Towards Multi-Agent Simulation of the Dynamic Vehicle Routing Problem in MATSim

The paper presents the idea and the initial outcomes of integrating MATSim, a multi-agent transport simulation system, with the DVRP Optimizer, an application for solving the Dynamic Vehicle Routing Problem. First, the justification for the research is given and the state of the art is outlined. Then, MATSim is presented with a short description of the recent results in areas related to the one proposed in the paper, followed up by the discussion on the DVRP Optimizer functionality, architecture and implemented algorithms. Next, the process of integrating MATSim and the DVRP Optimizer is presented, with the distinction of two phases, the off-line and on-line optimization. Then, a description of the off-line optimization is given along with the results obtained for courier and taxi services in urban areas. The paper ends with conclusions and future plans

Analysis of the characteristics and applications associated to the dynamic vehicle routing problem - DVRP

El Problema del Ruteo Dinámico de Vehículos - DVRP, permite analizar sistemas con la inclusión de una variable de carácter dinámico, ajustando el ruteo en función de nuevas restricciones y comportamientos a nivel de desarrollo de dimensiones temporales y desarrollo constructivo con información en tiempo real. Este problema se ha clasificado en diferentes sistemas, de acuerdo a su aplicabilidad y algoritmos de solución, además del efecto del dinamismo presente. Sin embargo, no todas las características y diferencias frente al ruteo estocástico clásico, han sido mencionadas y resaltadas, debido a su reciente desarrollo, así como la limitada investigación desarrollada. Por tal motivo el presente artículo, plantea la realización de un análisis de las principales características y aplicaciones asociadas a los problemas de ruteo dinámico de vehículos., a través de una revisión bibliográfica con el propósito de brindar información acerca de las características principales, fortalezas respecto al problema clásico y sus aplicaciones para solución. La metodología empleada, incluye una investigación cualitativa, basada en la búsqueda sistemática en bases de datos acerca del DVRP, en últimos cuatro años (2011-2014). Se concluye que el problema de ruteo dinámico de vehículos, permite establecer y analizar sistemas de ruteo, con la inclusión de una variable de carácter dinámico, permitiendo la aplicación y ajuste de heurísticas y metaheurísticas, permitiendo abarcar nuevos sistemas de análisis a nivel logístico. De la misma manera se evidencia que existe un comportamiento variable con tendencia a la baja, en referencia al número de publicaciones relacionadas con el tema, reflejando, un potencial de investigación y desarrollo inexplorado en referencia a la aplicación y ajuste de la temáticaThe Dynamic Vehicle Routing Problem- DVRP allows analyzing systems with the inclusion of a dynamic variable, adjusting the routing in function of new restrictions and behaviors at the development level of temporal dimensions and constructive development with real-time information. This problem has been classified into different systems, according to their applicability and solution algorithms, besides the current dynamic effect. However, not all features and differences compared to classical stochastic routing have been mentioned and highlighted because of their recent development, as well as limited research developed. Therefore, the present article proposes to carry out an analysis about the main features and applications associated with the dynamic routing vehicle problem, through a literature review with the purpose of providing information about the main characteristics, strengths compared to the classical problem and its applications to solution. The methodology includes a qualitative research based on a systematic search in databases about DVRP in last four years (2011-2014). As main conclusion, is related that the DVRP allows establishing and analyzing routing systems, with the inclusion of a variable dynamic, allowing the application and set of heuristics and metaheuristics, allowing embrace new analysis systems in a logistical level. Likewise, it is evident that there is a variable behavior downtrend, referring to the number of publications related to the theme, reflecting unexplored potential in research and development in reference to the application and setting the them

Benchmarking minimum passenger waiting time in online taxi dispatching with exact offline optimization methods

This paper analyses the use of exact offline optimization methods for benchmarking online taxi dispatching strategies where the objective is to minimize the total passenger waiting time. First, a general framework for simulating dynamic transport services in MATSim (Multi-Agent Transport Simulation) is described. Next, the model of online taxi dispatching is defined, followed by a formulation of the offline problem as a mixed integer programming problem. Three benchmarks based on the offline problem are presented and compared to two simple heuristic strategies and a hypothetical simulation with teleportation of idle taxis. The benchmarks are evaluated and compared using the simulation scenario of taxi services in the city of Mielec. The obtained (approximate) lower and upper bounds for the minimum total passenger waiting time indicate directions for further research

On the Development of a Multilayered Agent-based Heuristic System for Vehicle Routing Problem under Random Vehicle Breakdown

With the recent technological advancement, the Dynamic Vehicle Routing Problem (DVRP) is becoming more applicable but almost all of the research in this field limited the source of dynamism from the order side rather from the vehicle, in addition to the adoption of inflexible tools that are mainly designed for the static problem. Considering multiple random vehicle breakdowns complicates the problem of how to adapt and distribute the workload to other functioning vehicles. In this ongoing PhD research, a proposed multi-layered Agent-Based Model (ABM) along with a modelling framework on how to deal with such disruptive events in a reactive continuous manner. The model is partially constructed and experimented, with a developed clustering rule, on two randomly generated scenario for the purpose of validation. The rule achieved good order allocation to vehicles and reacted to different problem sizes by rejecting orders that are over the model capacity. This shows a promising path in fully adopting the ABM model in this dynamic problem

Potential Travel Time Reduction with Autonomous Vehicles for Different Types of Travellers

Autonomous Vehicles (AVs) have been designed to make changes in the travel behaviour of travellers. These changes can be interpreted using transport models and simulation tools. In this study, the daily activity plans were used to study the possibility of increasing the utility of travellers through minimizing the travel time by using AVs. Three groups of travellers were selected based on the benefits that they can obtain when AVs are on the market. The groups are (a) long-trip travellers (b) public transport riders, and (c) travellers with specified characteristics. Each group is divided into one or more scenarios based on the definition of each group and the collected data. A total of seven scenarios were derived from the collected data and simulated twice to include the existing transport modes and the presence of AVs. The simulations were conducted using Multi-Agents Transport Simulation (MATSim) that applies the concept of a co-evolutionary algorithm. MATSim simulates the current plans and the ones where AVs replace all or part of the existing conventional transport modes in the daily activity plans. The results have shown a reduction in the trip time: 13% to 42% for group (a), 33% for group (b), and 16% to 28% for group (c) compared with the original trip times. In conclusion, it can be claimed that AVs could reduce the travel time in all cases, which provides benefits for people to increase their utilities

Agent based simulation of the dial-a-flight problem

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Master of Science in Engineering. Johannesburg, May 2018Agent based simulation and modelling (ABSM) has been noted as a novel method in solving complex problems. This dissertation makes use of the ABSM method in conjunction with a Genetic Algorithm to find good solutions to the dial-a-flight problem. The task is to generate a schedule for a heterogeneous fleet of aircraft, with the objective to reduce operational cost but maintain customer satisfaction. By making use of booking list data from an air taxi business, operating in the Okavango Delta, two agent based models were designed, the first makes use of multi-criteria decision analysis (MCDA) and the other a method proposed by Campbell [7], to test their effectiveness against either upper bound or manual solutions. The solution quality varied between tests, with booking list sizes between 10 and 200 requests producing improvements to the upper bound and manual results with a mean improvement from the benchmarks of 1.61\%. The method could also be refined further by adopting improvement mechanisms to final schedules or by making use of retrospective decision making aided by self learning techniques.MT 201

The future of fully automated vehicles : opportunities for vehicle- and ride-sharing, with cost and emissions savings

textFully automated or autonomous vehicles (AVs) hold great promise for the future of transportation, with Google and other auto manufacturers intending on introducing self-driving cars to the public by 2020. New automation functionalities will produce dramatic transportation system changes, across safety, mobility, travel behavior, and the built environment. This work’s results indicate that AVs may save the U.S. economy up to $37.7 billion from safety, mobility and parking improvements at the 10$447.1 billion with 90% market penetration. With only 10% market share, over 1,000 lives could be saved annually. However, realizing these potential benefits while avoiding pitfalls requires overcoming significant barriers including AV costs, liability, security, privacy, and missing research. Additionally, once fully self-driving vehicles can safely and legally drive unoccupied, a new personal travel transportation mode looks set to arrive. This new mode is the shared automated vehicle (SAV), combining on-demand service features with self-driving capabilities. This work simulates a fleet of SAVs operating within Austin, Texas, first using an idealized grid-based representation, and next using Austin’s actual transportation network and travel demand flows. This second model incorporates dynamic ride-sharing (DRS), allowing two or more travelers with similar origins, destinations and departure times to share a ride. Model results indicate that each SAV could replace around 10 conventionally-owned household vehicles while serving over 56,000 person-trips. SAVs’ ability to relocate unoccupied between serving one traveler and the next may cause an increase of 7-10% more travel; however, DRS can result in reduced overall VMT, given enough SAV-using travelers willing to ride-share. Furthermore, using DRS results in overall lower wait and service times for travelers, particularly from pooling rides during peak demand. SAVs should produce favorable emissions outcomes, with an estimated 16% less energy use and 48% lower volatile organic compound (VOC) emissions, per person-trip compared to conventional vehicles. Finally, assuming SAVs cost $70,000 each, an SAV fleet in Austin could provide a 19$1 per trip-mile served. In summary, this new paradigm holds much promise that technological advances may soon realized.Civil, Architectural, and Environmental Engineerin