Real-world Routing Problems Research Articles

Multi-depot dispatch deployment analysis on classifying preparedness phase for flood-prone coastal demography in Sarawak

Multi-Depot VRP (MDVRP) is a metaheuristic approach with concurrent vehicle rendezvous across various depots within a demanded regulation, where the task assignment would eventually end up at the same initial depot. A review of the relief commodities distribution patterns among flood-prone areas in the underlying layouts of Sarawak residential areas has been conducted in retrospect and in light of common real-world routing problems. The purpose of this research is to demonstrate the benefits of multi-path route selection in task distribution to cater to simultaneous demands for adhering to strict constraint settings, including load dispatch dynamism and deployed vehicle quantities. Shortest path algorithms are improvised as an alternative to select the most optimum traveled routes during relief commodity distribution. This is done by determining critical allocation nodes, where solution steps are optimized using a genetic algorithm with predefined parameters. The experimental output displays the strong correlation between the number of prioritized customers and assigned depots to optimize the route complexity and natural affluence on generated final solution cost. The approach is seen as viable for further addressing problem-specific instances in vehicle routing problems such as adjusting parameter settings to generate rapid solution steps, including pathfinding shortest coverage distance and sorting out trade-offs between space covered and the time limitations of task distribution efforts.

2021 Amazon Last Mile Routing Research Challenge: Data Set

The 2021 Amazon Last Mile Routing Research Challenge, hosted by Amazon.com’s Last Mile Research team, and scientifically supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics, prompted participants to leverage real operational data to find new and better ways to solve a real-world routing problem. In this article, we describe the data set released for the research challenge, which includes route-, stop-, and package-level features for 9,184 historical routes performed by Amazon drivers in 2018 in five metropolitan areas in the United States. This real-world data set excludes any personally identifiable information: all route and package identifiers have been randomly regenerated and related location data have been obfuscated to ensure anonymity. Although multiple synthetic benchmark data sets are available in the literature, the data set of the 2021 Amazon Last Mile Routing Research Challenge is the first large and publicly available data set to include instances based on real-world operational routing data. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems.

A Scalable Many-Objective Pathfinding Benchmark Suite

Route planning also known as pathfinding is one of the key elements in logistics, mobile robotics and other applications, where engineers face many conflicting objectives. However, most of the current route planning algorithms consider only up to three objectives. In this paper, we propose a scalable many-objective benchmark problem covering most of the important features for routing applications based on real-world data. We define five objective functions representing distance, traveling time, delays caused by accidents, and two route specific features such as curvature and elevation. We analyse several different instances for this test problem and provide their true Pareto-front to analyse the problem difficulties. We apply three well-known evolutionary multi-objective algorithms. Since this test benchmark can be easily transferred to real-world routing problems, we construct a routing problem from OpenStreetMap data. We evaluate the three optimisation algorithms and observe that we are able to provide promising results for such a real-world application. The proposed benchmark represents a scalable many-objective route planning optimisation problem enabling researchers and engineers to evaluate their many-objective approaches.

Machine learning based simulation optimisation for urban routing problems

Many real world routing problems, including those in tourism and surveillance, can be formulated as team orienteering problems. The goal in such problems is to maximise the rewards collected by a fleet of vehicles whose routes must be completed within a time limit. This work considers a team orienteering problem set within a traffic simulation. In the stochastic environment of a road network, travel times depend on network structure, the demands of road users, driver behaviour and the congestion that arises from these. As a result travel times are difficult to predict. In this work a learnheuristic solution approach is proposed. Learnheuristics integrate machine learning and optimisation for solving combinatorial problems with inherent parameter learning problems—in this case travel times. The machine learning component is used to predict travel times based on data obtained from a limited budget of traffic simulation runs, a budget that is used within the run-time learnheuristic algorithm. In each iteration of the learnheuristic, the optimisation component utilises the travel time predictions of the machine learning algorithm to rapidly generate candidate solutions. The strongest candidate is tested in the traffic simulator, and the results of which are used to train the machine learning component. In a range of test instances, the effectiveness of different combinations of machine learning and optimisation components are tested. Experiments reveal that different combinations of machine learning and optimisation components produce solutions with different characteristics in terms of total reward and reliability. Local search followed by biased randomisation combined with a neural network was found to be effective in multiple instances. The question of how best to use the run-time of a learnheuristic is also addressed.

Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization

This paper presents the first results of an agent-based model aimed at solving a Capacitated Vehicle Routing Problem (CVRP) for inbound logistics using a novel Ant Colony Optimization (ACO) algorithm, developed and implemented in the NetLogo multi-agent modelling environment. The proposed methodology has been applied to the case study of a freight transport and logistics company in South Italy in order to find an optimal set of routes able to transport palletized fruit and vegetables from different farms to the main depot, while minimizing the total distance travelled by trucks. Different scenarios have been analysed and compared with real data provided by the company, by using a set of key performance indicators including the load factor and the number of vehicles used. First results highlight the validity of the method to reduce cost and scheduling and provide useful suggestions for large-size operations of a freight transport service.

Constrained shortest path problems in bi-colored graphs: a label-setting approach

Definition of an optimal path in the real-world routing problems is not necessarily the shortest one, because parameters such as travel time, safety, quality, and smoothness also played essential roles in the definition of optimality. In this paper, we use bi-colored graphs for modeling urban and heterogeneous environments and introduce variations of constraint routing problems. Bi-colored graphs are a kind of directed graphs whose vertices are divided into two subsets of white and gray. We consider two criteria, minimizing the length and minimizing the number of gray vertices and present two problems called gray vertices bounded shortest path problem and length bounded shortest path problem on bi-colored graphs. We propose an efficient time label-setting algorithm to solve these problems. Likewise, we simulate the algorithm and compare it with the related path planning methods on random graphs as well as real-world environments. The simulation results show the efficiency of the proposed algorithm.

Individually Optimized Commercial Road Transport: A Decision Support System for Customizable Routing Problems

The Vehicle Routing Problem (VRP) in its manifold variants is widely discussed in scientific literature. We investigate related optimization models and solution methods to determine the state of research for vehicle routing attributes and their combinations. Most of these approaches are idealized and focus on single problem-tailored routing applications. Addressing this research gap, we present a customizable VRP for optimized road transportation embedded into a Decision Support System (DSS). It integrates various model attributes and handles a multitude of real-world routing problems. In the context of urban logistics, practitioners of different industries and researchers are assisted in efficient route planning that allows for minimizing driving distances and reducing vehicle emissions. Based on the design science research methodology, we evaluate the DSS with computational benchmarks and real-world simulations. Results indicate that our developed DSS can compete with problem-tailored algorithms. With our solution-oriented DSS as final artifact, we contribute to an enhanced economic and environmental sustainability in urban logistic applications.

Solving the assignment of customers to trucks and visiting days in a periodic routing real-world case

Introduction: this work proposes a model, and two heuristic algorithms to assign customers to trucks and visiting days, as a first phase in the solution of a real-world routing problem, which is closely related to the Periodic vehicle routing problem, but a strategic decision of the company imposes the additional constraint that every customer must always be visited by the same truck. Methods: The proposed model aims to group the customers that are visited the same day by the same truck as close as possible. The first proposed heuristic has a constructive stage, and five underlying improvement heuristic, the second one uses an exact linear programming algorithm. Results: The algorithms are evaluated by instances taken from the literature and generated, taking into account the characteristics presented in the real-world case addressed.

A MapReduce-based approach for shortest path problem in large-scale networks

The cloud computing allows to use virtually infinite resources, and seems to be a new promising opportunity to solve scientific computing problems. The MapReduce parallel programming model is a new framework favoring the design of algorithms for cloud computing. Such framework favors processing of problems across huge datasets using a large number of heterogeneous computers over the web. In this paper, we are interested in evaluating how the MapReduce framework can create an innovative way for solving operational research problems. We proposed a MapReduce-based approach for the shortest path problem in large-scale real-road networks. Such a problem is the cornerstone of any real-world routing problem including the dial-a-ride problem (DARP), the pickup and delivery problem (PDP) and its dynamic variants. Most of efficient methods dedicated to these routing problems have to use the shortest path algorithms to construct the distance matrix between each pair of nodes and it could be a time-consuming task on a large-scale network due to its size. We focus on the design of an efficient MapReduce-based approach since a classical shortest path algorithm is not suitable to accomplish efficiently such task. Our objective is not to guarantee the optimality but to provide high quality solutions in acceptable computational time. The proposed approach consists in partitioning the original graph into a set of subgraphs, then solving the shortest path on each subgraph in a parallel way to obtain a solution for the original graph. An iterative improvement procedure is introduced to improve the solution. It is benchmarked on a graph modeling French road networks extracted from OpenStreetMap. The results of the experiment show that such approach achieves significant gain of computational time.

An interactive optimization approach to a real-world oceanographic campaign planning problem

In this paper we deal with a real-world routing problem that consists of finding the best route for an oceanographic campaign. The planning task involves setting up the initial route and managing the route variations along the way. Bearing in mind the idea of building an experimental tool to support the decision making process of the expert when planning the route, two different approaches to solve the problem were developed. First of all, we assumed that the problem was well characterized by the initial description of the expert, using as fitness function the time employed in the route and applying classical local search optimization heuristics. The difficulty of a precise constraint definition and the infeasibility of mathematically describing all the evaluation criteria of the expert led us towards interactive optimization as a way to introduce the knowledge of the expert. According to the results, this last approach was able to provide satisfactory solutions.

A Time-of-Day Model for Aircraft Scheduling

This paper describes an aircraft scheduling model which accounts for indivisibilities in production (airplanes, flights), capacity costs due to peak demand, and consumer preferences regarding the time of day flights are scheduled. Application of the model to real-world routing problems is not intended, as the scope is limited to a system comprised of a single city pair. However, the model does make possible a detailed examination of the economic welfare aspects of various routing criteria and the degree of realism in the assumptions made. Cost functions formulated from CAB data, along with hypothesized characteristics of air travel demand, form a data base for the model. Results are included to show the effect on economic welfare of utilizing a model which fails to incorporate passenger preferences as to the time of day flights are scheduled.