Linda Ramstedt

TAPAS: A multi-agent-based model for simulation of transport chains

We present the Transportation And Production Agent-based Simulator (TAPAS), which is an agent-based model for simulation of transport chains that can be used, e.g., for analysis of transport-related policy and infrastructure measures. TAPAS is more powerful than traditional approaches to freight transport analysis, as it explicitly models production and customer demand, and it captures the interaction between individual transport chain actors, their heterogeneity and decision making processes, as well as time aspects. Whereas traditional approaches rely on assumed statistical correlation, TAPAS relies on causality, i.e., the focus is on the decisions and negotiations that lead to activities. TAPAS is composed of two connected layers, one that simulates the physical activities, e.g., production and transportation, and one that simulates the decision making and interaction between actors. We illustrate TAPAS with a scenario in which the consequences of three transport policy and infrastructure measures are studied.

Agent-Based Approaches to Transport Logistics

This paper provides a survey of existing research on agent-based approaches to transportation and traffic management. A framework for describing and assessing this work will be presented and systematically applied. We are mainly adopting a logistical perspective, thus focusing on freight transportation. However, when relevant, work of traffic and transport of people will be considered. A general conclusion from our study is that agent-based approaches seem very suitable for this domain, but that this still needs to be verified by more deployed system.

A hybrid micro-simulator for determining the effects of governmental control policies on transport chains

A simulation-based tool is described which will be used to investigate how the actors in a transport chain are expected to act when different types of governmental control policies are applied, such as, fuel taxes, road tolls, vehicle taxes and requirements on vehicles. The simulator is composed of two layers, one layer simulating the physical activities taking place in the transport chain, e.g., production, storage, and transports of goods, and another layer simulating the different actors’ decision making processes. The decision layer is implemented by a multi-agent system where each agent corresponds to a particular actor and models the way it acts in different situations. The simulator will be used for analyzing the costs and environmental effects, and will in this way provide guidance in decision making regarding control policies. In addition, it will be possible for companies to use the simulator in order to determine cost-effective strategies given different (future) scenarios.

Inter-Organization Interoperability in Transport Chains Using Adapters Based on Open Source Freeware

The significance of electronic information exchange in transport chains and its influence on the performance of the logistics processes is well-known. While much focus is still on the various techniques for information exchange, many Small and Medium-sized Enterprises (SMEs) experience problems that are not primarily caused by an absence of technology. Several advanced solutions exist but investment possibilities within many SMEs are very limited while their needs for electronic data exchange may be very similar as for the large companies. We describe a general “adapter” solution based on open source freeware that make it possible (in principle) for any business system to exchange information with any other business system. It has been successfully applied in a pilot study involving two companies (the transport operator being an SME) using different business systems (Hogia Mobilast and Movex). The solution was evaluated with the result that it met the requirements of the companies and is now planned to be used for future data exchange between the companies.

Multi-Agent-Based simulation for analysis of transport policy and infrastructure measures

In this paper we elaborate on the usage of multi-agent-based simulation (MABS) for quantitative impact assessment of transport policy and infrastructure measures. We provide a general discussion on how to use MABS for freight transport analysis, focusing on issues related to input data management, validation and verification, calibration, output data analysis, and generalization of results. The discussion is built around an agent-based transport chain simulation tool called TAPAS (Transportation And Production Agent-based Simulator) and a simulation study concerning a transport chain around the Southern Baltic Sea.

Combining Macro-level and Agent-based Modeling for Improved Freight Transport Analysis☆

5th International Conference on Ambient Systems, Networks and Technologies, ANT 2014, June 2 - 5, 2014, Hasselt, Belgium.

An extended TAPAS-Z model and a case study of the transport of forest products

Abstract: We contribute an extension of the agent-based freight transport analysis model TAPAS-Z, whose aim is to simulate decision- making and other activities in transport chains. TAPAS-Z is a further development of the TAPAS model, and it enables stochastic variation, based on statistical distributions, of the locations of senders and receivers of freight, hence providing improved support for simulation of transport between larger regions. The model extension presented in this paper enables stochastic sampling of sender and receiver locations from historically known data, which we argue is beneficial in those situations where such data exist. We also contribute a case study where we used our extended TAPAS-Z model to simulate transport of timber from forest felling places to a Swedish paper mill. The case study illustrates how we recommend conducting a case study using the extended TAPAS-Z model. The aim of the study was to assess the possible implications of a structural change from a time-based to a distance-based Eurovignette system for heavy freight trucks in Sweden, which will lead to increased costs for most road users. As a mitigation action for the increased costs, we studied the possibility to use heavier trucks than the currently allowed 60 tonne trucks. The simulation results suggest that an introduction of a distance-based Eurovignette is expected to cause a small modal shift from road to rail for the studied type of transport. Furthermore, an introduction of larger freight trucks is expected to lead to a complete shift to road transport.

Movement of People and Goods

Due to the continuous growth of traffic and transportation and thus an increased urgency to analyze resource usage and system behavior, the use of computer simulation within this area has become more frequent and acceptable. This chapter presents an overview of modeling and simulation of traffic and transport systems and focuses in particular on the imitation of social behavior and individual decision-making in these systems. We distinguish between transport and traffic. Transport is an activity where goods or people are moved between points A and B, while traffic is referred to as the collection of several transports in a common network such as a road network. We investigate to what extent and how the social characteristics of the users of these different traffic and transport systems are reflected in the simulation models and software. Moreover, we highlight some trends and current issues within this field and provide further reading advice.

A Role-Based Method for Analyzing Supply Chain Models

We present a role-based method for structured and uniform analysis of supply chain simulation models. The method is based on a framework of supply chain roles, responsibilities, and interactions, which can be used to represent different types of organizations involved in providing and using products and transport services. To show the applicability, validity, and generality of the method, we have used it to analyze five different supply chain simulation models: three agent-based models, one model based on discrete event simulation, and one based on system dynamics.