Rich Vehicle Routing Problem Research Articles

Solving a real case of rich vehicle routing problem with zone-dependent transportation costs

Solving a real case of rich vehicle routing problem with zone-dependent transportation costs

Optimal Transportation Planning for a Do-It-Yourself Retailer with a Zone-Based Tariff

This article describes the development of a decision support tool for a Rich Vehicle Routing Problem (R-VRP) of a major European do-it-yourself (DIY) retailer. The tool enables more comprehensive and structured planning at the retailer and an average of 8% transportation cost savings, translating to total savings of more than € 1 million per year for this specific retailer compared with the status quo.

Optimizing Maritime Passenger Transfer in Rich Vehicle Routing Problem Using a Hybrid Genetic Algorithm

Optimizing Maritime Passenger Transfer in Rich Vehicle Routing Problem Using a Hybrid Genetic Algorithm

Standardized validation of vehicle routing algorithms

Designing routing schedules is a pivotal aspect of smart delivery systems. Therefore, the field has been blooming for decades, and numerous algorithms for this task have been proposed for various formulations of rich vehicle routing problems. There is, however, an important gap in the state of the art that concerns the lack of an established and widely-adopted approach toward thorough verification and validation of such algorithms in practical scenarios. We tackle this issue and propose a comprehensive validation approach that can shed more light on functional and non-functional abilities of the solvers. Additionally, we propose novel similarity metrics to measure the distance between the routing schedules that can be used in verifying the convergence abilities of randomized techniques. To reflect practical aspects of intelligent transportation systems, we introduce an algorithm for elaborating solvable benchmark instances for any vehicle routing formulation, alongside the set of quality metrics that help quantify the real-life characteristics of the delivery systems, such as their profitability. The experiments prove the flexibility of our approach through utilizing it to the NP-hard pickup and delivery problem with time windows, and present the qualitative, quantitative, and statistical analysis scenarios which help understand the capabilities of the investigated techniques. We believe that our efforts will be a step toward the more critical and consistent evaluation of emerging vehicle routing (and other) solvers, and will allow the community to easier confront them, thus ultimately focus on the most promising research avenues that are determined in the quantifiable and traceable manner.

Code and Data Repository for VRPSolverEasy: A Python Library for the Exact Solution of a Rich Vehicle Routing Problem

VRPSolverEasy is a Python package which provides a simple interface for VRPSolver, a state-of-the-art Branch-Cut-and-Price exact solver for vehicle routing problems (VRPs). The simplified interface is accessible for users without operations research background, meaning you do not need to know how to model your problem as an Integer Programming problem. As a price to pay for the simplicity, this interface is restricted to some standard VRP variants, which involve the following features and their combinations: capacitated vehicles customer time windows heterogeneous fleet multiple depots open routes optional customers with penalties parallel links to model transition time/cost trade-off incompatibilities between vehicles and customers customers with alternative locations and/or time windows To our knowledge, VRPSolver is the most efficient exact solver available for VRPs. Its particularity is to focus on finding and improving a lower bound on the optimal solution value of your instance. It is less efficient in finding feasible solutions but still can be better than available heuristic solvers for non-classic VRP variants.

VRPSolverEasy: A Python Library for the Exact Solution of a Rich Vehicle Routing Problem

The optimization community has made significant progress in solving vehicle routing problems (VRPs) to optimality using sophisticated branch-cut-and-price (BCP) algorithms. VRPSolver is a BCP algorithm with excellent performance in many VRP variants. However, its complex underlying mathematical model makes it hardly accessible to routing practitioners. To address this, VRPSolverEasy provides a Python interface to VRPSolver that does not require any knowledge of mixed integer programming modeling. Instead, routing problems are defined in terms of familiar elements, such as depots, customers, links, and vehicle types. VRPSolverEasy can handle several popular VRP variants and arbitrary combinations of them. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: This work was supported by Faperj [Grant E-26/202.887/2017] and Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grant 305684/2022-1]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0103 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0103 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The Fish Feed Production Routing Problem

This paper introduces the fish feed production routing problem (FFPRP) faced by Norwegian salmon feed producers. The FFPRP is comprised of a production scheduling problem and a rich vehicle routing problem (VRP) and thus denotes a variant of the integrated production scheduling and vehicle routing problem. We present a discrete time mixed integer programming (MIP) model of the FFPRP. Specifically, the model incorporates a multi-product, multi-trip, and multi-depot setting, where orders are produced at production lines at factories, and delivered within their respective time windows to customers by a heterogeneous fleet of vessels. The main objective is to minimize total costs, including production costs, routing costs, and costs of not delivering orders. We propose a heuristic, combining decomposition and the adaptive large neighborhood search (ALNS) heuristic, to solve the FFPRP. The decomposition-based ALNS heuristic is tested on a number of test instances which are generated based on vessel, factory, and order data from two of Norway’s largest fish feed producers. For small problem instances, for which the commercial MIP solver often is able to prove optimality, the average optimality gaps of the proposed heuristic are relatively small, while for the larger problem instances, the heuristic significantly outperforms the commercial MIP solver both in terms of solution quality and run time.

A Rich Vehicle Routing Problem for a City Logistics Problem

In this work, a Rich Vehicle Routing Problem (RVRP) is faced for solving city logistic problems. In particular, we deal with the problem of a logistic company that has to define the best distribution strategy for obtaining an efficient usage of vehicles and for reducing transportation costs while serving customers with different priority demands during a given planning horizon. Thus, we deal with a multi-period vehicle routing problem with a heterogeneous fleet of vehicles, with customers’ requirements and company restrictions to satisfy, in which the fleet composition has to be daily defined. In fact, the company has a fleet of owned vehicles and the possibility to select, day by day, a certain number of vehicles from the fleet of a third-party company. Routing costs must be minimized together with the number of vehicles used. A mixed integer programming model is proposed, and an experimental campaign is presented for validating it. Tests have been used for evaluating the quality of the solutions in terms of both model behavior and service level to grant to the customers. Moreover, the benefits that can be obtained by postponing deliveries are evaluated. Results are discussed, and some conclusions are highlighted, including the possibility of formulating this problem in such a way as to use the general solver proposed in the recent literature. This seems to be the most interesting challenge to permit companies to improve the distribution activities.

Tackling the rich vehicle routing problem with nature-inspired algorithms

In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.

A TSP-based nested clustering approach to solve multi-depot heterogeneous fleet routing problem

The distribution of goods and urban services has made the issue of vehicle routing of particular importance to researchers. Advanced Routing Vehicle (RVRP) Rich Vehicle Routing Problem As a hybrid optimization problem, it is widely used in many transportation and logistics planning. The approach of this paper is to present a heuristic method for solving the problem called Nested Clustering for Traveling Salesman Problem (NC-TSP), in this method to optimize the search space, we break the problem in consecutive space. In the first step, using the nearest neighbor (Knn) algorithm with the center of each depot, and then using the fuzzy C-means clustering method within each cluster obtained from the Knn method, to find the optimal set of nodes. Then we solve the problem using the extension of MILP linear functions to the heterogeneous nature of the transport fleet and the warehouses that supply the goods, using the optimization algorithm (GA). The proposed approach, despite its great complexity, solves the problem to a large extent and shows promising cost-effective results in the existing criteria.

C.H. Robinson Uses Heuristics to Solve Rich Vehicle Routing Problems

We consider a broad family of vehicle routing problem variants with many complex and practical constraints, known as rich vehicle routing problems, which are faced on a daily basis by C.H. Robinson (CHR). Because CHR has many customers, each with distinct requirements, various routing problems with different objectives and constraints must be solved. We propose a novel framework for solving rich vehicle routing problems, which we demonstrate is effective in solving a variety of different problems. This framework, along with a simple user interface, has been wrapped into a new module and integrated into the company’s transportation planning and execution technology platform. Since its implementation, this new module has outperformed the previously used third-party technologies at CHR, significantly reduced setup times, and improved users’ productivity as well as customer outcomes.

Toward a more flexible VRP with pickup and delivery allowing consolidations

One important requirement of modern supply chain management is the frequent exchange of containers via multiple cross-docks which requires spatial and time synchronisations between different types of vehicle. Moreover, as collaborations in logistics between several companies become popular, more flexible and extended models must be solved to consider the different needs of the companies. This is of high importance in a new logistics concept, the Physical Internet, which is expected to considerably improve the way logistics are handled in the current supply chain management.To optimise the aforementioned requirements, a rich vehicle routing problem with pickup and delivery including numerous attributes is modelled and solved. A mathematical formulation is proposed and implemented in CPLEX to solve the problem. Given the complexity of the problem, solving large instances with exact methods is very time-consuming. Therefore, a multi-threaded meta-heuristic based on Simulated Annealing is developed. A set of new operators coupled with a restart strategy and memory are developed to help improve the performance.Computational results on a generated data-set showed that the proposed meta-heuristic is superior to the CPLEX solver in terms of solvability and computational time. The proposed meta-heuristic was also compared with the best-known results by current state-of-the-art methods on a classical benchmark on pickup and delivery problems with time windows (with up to 200 customers). The experimental results showed that the proposed method was able to match the best-known results in many of these large scale instances

Integrated decision support system for rich vehicle routing problems

Recent economic and environmental constraints push supply chain management systems to adopt closed-loop supply chain operating modes that have to address very complex problems including the end-user quality of services, environmental considerations, and daily transportation time variations. Relevant and challenging research areas require a proper coordination between the data provider software (Transport Management Software) and the operational research tool in charge of trip definition.This paper proposes a decision support system applied to the Vehicle Routing Problem able to tackle very large instances with real-life constraints. Our contribution is to propose an architecture that handle both static resolution prior to the completion of routes and update them in a dynamical context during their completions. This is implemented through a REST based API using numerous state-of-the-art operational research methods. Moreover, this system in used in practice by the Mapotempo company.

The secure time-dependent vehicle routing problem with uncertain demands

This paper addresses the transportation of valuable goods in which security carriers are interested to optimize the operating costs and the vehicle routes’ security. In real operations, route planning and scheduling are normally performed manually based on individual experience. Hereupon, an optimizing approach should be developed to target the robbery risk reduction and the distribution network’ operational costs. Facing the dangerous nature of operations, some considerations play an important role, including demand fluctuations and traffic congestion of the urban environment. To handle these issues, we respectively benefit from the robust optimization theory and considering time-dependent travel speeds with satisfying the “first-in-first-out” property. This paper proposes a rich vehicle routing problem denoted by the secure time-dependent vehicle routing problem with time windows including pickup and delivery with uncertain demands (S-TD-VRPTWPD-UD). A mathematical formulation and an efficient solution approach combining the greedy randomized adaptive search procedure (GRASP) and the iterated local search (ILS) are developed to minimize the predictability of route plans using an integrated risk index, besides the travel costs. Extensive computational experiments on this problem are performed to analyze the impact of the demand uncertainty and the speed time-dependency and to show the efficiency of the GRASP×ILS implementation. The results show the significant improvements due to the time-dependency as well as the extra cost of protecting the model against the worst-case scenario of demand requests by deriving the robust counterpart of the S-TD-VRPTWPD-UD.

An Agile and Reactive Biased-Randomized Heuristic for an Agri-Food Rich Vehicle Routing Problem

Operational problems in agri-food supply chains usually show characteristics that are scarcely addressed by traditional academic approaches. These characteristics make an already NP-hard problem even more challenging; hence, this problem requires the use of tailor-made algorithms in order to solve it efficiently. This work addresses a rich vehicle routing problem in a real-world agri-food supply chain. Different types of animal food products are distributed to raising-pig farms. These products are incompatible, i.e., multi-compartment heterogeneous vehicles must be employed to perform the distribution activities. The problem considers constraints regarding visit priorities among farms, and not-allowed access of large vehicles to a subset of farms. Finally, a set of flat tariffs are employed to formulate the cost function. This problem is solved employing a reactive savings-based biased-randomized heuristic, which does not require any time-costly parameter fine-tuning process. Our results show savings in both cost and traveled distance when compared with the real supply chain performance.

An exact solution method for a rich helicopter flight scheduling problem arising in offshore oil and gas logistics

This paper studies the problem of creating an optimal flight schedule for a heterogeneous fleet of helicopters tasked with transporting personnel to, from, and between offshore installations. The problem can be modelled as a rich vehicle routing problem and combines the following properties from the vehicle routing literature: pickup and delivery structure, heterogeneous fleet operating out of multiple depots, multi-trip, and temporal synchronization of transportation tasks. We present compact and extended mathematical models of the problem, where the extended model is based on generating all trips apriori. When solving the extended model we apply delayed constraint generation (DCG) to parts of the model to speed up the solution process. Computational results are presented that show that the extended formulation and solution method can solve realistic instances of the problem within one hour. The results further show that the DCG method works significantly better than using lazy constraints from a commercial solver, especially when the number of transportation tasks requiring temporal synchronization becomes large.

A cooperative rich vehicle routing problem in the last-mile logistics industry in rural areas

This study proposes a cooperative rich vehicle routing problem (CoRVRP) among three typical logistics providers in the last-mile logistics industry in rural areas. The problem is formulated based on the information fusion system and the ordering interface system, and it is solved by a proposed branch-price-and-cut algorithm. Based on the optimized cooperation scheme, the core and Shapley value are used to fairly allocate the cost savings. The numerical results show that a long-term grand coalition will be beneficial to all the participators. The proposed information-merging-and-algorithm-driven cooperative optimization model will be practicable for intelligent logistics systems.

Exact and hyper‐heuristic solutions for the distribution‐installation problem from the VeRoLog 2019 challenge

AbstractThis work tackles a rich vehicle routing problem (VRP) problem integrating a capacitated vehicle routing problem with time windows (CVRPTW), and a service technician routing and scheduling problem (STRSP) for delivering various equipment based on customers' requests, and the subsequent installation by a number of technicians. The main objective is to reduce the overall costs of hired resources, and the total transportation costs of trucks/technicians. The problem was the topic of the fourth edition of the VeRoLog Solver Challenge in cooperation with the ORTEC company. Our contribution to research is the development of a mathematical model for this problem and a novel hyper‐heuristic algorithm to solve the problem based on a population of solutions. Experimental results on two datasets of small and real‐world size revealed the success of the hyper‐heuristic approach in finding optimal solutions in a shorter computational time, when compared to our exact model. The results of the large size dataset were also compared to the results of the eight finalists in the competition and were found to be competitive, proving the potential of our developed hyper‐heuristic framework.

On the design of hybrid bio‐inspired meta‐heuristics for complex multiattribute vehicle routing problems

AbstractThis paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta‐heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta‐heuristics to solve the problem. As such ACO‐GA is a hybrid meta‐heuristic using ACO as main approach and GA as local search. GA‐ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO‐GA approach outperforms the others. Considering the cost, ACO, GA, and GA‐ACO provide better results. Regarding computation time, GA and GA‐ACO have been found the most competitive among the benchmark.

A MILP model for the selective solid waste collection routing problem

Nowadays, in the European Union selective solid waste management be-longs to important responsibilities of municipalities. In Solid Waste Management (SWM) the main operational task is to set a schedule for solid waste collection and to find optimal routes for garbage trucks so that the total costs of solid waste collection service can be minimized subject to a series of constraints which guarantee not only fulfillment of SWM’s obligations but also desirable level of quality of that service. Optimization problem of garbage trucks routing is a special case of rich Vehicle Routing Problem as it has to cover following constraints: pickup nodes (clients) must be visited during their predefined time windows; the number and capacity of depots and specialized sorting units can-not be exceeded; each garbage truck can be assigned to at most one depot; each route should be dedicated to collecting one type of segregated solid waste, and the route must be served by a garbage truck which can collect that type of solid waste; availability of garbage trucks and their drivers must be respected; each garbage truck must be drained at a specialized sorting unit before going back to the depot. This paper contributes with a new Mixed-Integer Programming (MIP) model for the Selective Solid Waste Collection Routing Problem (SS-WCRP) with time windows, limited heterogeneous fleet, and different types of segregated solid waste to be collected separately. Utilization of MIP for solving small-sized instance of the Fleet Optimization Problem for Selective Solid Waste Collection (FOPSSWC) is and obtained results are reported.