Suchergebnisse

This thesis explores how multi-agent-based simulation can be used for transport policy analysis. Transport policies are often used as a means to reach governmental goals, such as environmental targets to reduce the impact of transportation. To predict how transportation is influenced by policies, public authorities often make use of simulation models. A structured review of such models is made focussing on important transport chain characteristics. We argue that to properly predict the actual environmental, economic, and logistical effects of transport policies, the logistical decisions made in transport chains must be modelled appropriately. Such decisions, e.g., concern the choice of producer and traffic mode, planning of transportation, production, and terminal handling. The review concludes that models currently used for transport policy analysis fail to capture many of these characteristics. We argue that agent-based models have the potential to include these aspects since they are able to explicitly model the actual decision making in transport chains. We have identified a set of generic roles in transport chains where each role is responsible for certain decisions. A multi-agent-based simulator, TAPAS, has been developed in which these roles are modelled as agents. Thus, the decision making in transport chains and its influence by the application of transport policies are captured. The decisions lead to the execution of the logistical operations which in turn have consequences on the logistics, economic, and environmental performance. The usage of TAPAS is illustrated by presenting two scenarios based on realworld transport chains. Simulation experiments of the scenarios have been performed where different types of transport policies are introduced. The simulation results are analysed, e.g., by comparing the results to similar studies and by sensitivity analysis of input parameters. To facilitate the validation and generalisation of simulation results we suggest making use of typical transport chains and roles characterised by, e.g., product type and geographical locations. The type of studies that TAPAS can support are described and compared to studies typically made with traditional models. Transport policies which are relevant to examine are described and their potential influence on transport chains are analysed. The possible usage of TAPAS is discussed and related to different types of users. Public authorities can, e.g., use TAPAS to complement studies using traditional models. This can improve the accuracy of the simulation results by the inclusion of more logistical aspects. Large companies are another type of user which, e.g., can use TAPAS to analyse new market segments, such as new product types or new consumers, where historical data is not available.

Increasing freight transportation volumes continue to increase problems related to human health, congestion on the transport infrastructure, noise, climate changes, etc. Governments often want to minimize these negative effects, and this wish is expressed in societal goals, e.g., to reach environmental targets. An important instrument for reaching societal goals is governmental control policies, e.g., regulations, taxes and fees, which can influence the behaviour of the actors in a transport chain. Before implementing such control policies, it is crucial to predict their effects in order to make probable that it is a good measure. A review of models that consider the effects of governmental control policies on transportation has been performed which shows that macro-level models are mainly used for this purpose. However, the behaviour of the individual transport chain actors can hardly be captured in such models since the decision making actors are not modelled explicitly. Consequently, the negative effects caused by the decisions taken by the individual transport chain actors are not fully captured in these models. We believe that micro-level models have the potential to bridge the gap between governmental policy-making and the behaviour of transport chain actors. A micro-level model based on agent-technology has been developed which captures the environmental, quality and economical performance in transport chains, given different governmental control policies. The transport chain actors are represented by decision-making agents in the model. Logistical factors for characterizing transport chains have been identified and described according to degree of influence. To illustrate the usage of the micro-level model, simulation experiments based on a real world case have been performed where different levels of governmental control policies are introduced. The simulation results so far have showed that the model seems to simulate the behaviour of the transport chain actors correctly in the studied scenarios. The simulation tool can then be used as a decision support for policy-makers and serve as a complement to existing tools based on macro-level models.

To predict which supply chain effects will appear when applying governmental control policies, infrastructure investments, and business strategies, multi-agent-based simulation (MABS) can be used. In this paper, we identify abstract supply chain responsibilities, roles and interactions that are argued to be sufficient for representing all types of organizations involved in the processes of buying and selling products and transport services. The identified responsibilities, roles and interactions are organized into a framework together witha set of modeling guidelines, which we relate to the GAIAmethodology to simplify the process of developing multi-agent-based supply chain simulation models. To illustrate the usage of the framework, we provide two case studies where we apply it to two different MABS models.

To predict which supply chain effects will appear when applying governmental control policies, infrastructure investments, and business strategies, multi-agent-based simulation (MABS) can be used. In this paper, we identify abstract supply chain responsibilities, roles and interactions that are argued to be sufficient for representing all types of organizations involved in the processes of buying and selling products and transport services. The identified responsibilities, roles and interactions are organized into a framework together with a set of modeling guidelines, which we relate to the GAIA methodology to simplify the process of developing multi-agent-based supply chain simulation models. To illustrate the usage of the framework, we provide two case studies where we apply it to two different MABS models.

This project investigates how the actors in a transportation chain are expected to act when different types of governmental control policies are applied. A simulation-based tool is being developed for use in analyzing the effects of different control policies. The simulator is based on agent technology and include some intelligence with respect to how the transport operators act in different situations. The simulator will be used for analyzing the total costs and environmental effects and in this way provide guidance in decision making regarding control policies. Also, cost-effective strategies for companies can be determined with the simulator.

An agent-based tool for micro-level simulation of transport chains (TAPAS) is described. It is more powerful than traditional approaches as it is able to capture the interactions between individual actors of a transport chain, as well as their heterogeneity and decision making processes. Whereas traditional approaches rely on assumed statistical correlation between different parameters, TAPAS relies on causality, i.e., the decisions and negotiations that lead to the transports being performed. An additional advantage is that TAPAS is able to capture time aspects, such as, the influence of timetables, arrival times, and time-differentiated taxes and fees. TAPAS 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 and interaction. The decision layer is implemented as a multi-agent system using the JADE platform, where each agent corresponds to a particular actor. We demonstrate the use of TAPAS by investigating 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, and vehicle taxes. By analyzing the costs and environmental effects, TAPAS provides guidance in decision making regarding such control policies. We argue that TAPAS may also complement existing approaches in different ways, for instance by generating input data such as transport demand. Since TAPAS models a larger part of the supply chain, the transport demand is a natural part of the output. Studies may concern operational decisions like choice of consignment size and frequency of deliveries, as well as strategic decisions like where to locate storages, terminals, etc., choice of producer, and adaptation of vehicle fleets.

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.

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.