Methodologies for self-organising systems:a SPEM approach

We define ’SPEM fragments’ of five methods for developing self-organising multi-agent systems. Self-organising traffic lights controllers provide an application scenario

Evolution of a supply chain management game for the trading agent competition

TAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt

Design choices for agent-based control of AGVs in the dough making process

In this paper we consider a multi-agent system (MAS) for the logistics control of Automatic Guided Vehicles (AGVs) that are used in the dough making process at an industrial bakery. Here, logistics control refers to constructing robust schedules for all transportation jobs. The paper discusses how alternative MAS designs can be developed and compared using cost, frequency of messages between agents, and computation time for evaluating control rules as performance indicators. Qualitative design guidelines turn out to be insufficient to select the best agent architecture. Therefore, we also use simulation to support decision making, where we use real-life data from the bakery to evaluate several alternative designs. We find that architectures in which line agents initiate allocation of transportation jobs, and AGV agents schedule multiple jobs in advance, perform best. We conclude by discussing the benefits of our MAS systems design approach for real-life applications

Transit network design with meta-heuristic algorithms and agent based simulation

In this work, a transit network design problem is presented. The problem is identified as a typical large-scale complex system. Subsequently, it is decomposed into its sub-components. The first two sub-components, which encompass the network design and frequency setting problems, are then tackled by means of an innovative solution framework that combines a genetic algorithm with agent-based travel demand modelling. An analysis of results obtained from applying the proposed method to different testing scenarios shows that it is capable of designing transit networks that address the individual and collective perspectives of different stakeholders. Hence it can be used as a viable decision support tool for policy makers in the transportation network sector.https://www.journals.elsevier.com/ifac-papersonlinehj2019Industrial and Systems Engineerin