Analysis and modeling of ants’ behavior from single to multi-body

In order to shed light on the collective behavior of social insects, we analyzed the behavior of ants from single to multi-body. In an experimental set-up, ants are placed in hemisphere without a nest and food. Trajectory of ants is recorded. From this bottom-up approach, we found that collective behavior of ants as follows: 1. Activity of single ant increases and decreases periodically. 2. Spontaneous meeting process is observed between two ants and meeting spot of two ants is localized in hemisphere. 3. Result on division of labor is obtained between two ants.

A semiotic approach of multi-agent simulation for policy evaluation

Nowadays the changing environment becomes the main challenge for most of organizations, since they have to evaluate proper policies to adapt to the environment. In this paper, we propose a multi-agent simulation method to evaluate policies based on complex adaptive system theory. Furthermore, we propose a semiotic EDA (Epistemic, Deontic, Axiological) agent model to simulate agent's behavior in the system by incorporating the social norms reflecting the policy. A case study is also provided to validate our approach. Our research present better adaptability and validity than the qualitative analysis and experiment approach and the semiotic agent model provides high creditability to simulate agents' behavior.

Multi-agent simulation of sawmill yard operations

This paper reports the findings of using multi-agent based simulation model to evaluate the sawmill yard operations within a large privately owned sawmill in Sweden, Bergkvist Insjön AB in the current case. Conventional working routines within sawmill yard threaten the overall efficiency and thereby limit the profit margin of sawmill. Deploying dynamic work routines within the sawmill yard is not readily feasible in real time, so discrete event simulation model has been investigated to be able to report optimal work order depending on the situations. Preliminary investigations indicate that the results achieved by simulation model are promising. It is expected that the results achieved in the current case will support Bergkvist-Insjön AB in making optimal decisions by deploying efficient work order in sawmill yard.

Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The role of multi-physics simulation in virtual manufacturing

Abstract not available

A Multi-Agent Simulation of Retail Management Practices

We apply Agent-Based Modeling and Simulation (ABMS) to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents do offer potential for developing organizational capabilities in the future. Our multi-disciplinary research team has worked with a UK department store to collect data and capture perceptions about operations from actors within departments. Based on this case study work, we have built a simulator that we present in this paper. We then use the simulator to gather empirical evidence regarding two specific management practices: empowerment and employee development.

Multi-Agent Simulation and Management Practices

Intelligent agents offer a new and exciting way of understanding the world of work. Agent-Based Simulation (ABS), one way of using intelligent agents, carries great potential for progressing our understanding of management practices and how they link to retail performance. We have developed simulation models based on research by a multi-disciplinary team of economists, work psychologists and computer scientists. We will discuss our experiences of implementing these concepts working with a well-known retail department store. There is no doubt that management practices are linked to the performance of an organisation (Reynolds et al., 2005; Wall & Wood, 2005). Best practices have been developed, but when it comes down to the actual application of these guidelines considerable ambiguity remains regarding their effectiveness within particular contexts (Siebers et al., forthcoming a). Most Operational Research (OR) methods can only be used as analysis tools once management practices have been implemented. Often they are not very useful for giving answers to speculative ‘what-if’ questions, particularly when one is interested in the development of the system over time rather than just the state of the system at a certain point in time. Simulation can be used to analyse the operation of dynamic and stochastic systems. ABS is particularly useful when complex interactions between system entities exist, such as autonomous decision making or negotiation. In an ABS model the researcher explicitly describes the decision process of simulated actors at the micro level. Structures emerge at the macro level as a result of the actions of the agents and their interactions with other agents and the environment. We will show how ABS experiments can deal with testing and optimising management practices such as training, empowerment or teamwork. Hence, questions such as “will staff setting their own break times improve performance?” can be investigated.

Introduction to Multi-Agent Simulation

When designing systems that are complex, dynamic and stochastic in nature, simulation is generally recognised as one of the best design support technologies, and a valuable aid in the strategic and tactical decision making process. A simulation model consists of a set of rules that define how a system changes over time, given its current state. Unlike analytical models, a simulation model is not solved but is run and the changes of system states can be observed at any point in time. This provides an insight into system dynamics rather than just predicting the output of a system based on specific inputs. Simulation is not a decision making tool but a decision support tool, allowing better informed decisions to be made. Due to the complexity of the real world, a simulation model can only be an approximation of the target system. The essence of the art of simulation modelling is abstraction and simplification. Only those characteristics that are important for the study and analysis of the target system should be included in the simulation model. The purpose of simulation is either to better understand the operation of a target system, or to make predictions about a target system’s performance. It can be viewed as an artificial white-room which allows one to gain insight but also to test new theories and practices without disrupting the daily routine of the focal organisation. What you can expect to gain from a simulation study is very well summarised by FIRMA (2000). His idea is that if the theory that has been framed about the target system holds, and if this theory has been adequately translated into a computer model this would allow you to answer some of the following questions: · Which kind of behaviour can be expected under arbitrarily given parameter combinations and initial conditions? · Which kind of behaviour will a given target system display in the future? · Which state will the target system reach in the future? The required accuracy of the simulation model very much depends on the type of question one is trying to answer. In order to be able to respond to the first question the simulation model needs to be an explanatory model. This requires less data accuracy. In comparison, the simulation model required to answer the latter two questions has to be predictive in nature and therefore needs highly accurate input data to achieve credible outputs. These predictions involve showing trends, rather than giving precise and absolute predictions of the target system performance. The numerical results of a simulation experiment on their own are most often not very useful and need to be rigorously analysed with statistical methods. These results then need to be considered in the context of the real system and interpreted in a qualitative way to make meaningful recommendations or compile best practice guidelines. One needs a good working knowledge about the behaviour of the real system to be able to fully exploit the understanding gained from simulation experiments. The goal of this chapter is to brace the newcomer to the topic of what we think is a valuable asset to the toolset of analysts and decision makers. We will give you a summary of information we have gathered from the literature and of the experiences that we have made first hand during the last five years, whilst obtaining a better understanding of this exciting technology. We hope that this will help you to avoid some pitfalls that we have unwittingly encountered. Section 2 is an introduction to the different types of simulation used in Operational Research and Management Science with a clear focus on agent-based simulation. In Section 3 we outline the theoretical background of multi-agent systems and their elements to prepare you for Section 4 where we discuss how to develop a multi-agent simulation model. Section 5 outlines a simple example of a multi-agent system. Section 6 provides a collection of resources for further studies and finally in Section 7 we will conclude the chapter with a short summary.

Puunkäsittelykoneen simulointi ja mekaaniset häviöt

Diplomityö on osa Savonia-amk:n koneosaston TOVI-projektia, jossa metsä-konevalmistaja Ponsse Oyj on mukana. Työssä tutkittiin Ponsse Oyj:n metsäkoneen harvesteripäätä. Tavoitteena oli harvesteripään puun syöttöliikkeessä syntyvien mekaanisten häviöiden selvittäminen. Mekaanisilla häviöillä tarkoitamme karsintaterien kitkavoimia ja syöttörullien vierintävastusta.Edellisten lisäksi tavoitteena oli tutkia puun syöttöliikkeen simuloitavuutta monikappaledynamiikkaan perustuvalla simu-lointiohjelmistolla. Työ toteutettiin mittaamalla harvesteripään hydraulisten toimilaitteiden paineita, puun syötön aikana. Mittasimme myös puun ja harvesteripään välistä liikematkaa, nopeutta, kiihtyvyyttä, sekä puun paksuutta. Mittausten lisäksi harvesteripäästä rakennettiin simulointimalli. Mitattujen paineiden avulla laskettiin vastaavien toimilaitteiden synnyttämät voimat ja momentit. Simulointimallilla toistettiin mittaustapahtumat, käyttäen mittausten avulla laskettuja voimia ja momentteja. Mallin kitkakertoimien ja vierintävastusten avulla simuloidut ja mitatut liikematkat haettiin yhteneviksi. Toisin sanoen, simulointimalli verifioitiin todellisuutta vastaavaksi, jolloin simulointimallista voitiin lukea syntyneet häviöt.

Kiinnirullaimen dynamiikan ja värähtelyjen simulointi

Diplomityössä tutkittiin kaupallisen monikappaledynamiikkaohjelmiston soveltuvuutta kiinnirullaimen dynamiikan ja värähtelyjen tutkimiseen. Erityisen kiinnostuneita oltiin nipin kuvauksesta sekä nipissä tapahtuvista värähtelyistä. Tässä diplomityössä mallinnettiin kiinnirullaimen ensiö- ja toisiokäytöt sekä tampuuritela. Malli yhdistettiin myöhemmin Metso Paper Järvenpäässä rinnakkaisena diplomityönä tehtyyn malliin, joista muodostui kahteen ratkaisijaan perustuva simulointimalli. Simulointimalli rakennettiin käyttämään kahta erillistä ratkaisijaa, joista toinen on mekaniikkamallin rakentamisessa käytetty ADAMS-ohjelmisto ja toinen säätöjärjestelmää ja hydraulipiirejä kuvaava Simulink-malli. Nipin mallintamiseksi tampuuritela ja rullaussylinteri mallinnettiin joustaviksi käyttäen keskitettyjen massojen menetelmää. Siirtolaitteissa sekä runkorakenteissa tapahtuvat joustot kuvattiin yhden vapausasteen jousi-vaimennin voimilla kuvattuina järjestelminä. Tässä diplomityössä on myös keskitytty esittelemään ADAMS-ohjelmiston toimintaa ohjeistavasti sekä käsittelemään parametrisen mallintamisen etuja. Työssä havaittiin monikappaledynamiikan soveltuvuus kiinnirullaimen dynamiikan sekä dynaamisten voimien aiheuttamien värähtelyjen tutkimiseen. Suoritetuista värähtelymittauksista voitiin tehdä vain arvioita. Mallin havaittiin vaativan lisätutkimusta ja kehitystyötä

Dynamiikan simulointi poralaitteiden tuotekehityksessä

Diplomityö käsittelee kallioporalaitteen eliniän kuormitusten arviointiin tarkoitetun simulointimallin rakentamista. Työssä luotiin modulaarinen malli, jolla voidaan simuloida dynaamisesti esteen yliajo. Esteen yliajo on perinteinen tehdastesti uusien laitteiden prototyypeille. Sillä pyritään saamaan selville laitteen maksimikuormitustapaukset. Pintaporalaitteen simulointimalli tehtiin ADAMS-ohjelmistolla laitteen suunnittelussa tehtyjä CAD-malleja hyödyntäen. ADAMS-ohjelmistolla mallinnettiin laitteen yksinkertaistettu mekaniikka siten, että laite oli jaettu viiteen eri moduuliin. Runko ja telasto olivat omana osionaan. Puomi ja syöttölaite oli oma kokoonpanonsa ja ohjaamo sekä katteet olivat kaksi erillistä modulia. Viides moduuli oli hydrauliikka, joka simuloitiin rinnakkaissimulointina EASY5-ohjelmistolla. Simuloituja esteen yliajon tuloksia verrattiin prototyyppilaitteen tehdastestien mitattuihin kuormituksiin. Vertailu tehtiin mallinnettujen neljän sylinterin paineita tarkastelemalla. Tuloksia tarkastellessa havaittiin, että simulaatiomalli antaa varsinkin staattisissa tarkasteluissa oikean suuntaisia tuloksia. Dynaamisissa tilanteissa koneen maksimikuormituksilla mallinnustarkkuus ei tämän työn laajuudessa ole riittävä varsinaiseen eliniän analysointiin. Sen sijaan mallinnustekniikka todettiin periaatteessa toimivaksi ja jatkokehityskelpoiseksi.

Dynamische Standsicherheit von Portalstaplern

Der Artikel beschreibt die Untersuchung der dynamischen Standsicherheit von Portalstaplern. Dazu wurde ein aktueller repräsentativer Portalstapler analysiert und in ein Mehrkörper-Simulationsmodell abgebildet. Die Validierung des Modells erfolgte auf Basis von Messfahrten am realen System. Aus den durchgeführten Simulationsstudien wurden technische Verbesserungsmaßnahmen abgeleitet, welche die dynamische Standsicherheit von Portalstaplern erhöhen.