The Merit of Agents in Freight Transport

The Merit of Agents in Freight Transport

Tamás Máhr (Almende/TU Delft, The Netherlands), F. Jordan Srour (Erasmus University, The Netherlands), Mathijs de Weerdt (TU Delft, The Netherlands) and Rob Zuidwijk (Erasmus University, The Netherlands)
Copyright: © 2009 |Pages: 19
DOI: 10.4018/978-1-60566-226-8.ch016
OnDemand PDF Download:


While intermodal freight transport has the potential to introduce efficiency to the transport network,this transport method also suffers from uncertainty at the interface of modes. For example, trucks moving containers to and from a port terminal are often uncertain as to when exactly their container will be released from the ship, from the stack, or from customs. This leads to much difficulty and inefficiency in planning a profitable routing for multiple containers in one day. In this chapter, the authors examine agent-based solutions as a mechanism to handle job arrival uncertainty in the context of a drayage case at the Port of Rotterdam. They compare their agent-based solution approach to a wellknown on-line optimization approach and study the comparative performance of both systems across four scenarios of varying job arrival uncertainty. The chapter concludes that when less than 50% of all jobs are known at the start of the day then an agent-based approach performs competitively with an on-line optimization approach.
Chapter Preview


Scheduling the routes of trucks to pick-up and deliver containers is a complex problem. In general such Vehicle Routing Problems (VRPs) are known to be NP-complete and therefore inherently hard and time consuming to solve to optimality (Toth & Vigo, 2002). Fortunately, these problems have a structure that can facilitate efficient derivation of feasible (if not optimal) solutions. Specifically, the routes of different trucks are more or less independent. Such “locality” in a problem is a first sign that an agent-based approach may be viable.

Modeling and solving a VRP by coordinating a set of agents can bring a number of advantages over more established approaches in the field of operations research even when using state of-the-art mixed integer solvers such as CPLEX (ILOG, Inc., 1992). Agent advantages include the possibility for distributed computation, the ability to deal with proprietary data from multiple companies, the possibility to react quickly on local knowledge (Fischer et al., 1995), and the capacity for mixed-initiative planning (Bürckert et al., 2000).

In particular, agents have been shown to perform well in uncertain domains. That is, in domains where the problem is continually evolving (Fischer et al., 1995). In the VRP, for example, a very basic form of uncertainty is that of job arrivals over time. To the best of our knowledge, however, the effect of even this basic level of uncertainty on the performance of agent-based planning in a realistic logistics problem has never been shown.

We think it is safe to assume, based on its long history, that current practice in operations research (OR) outperforms agent-based approaches in settings where all information is known in advance (static settings). However, in situations with a high level of uncertainty, agent-based approaches are expected to outperform these traditional methods (Jennings & Bussmann, 2003).

In this chapter we investigate whether a distributed agent-based planning approach indeed suffers less from job arrival uncertainty than a centralized optimization-based approach. Our main contribution is to determine at which level of job arrival uncertainty agent-based planning outperforms on-line operations research methods. These results can help transportation companies decide when to adopt an agent-based approach, and when to use an on-line optimization tool, depending on the level of uncertainty job arrivals exhibit in their daily business.

In the next section we provide a survey of current work on agent-based approaches to logistics problems. We then introduce the case of a transportation company near the port of Rotterdam. Based on this literature review and the specific nature of our case study VRP, we propose a state-of-the-art agent-based approach where orders are auctioned among trucks in such a way that each order is assigned to the truck that can most efficiently transport the container. Moreover, these trucks continuously negotiate among each other to exchange orders as the routing situation evolves. This agent-based approach is fully described in this chapter. We follow this description with a description of the centralized on-line optimization approach used in comparison to our distributed agent-based system. The structure of our test problems and the computational results are covered in the next to last section. In the final section we discuss the consequences of our results, summarize our advice to transportation companies, and give directions for future work.

Complete Chapter List

Search this Book:
List of Reviewers
Table of Contents
Ana Bazzan, Franziska Klügl
Ana Bazzan, Franziska Klügl
Chapter 1
Takeshi Takama
This study discusses adaptation effects and congestion in a multi-agent system (MAS) to analyse real transport and traffic problems. Both... Sample PDF
Adaptation and Congestion in a Multi-Agent System to Analyse Empirical Traffic Problems: Concepts and a Case Study of the Road User Charging Scheme at the Upper Derwent
Chapter 2
Qi Han, Theo Arentze, Harry Timmermans, Davy Janssens, Geert Wets
Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling... Sample PDF
A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns
Chapter 3
Michael Balmer, Marcel Rieser, Konrad Meister, David Charypar, Nicolas Lefebvre, Kai Nagel
Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In... Sample PDF
MATSim-T: Architecture and Simulation Times
Chapter 4
Ulf Lotzmann
In this chapter an agent-based traffic simulation approach is presented which sees agents as individual traffic participants moving in an artificial... Sample PDF
TRASS: A Multi-Purpose Agent-Based Simulation Framework for Complex Traffic Simulation Applications
Chapter 5
Paulo A.F. Ferreira, Edgar F. Esteves, Rosaldo J.F. Rossetti, Eugénio C. Oliveira
Trading off between realism and too much abstraction is an important issue to address in microscopic traffic simulation. In this chapter the authors... Sample PDF
Applying Situated Agents to Microscopic Traffic Modelling
Chapter 6
Andreas Schadschneider, Hubert Klüpfel, Tobias Kretz, Christian Rogsch, Armin Seyfried
Multi-Agent Simulation is a general and powerful framework for understanding and predicting the behaviour of social systems. Here the authors... Sample PDF
Fundamentals of Pedestrian and Evacuation Dynamics
Chapter 7
Rex Oleson, D. J. Kaup, Thomas L. Clarke, Linda C. Malone, Ladislau Bölöni
The “Social Potential”, which the authors will refer to as the SP, is the name given to a technique of implementing multi-agent movement in... Sample PDF
"Social Potential" Models for Modeling Traffic and Transportation
Chapter 8
Sabine Timpf
In this chapter, the authors present a methodology for simulating human navigation within the context of public, multi-modal transport. They show... Sample PDF
Towards Simulating Cognitive Agents in Public Transport Systems
Chapter 9
Kurt Dresner, Peter Stone, Mark Van Middlesworth
Fully autonomous vehicles promise enormous gains in safety, efficiency, and economy for transportation. In previous work, the authors of this... Sample PDF
An Unmanaged Intersection Protocol and Improved Intersection Safety for Autonomous Vehicles
Chapter 10
Heiko Schepperle, Klemens Böhm
Current intersection-control systems lack one important feature: They are unaware of the different valuations of reduced waiting time of the... Sample PDF
Valuation-Aware Traffic Control: The Notion and the Issues
Chapter 11
Charles Desjardins, Julien Laumônier, Brahim Chaib-draa
This chapter studies the use of agent technology in the domain of vehicle control. More specifically, it illustrates how agents can address the... Sample PDF
Learning Agents for Collaborative Driving
Chapter 12
Kagan Tumer, Zachary T. Welch, Adrian Agogino
Traffic management problems provide a unique environment to study how multi-agent systems promote desired system level behavior. In particular, they... Sample PDF
Traffic Congestion Management as a Learning Agent Coordination Problem
Chapter 13
Matteo Vasirani, Sascha Ossowski
The problem of advanced intersection control is being discovered as a promising application field for multiagent technology. In this context... Sample PDF
Exploring the Potential of Multiagent Learning for Autonomous Intersection Control
Chapter 14
Tomohisa Yamashita, Koichi Kurumatani
With maturation of ubiquitous computing technology, it has become feasible to design new systems to improve our urban life. In this chapter, the... Sample PDF
New Approach to Smooth Traffic Flow with Route Information Sharing
Chapter 15
Denise de Oliveira, Ana L.C. Bazzan
In a complex multiagent system, agents may have different partial information about the system’s state and the information held by other agents in... Sample PDF
Multiagent Learning on Traffic Lights Control: Effects of Using Shared Information
Chapter 16
Tamás Máhr, F. Jordan Srour, Mathijs de Weerdt, Rob Zuidwijk
While intermodal freight transport has the potential to introduce efficiency to the transport network,this transport method also suffers from... Sample PDF
The Merit of Agents in Freight Transport
Chapter 17
Lawrence Henesey, Jan A. Persson
In analyzing freight transportation systems, such as the intermodal transport of containers, often direct monetary costs associated with... Sample PDF
Analyzing Transactions Costs in Transport Corridors Using Multi Agent-Based Simulation
Chapter 18
Shawn R. Wolfe, Peter A. Jarvis, Francis Y. Enomoto, Maarten Sierhuis, Bart-Jan van Putten
Today’s air traffic management system is not expected to scale to the projected increase in traffic over the next two decades. Enhancing... Sample PDF
A Multi-Agent Simulation of Collaborative Air Traffic Flow Management
About the Contributors