Modeling dynamic interactions in supply chains using agentbased simulations

In this work, we present preliminary results of our research on the construction of an agent-based simulation framework suitable to support the analysis of complex supply chain interactions as the one required for the performance assessment in collaborative supply chains. In particular we focus in the modeling of dynamic interactions through agent-to-agent message communication avoiding predefined supply chain network structures. For defining the internal structure of agents we explore the application of the SCOR reference model to bring a business process perspective and adopt the requirement of making explicit separation of the execution and control and decision making processes.Sociedad Argentina de Informática e Investigación Operativ

Modeling Transport in Service-Oriented Framework for Agent-Based Simulations of Supply Chains

This work presents an extension to the service-oriented framework for performing agent-based simulations to support the analysis of collaborative relationships in supply chain interactions already described in [5]. The extension includes a new service for simulating the planning of transportation, which is designed to allocate finite transportation resources to the transportation orders. The service includes the functionality that matches the required vehicle type, location and time to track the availability of a vehicle fleet as it is allocated to a plan. The new service proposal is validated by simulating three different scenarios of a case study. The case study is based on an actual supply chain of dairy products covering the central region of Argentina.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Modeling Transport in Service-Oriented Framework for Agent-Based Simulations of Supply Chains

This work presents an extension to the service-oriented framework for performing agent-based simulations to support the analysis of collaborative relationships in supply chain interactions already described in [5]. The extension includes a new service for simulating the planning of transportation, which is designed to allocate finite transportation resources to the transportation orders. The service includes the functionality that matches the required vehicle type, location and time to track the availability of a vehicle fleet as it is allocated to a plan. The new service proposal is validated by simulating three different scenarios of a case study. The case study is based on an actual supply chain of dairy products covering the central region of Argentina.Sociedad Argentina de Informática e Investigación Operativa (SADIO

SCS: 60 years and counting! A time to reflect on the Society's scholarly contribution to M&S from the turn of the millennium.

The Society for Modeling and Simulation International (SCS) is celebrating its 60th anniversary this year. Since its inception, the Society has widely disseminated the advancements in the field of modeling and simulation (M&S) through its peer-reviewed journals. In this paper we profile research that has been published in the journal SIMULATION: Transactions of the Society for Modeling and Simulation International from the turn of the millennium to 2010; the objective is to acknowledge the contribution of the authors and their seminal research papers, their respective universities/departments and the geographical diversity of the authors' affiliations. Yet another objective is to contribute towards the understanding of the overall evolution of the discipline of M&S; this is achieved through the classification of M&S techniques and its frequency of use, analysis of the sectors that have seen the predomination application of M&S and the context of its application. It is expected that this paper will lead to further appreciation of the contribution of the Society in influencing the growth of M&S as a discipline and, indeed, in steering its future direction

Hierarchical reinforcement learning for trading agents

Autonomous software agents, the use of which has increased due to the recent growth in computer power, have considerably improved electronic commerce processes by facilitating automated trading actions between the market participants (sellers, brokers and buyers). The rapidly changing market environments pose challenges to the performance of such agents, which are generally developed for specific market settings. To this end, this thesis is concerned with designing agents that can gradually adapt to variable, dynamic and uncertain markets and that are able to reuse the acquired trading skills in new markets. This thesis proposes the use of reinforcement learning techniques to develop adaptive trading agents and puts forward a novel software architecture based on the semi-Markov decision process and on an innovative knowledge transfer framework. To evaluate my approach, the developed trading agents are tested in internationally well-known market simulations and their behaviours when buying or/and selling in the retail and wholesale markets are analysed. The proposed approach has been shown to improve the adaptation of the trading agent in a specific market as well as to enable the portability of the its knowledge in new markets