the authors did not aim to provide a historical perspective on the study of vehicle routing problem (it is quite fully reflected in the majority of works), but preferred the publications of recent years that show the current state of research.Most real-world vehicle routing problems have additional constraints that give rise to a whole range of new problem formulations. The paper presents a number of classes of vehicle routing problems. The main limitation is the capacity, and the criterion is the total cost of transportation. In routing problems with time constraints or “time windows”, minimizing the total cost of transportation is combined with minimizing the number of vehicles involvedand the overall waiting time for vehicle customers. Minimizing the cost of transportation and the size of the park of the involved vehicles can be required even if these vehicles leave from several depots, and both the start and the end of the route may be located not in fixed, but in alternative, in particular, dynamic depots. Beside the above mentioned the following tasks are considered: routing problems for return and delivery of goods, routing with two-dimensional restrictions on vehicle loading, maximization of savings on goods transportation, routing with different modes of transport, periodic routing with random data, routing with the possibility of loading, routing with predefined deadlines. For each problem type additional restrictions that differ from the classical problem are introduced.

An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics

Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world vehicle routing problems (VRPs) in the field of logistics. The work consists of two basic units: (i) an innovative multistep algorithm for successful and entirely feasible solving of the VRPs in logistics and (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics, and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the decision support system with predictive models: generalized linear models (GLMs) and support vector machine (SVM). The algorithm, along with the control parameters, which uses the prediction method, was acquired and was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.

An evolutionary algorithm approach for the constrained multi-depot vehicle routing problem

Purpose – The purpose of this paper is to explore a real world vehicle routing problem (VRP) that has multi-depot subcontractors with a heterogeneous fleet of vehicles that are available to pickup/deliver jobs with varying time windows and locations. Both the overall job completion time and number of drivers utilized are analyzed for the automated job allocations and manual job assignments from transportation field experts. Design/methodology/approach – A nested genetic algorithm (GA) is used to automate the job allocation process and minimize the overall time to deliver all jobs, while utilizing the fewest number of drivers – as a secondary objective. Findings – Three different real world data sets were used to compare the results of the GA vs transportation field experts’ manual assignments. The job assignments from the GA improved the overall job completion time in 100 percent (30/30) of the cases and maintained the same or fewer drivers as BS Logistics (BSL) in 47 percent (14/30) of the cases. Originality/value – This paper provides a novel approach to solving a real world VRP that has multiple variants. While there have been numerous models to capture a select number of these variants, the value of this nested GA lies in its ability to incorporate multiple depots, a heterogeneous fleet of vehicles as well as varying pickup times, pickup locations, delivery times and delivery locations for each job into a single model. Existing research does not provide models to collectively address all of these variants.

Heuristics to Solve a Real-world Asymmetric Vehicle Routing Problem with Side Constraints

To maintain patients at home as most as possible, healthcare services are nowadays quite diversified. We present the case of a public medical clinic offering activities, mostly to the elderly, at a daycare center. Users are brought into the daycare center by bus or by taxi. The global problem consists in defining routes to pick up users while assigning them to time slots in the week. At first sight this problem could be viewed as an asymmetric multiple vehicle routing problem. However many additional constraints must be considered. In this paper, we propose a metaheuristic including two phases to solve the problem. In the first phase, the initial solution is determined using one of two proposed constructed algorithms and is improved using tabu search in the second phase. These algorithms are tested on 10 problem instances. Experimental results are presented.

Solving a real-world vehicle routing problem with multiple use of tractors and trailers and EU-regulations for drivers arising in air cargo road feeder services

In this paper we present two approaches for solving a real-world vehicle routing problem arising in the air cargo road feeder service business. The problem is to combine transportation tasks from a given timetable to trips which have to be assigned to tractors and which can be operated by tractor drivers respecting the restrictive rules on driving times from EC Regulation No. 561/2006. Tractor trips which start and end at the hub can be combined to multiple-trips which are operated by the same tractor. Also, to each trip a trailer has to be assigned which is compatible with all tasks in the trip. The primary objective is to minimize the number of required tractors, i.e. the number of multiple-trips. The methods developed are currently applied in practice.

Solving real-world vehicle routing problems with time windows using virus evolution strategy

This paper proposes a new solution to the vehicle routing problem with time windows using an evolution strategy adopting viral infection. The problem belongs to the NP-hard class and is very difficult to solve within practical time limits using systematic optimization techniques. In conventional evolution strategies, a schema with a high degree-of-fitness produced in the process of evolution may not be inherited when the fitness of the individual containing the schema is low. The proposed method preserves the schema as a virus and uses it by the infection operation in successive generations. Experimental results using extended Solomon's benchmark problems with 1000 customers proved that the proposed method is superior to conventional methods in both its rates of searches and the probability of obtaining solutions. Further experiments using the map of the central part of Tokyo with 20000 intersections and real traffic data also gave that the rate of search of the proposed method is higher than that of the conventional method.

Solving real-world vehicle routing problems in uncertain environments

This is a summary of the Ph.D. thesis defended by the author in December 2009 at Ecole des Mines de Nantes and Universidad de los Andes in Bogota. The thesis was advised by Christelle Gueret and Andres L. Medaglia and co-advised by Bruno Castanier and Nubia Velasco. The manuscript is written in English and it is available from the author upon request. The focus of the dissertation is to study real-world vehicle routing problems (VRPs) in uncertain environments. First, the thesis proposes a set of new methods for the VRP faced by a public utility and reports how these methods were embedded into a decision support system. Second, the thesis introduces a stochastic VRP widely found in practice but never studied in the literature before: the multi-compartment VRP with stochastic demands (MC-VRPSD). To solve the problem the dissertation proposes a set of solution methods that offer different tradeoffs between accuracy, speed, simplicity and flexibility. Lastly, the thesis proposes two multiobjective approaches to address the risk behavior of decision makers towards the cost spread in stochastic routing problems and applies them to the MC-VRPSD.

Solving real-world vehicle routing problems with time windows using virus evolution strategy

This paper proposes a new solution to the vehicle routing problem with time windows using an evolution strategy adopting viral infection. The problem belongs to the NP-hard class and is very diffic...

Call for Papers—Special Issue of Transportation Science: Advances in Vehicle Routing

Vehicle routing has been one of the main research areas in transportation science over the last 50 years. The design of good models and efficient solution methods to this problem is becoming increasingly important. There is also a clear trend toward richer, more descriptive models and correspondingly improved solution methods, enabling both larger and more complex real-world vehicle routing problems to be solved. Since 1991, the TRISTAN conferences have become one of the main gathering points of top researchers from academia and industry in all areas of transportation science, including vehicle routing. TRISTAN VII, which will be held in Tromso, Norway from June 20 to 25, 2010, will continue this tradition of excellence and friendly scientific exchanges. In connection with the activities of this conference, we are very glad to announce a special issue of Transportation Science, devoted to selected publications on Advances in Vehicle Routing. We seek submissions from participants to TRISTAN VII, but the special issue is open to the entire international research community in vehicle routing. Contributions covering all topics in vehicle routing are welcome. The topics include, but are not limited to, the full range from new problem formulations and new models to more effective exact or approximate solution approaches. All contributions will be peer reviewed according to the usual standards of the journal. Submission: Please submit your manuscript online via ScholarOne Manuscripts at http://mc. manuscriptcentral.com/transci. When choosing Manuscript Type in step 1 of the submission procedure, click “Special Issue—Vehicle Routing,” otherwise your submission will be handled as a regular manuscript.

Ant colony optimization for real-world vehicle routing problems

Ant colony optimization (ACO) is a metaheuristic for combinatorial optimization problems. In this paper we report on its successful application to the vehicle routing problem (VRP). First, we introduce the VRP and some of its variants, such as the VRP with time windows, the time dependent VRP, the VRP with pickup and delivery, and the dynamic VRP. These variants have been formulated in order to bring the VRP closer to the kind of situations encountered in the real-world. Then, we introduce the basic principles of ant colony optimization, and we briefly present its application to the solution of the VRP and of its variants. Last, we discuss the applications of ACO to a number of real-world problems: a VRP with time windows for a major supermarket chain in Switzerland; a VRP with pickup and delivery for a leading distribution company in Italy; a time dependent VRP for freight distribution in the city of Padua, Italy, where the travel times depend on the time of the day; and an on-line VRP in the city of Lugano, Switzerland, where customers’ orders arrive during the delivery process.

Ant colony optimization for real-world vehicle routing problems

Metaheuristics like ant colony optimization (ACO) can be used to solve combinatorial optimization problems. In this paper we refer to its successful application to the vehicle routing problem (VRP). At the beginning, we introduce the VRP and some of its variants. The variants of VRP were designed to reproduce the kind of situations faced in the real-world. Further, we introduce the fundamentals of ant colony optimization, and we present in few words its application to the solution of the VRP. At the end, we discuss the applications of ACO to a number of real-world problems: a VRP with time windows for a major supermarket chain in Switzerland; a VRP with pickup and delivery for a leading distribution company in Italy and an on-line VRP in the city of Lugano, Switzerland, where clients' orders arrive during the delivery process.

Online-Optimization of Large-Scale Vehicle Dispatching Problems

Abstract In this talk we investigate a real-world large scale vehicle routing problem posed by our cooperation partner, the German Automobile Association (ADAC). Service vunits are requested to assist people whose cars break down. Such service requests arrive online. The goal is to route the requests to service vehicles such that low operational costs and good quality of service is provided (soft time windows). Currently, a column generation approach is used to solve the offline problem, in which only known requests are considered. In order to speed up the column generation, we need to start with “good” short routes. We will show that that this problem is NP-hard even for two requests per service unit. Moreover, we present an approximation algorithm with a constant-factor approximation rate even with nonlinear lateness costs for violating the soft time windows.

Economic Heuristic Optimization for Heterogeneous Fleet VRPHESTW

A three-step local search algorithm based on a probabilistic variable neighborhood search is presented for the vehicle routing problem with a heterogeneous fleet of vehicles and soft time windows (VRPHESTW). A generation mechanism based on a greedy randomized adaptive search procedure, a diversification procedure using an extinctive selection evolution strategy, and a postoptimization method based on a threshold algorithm with restarts are considered to solve the problem. The results show the convenience of using an economic objective function to analyze the influence of the changes in the economic environment on the transportation average profit of vehicle routing problems. Near real-world vehicle routing problems need (1) an economic objective function to measure the quality of the solutions as well as (2) an appropriate guide function, which may be different from the economic objective function, for each heuristic method and for each economic scenario.

Waiting Strategies for Dynamic Vehicle Routing

Many real-world vehicle routing problems are dynamic optimization problems, with customer requests arriving over time, requiring a repeated reoptimization. In this paper, we consider a dynamic vehicle routing problem where one additional customer arrives at a beforehand unknown location when the vehicles are already under way. Our objective is to maximize the probability that the additional customer can be integrated into one of the otherwise fixed tours without violating time constraints. This is achieved by letting the vehicles wait at suitable locations during their tours, thus influencing the position of the vehicles at the time when the new customer arrives. For the cases of one and two vehicles, we derive theoretical results about the best waiting strategies. The general problem is shown to be NP-complete. Several deterministic waiting strategies and an evolutionary algorithm to optimize the waiting strategy are proposed and compared empirically. It is demonstrated that a proper waiting strategy can significantly increase the probability of being able to service the additional customer, at the same time reducing the average detour to serve that customer.

Dynamic vehicle routing: Status and prospects

Although most real-world vehicle routing problems are dynamic, the traditional methodological arsenal for this class of problems has been based on adaptations of static algorithms. Still, some important new methodological approaches have recently emerged. In addition, computer-based technologies such as electronic data interchange (EDI), geographic information systems (GIS), global positioning systems (GPS), and intelligent vehicle-highway systems (IVHS) have significantly enhanced the possibilities for efficient dynamic routing and have opened interesting directions for new research. This paper examines the main issues in this rapidly growing area, and surveys recent results and other advances. The assessment of possible impact of new technologies and the distinction of dynamic problems vis-a-vis their static counterparts are given emphasis.

Solving Practical Vehicle Routing Problem with Time Windows Using Metaheuristic Algorithms

This paper addresses the Vehicle Routing Problem with Time Windows (VRPTW) and shows that implementing algorithms for solving various instances of VRPs can significantly reduce transportation costs that occur during the delivery process. Two metaheuristic algorithms were developed for solving VRPTW: Simulated Annealing and Iterated Local Search. Both algorithms generate initial feasible solution using constructive heuristics and use operators and various strategies for an iterative improvement. The algorithms were tested on Solomon’s benchmark problems and real world vehicle routing problems with time windows. In total, 44 real world problems were optimized in the case study using described algorithms. Obtained results showed that the same distribution task can be accomplished with savings up to 40% in the total travelled distance and that manually constructed routes are very ineffective.