Search

To allow a better exergy exploitation than the current state-of-the-art waste heat to power solutions in the steel industry, a new type of energy recovery system based on Phase Change Materials is proposed. In particular, the use of high temperature PCMs evolves from simply smoothing off gas temperature, as in the most recent studies for energy recovery from electric arc furnaces, to generating constant superheated steam able to feed the downstream turbine nearly at nominal load. This result is achieved by introducing an auxiliary section between the PCM Section and the steam generation one, which provides the auxiliary heat needed to level the thermal content of off gas. The auxiliary heat is extracted from the PCM units by a heat transfer fluid flowing across the inner tube of each PCM container. Different models to properly size and simulate the operations of the proposed energy recovery system have been developed and integrated. Results show how the size of the steam generator and the turbine can be reduced of about 41% with respect to traditional solutions, while increasing electric power production by 22% thanks to the reduced fluctuation in steam parameters at the turbine inlet, which leads to a greater overall efficiency. Accepted version

Conflict, from small-scale verbal disputes to large-scale violent war between nations, is one of the most fundamental elements of social life and a central topic in social science research. The main argument of this book is that computational approaches have enormous potential to advance conflict research, e.g., by making use of the ever-growing computer processing power to model complex conflict dynamics, by drawing on innovative methods from simulation to machine learning, and by building on vast quantities of conflict-related data that emerge at unprecedented scale in the digital age. Our goal is (a) to demonstrate how such computational approaches can be used to improve our understanding of conflict at any scale and (b) to call for the consolidation of computational conflict research as a unified field of research that collectively aims to gather such insights. We first give an overview of how various computational approaches have already impacted on conflict research and then guide through the different chapters that form part of this book. Finally, we propose to map the field of computational conflict research by positioning studies in a two-dimensional space depending on the intensity of the analyzed conflict and the chosen computational approach.

Rebel groups engage in a series of economic transactions with their local populations during a civil war. These interactions resemble those of a protection racket, in which aspiring governing groups extort the local economic actors to fund their fighting activities and control the territory. Seeking security in this unstable political environment, these economic actors may decide to flee or to pay the rebels in order to ensure their own protection, impacting the outcomes of the civil war. We present a simulation model (executable at https://gnardin.github.io/RebelGroups) that attempts to capture the decision-making and behavior of the involved actors during protection racket interactions as well as the cooperation and competition between rebel groups to control territory. Our model reveals insights about the mechanisms that are helpful for understanding violence outcomes in civil wars, and the conditions that may lead rebel groups to prevail. Analysis of various scenarios demonstrates the impact that different security factors play on civil war dynamics. Using Somalia as a case study, we also assess the importance of the rebel groups’ economic bases of support in a real-world setting.

First Online: 01 September 2016 Computer simulation has recently been recognised by criminologists as a useful tool for bridging the gap between theoretical and empirical analyses of organised crime and for supplementing their weaknesses. GLODERS-S is an innovative and configurable agent-based simulator specialised in reproducing the dynamics of a specific type of criminal organisations: protection racketeering groups. The simulator adopts an event-based approach that provides a more realistic operation of the agents, which integrated with its configurability provides policy-makers with a highly flexible platform for analysing multiple scenarios and assessing policies to counter organised crime. In this paper, we describe the principles of the simulator design, its features and limitations, and possible applications.

This article develops an analytical and numerical approach to evaluate thermal stress in a phase change material (PCM) system, used for temperature smoothing of waste gas of Electric Arc Furnace, in which the PCM is encapsulated in a cylindrical steel container. Thermal analysis shows that temperature distribution in the PCM system can be considered as uniform at any time instant according to the lumped capacitance method; the thermal behaviour of PCM system is thus simulated as a sequence of steady state analyses. Mechanical analysis adopts an axialsymmetric plane analytical model to compare elastic thermal stress distribution for different stainless steels and to identify AISI 316 as the most suitable material for the PCM container. A simple two-bars model and a stress index are also used to allow a physical understanding and a satisfactory interpretation of the PCM system response. Mechanical analysis shows that thermal stresses exceed the yield point of both stainless steels used in the container. A finite element elastic-plastic model is then developed to estimate the extension of the plastic zone. Finally, an alternative PCM system geometry based on concentric pipes is designed to keep the maximum stresses in the PCM container below the yielding point. A sensitivity analysis shows that the most relevant design parameters of the alternative geometry are the diameter of inner pipe and thickness of the external pipe.

Protection racketeering groups are powerful, deeply entrenched in multiple societies across the globe, and they harm the societies and economies in which they operate in multiple ways. These reasons make their dynamics important to understand and an objective of both scientific and application-oriented interest. Legal and social norm-based approaches arguably play significant roles in influencing protection racket dynamics. We propose an agent-based simulation model, the Palermo Scenario, to enrich our understanding of these influences and to test the effect of different policies on protection racket dynamics. Our model integrates the legal and the social norm-based approaches and uses a complex normative agent architecture that enables the analysis of both agents' behaviours and mental normative representations driving behaviour. We demonstrate the usefulness of the model and the benefits of using this complex normative architecture through a case study of the Sicilian Mafia. This work was partially supported by the FP7-ICT Science of Global Systems programme of the European Commission through Project GLODERS (http://www.gloders.eu, Global Dynamics of Extortion Racket Systems) under Grant agreement no.: 315874.

We understand a socio-technical system (STS) as a cyber-physical system in which two or more autonomous parties interact via or about technical elements, including the parties’ resources and actions. As information technology begins to pervade every corner of human life, STSs are becoming ever more common, and the challenge ofgoverningSTSs is becoming increasingly important. We advocate a normative basis for governance, whereinnormsrepresent the standards of correct behaviour that each party in an STS expects from others. A major benefit of focussing on norms is that they provide a socially realistic view of interaction among autonomous parties that abstracts low-level implementation details. Overlaid on norms is the notion of asanctionas a negative or positive reaction to potentially any violation of or compliance with an expectation. Although norms have been well studied as regards governance for STSs, sanctions have not. Our understanding and usage of norms is inadequate for the purposes of governance unless we incorporate a comprehensive representation of sanctions.We address the aforementioned gap by proposing (i) a sanction typology that reflects the relevant features of sanctions, and (ii) a conceptual sanctioning process model providing a functional structure for sanctioning in STS. We demonstrate our contributions via a motivating scenario from the domain of renewable energy trading.

Consistent confirmations obtained independently of each other lend credibility to a scientific result. We refer to results satisfying this consistency as reproducible and assume that reproducibility is a desirable property of scientific discovery. Yet seemingly science also progresses despite irreproducible results, indicating that the relationship between reproducibility and other desirable properties of scientific discovery is not well understood. These properties include early discovery of truth, persistence on truth once it is discovered, and time spent on truth in a long-term scientific inquiry. We build a mathematical model of scientific discovery that presents a viable framework to study its desirable properties including reproducibility. In this framework, we assume that scientists adopt a model-centric approach to discover the true model generating data in a stochastic process of scientific discovery. We analyze the properties of this process using Markov chain theory, Monte Carlo methods, and agent-based modeling. We show that the scientific process may not converge to truth even if scientific results are reproducible and that irreproducible results do not necessarily imply untrue results. The proportion of different research strategies represented in the scientific population, scientists' choice of methodology, the complexity of truth, and the strength of signal contribute to this counter-intuitive finding. Important insights include that innovative research speeds up the discovery of scientific truth by facilitating the exploration of model space and epistemic diversity optimizes across desirable properties of scientific discovery., EDITS: New title, corrected typos and errors, extended model and results description

Sanctioning is one of the most adopted enforcement mechanisms in the governance of multiagent systems. Current enforcement frameworks, however, restrict agents to reason about and make sanctioning decisions. We developed the Gavel framework, an adaptive sanctioning enforcement framework that enables agents to decide for the most appropriate sanction to apply depending on various decision factors. The potential benefits and use of the framework are shown using a Public Goods Game in which agents are endowed with different strategies combining material and reputational sanctions.

In order to allow an efficient energy recovery from off-gas in the steel industry, the high variability of heat flow should be managed. A temperature smoothing device based on phase change materials at high temperatures is inserted into the off-gas line of a continuous charge electric arc furnace process with scrap preheating. To address overheating issues, a heat transfer fluid flowing through containers is introduced and selected by developing an analytical model. The performance of the smoothing system is analyzed by thermo-fluid dynamic simulations. The reduced maximum temperature of off-gas allows to reduce the size and investment cost of the downstream energy recovery system, while the increased minimum temperature enhances the steam turbine load factor, thus increasing its utilization. Benefits on environmental issues due to dioxins generation are also gained.

To maximize the environmental performance of the energy systems, a paradigm change towards the concept of smart multi energy systems is needed. Optimal planning, design and operation of such energy systems, which efficiently integrate different energy sources, vectors and needs, is intrinsically a multi-objective problem in terms of sustainability. In this study, a decision support system based on performance indicators and Pareto multi-objective optimization is developed. System design combines renewable energy sources and combined cooling heat and power serving a cluster of firms through district energy distribution networks. Results show that the model enables the analysis of the trade-off between the different objective functions, allowing to identify the optimal energy systems layout through the selection of the proper size of the generation units. It also provides design directions such as the thermal energy storage capacity. The case study evidences that the smart energy systems concept can really represent a main opportunity to industrial districts both from the sustainability and the competitiveness perspective. Research also suggests that some financial incentives should be studied so that the solution providing the largest energy saving and carbon dioxide emission reduction could improve its economic attractiveness.

Cooperation is central to the success of human societies as it is crucial for overcoming some of the most pressing social challenges of our time. Yet how human cooperation is achieved and may persist is still a main puzzle in the social and biological sciences. Recently, scholars have recognized the importance of social norms as solutions to major local and large-scale collective action problems, from the management of water resources to the reduction of smoking in public places to the change in fertility practices. Yet a well-founded model of the effect of social norms on human cooperation is still lacking. Using statistical physics techniques and integrating findings from cognitive and behavioral sciences, we present an analytically-tractable model in which individuals base their decisions to cooperate both on the economic rewards they obtain and on the degree to which their action comply with social norms. Results from this parsimonious model are in agreement with what has been observed in recent large-scale experiments with humans. We also find the phase diagram of the model and show that the experimental human group is poised near a critical point, a regime where recent work suggests living systems respond to changing external conditions in an efficient and coordinated manner., Comment: Modified in reply to referees. Added section and table summarizing assumptions and state of the art, moved part of appendices to main text, extended discussion, emphasize further our contributions, change title. 26 pages, 4 figures, 4 tables

The problem of energy recovery from the electric arc furnace process of steel industry is addressed. During a tap to tap cycle, a significant part of the energy required for steel production is dissipated by the off-gas. The high variability of temperatures and flows, and the high concentration of dust, which characterize the production process, make the adoption of current energy recovery solutions quite difficult, both from the technological and the economical perspective. A new system is proposed exploiting the characteristics of phase change materials (PCM), in particular aluminum, to reduce the variability of off-gas temperatures and thermal powers, in order to allow an efficient energy recovery. The smoothing device is analyzed by thermo-fluid dynamic simulations in order to optimize its performance. A new boiler configuration equipped with cyclones is proposed to overcome also the problem of high dust content of the off-gas. The high recovery efficiencies, the low investment and operation costs and non-invasive plant modifications induced by the smoothing system, make the proposed PCM-based recovery system a feasible solution to reduce energy supply costs and emissions in the steel industry.

Italy has witnessed an extraordinary growth in biogas generation from livestock effluents and agricultural activities in the last few years as well as a severe isomorphic process, leading to a market dominance of 999 kW power plants owned by “entrepreneurial farms”. Under the pressure of the economic crisis in the country, the Italian government has restructured renewable energy support schemes, introducing a new program in 2013. In this paper, the effects of the previous and current support schemes on the optimal plant size, feedstock mix and profitability were investigated by introducing a spatially explicit biogas supply chain optimization model, which accounts for different incentive structures. By applying the model to a regional case study, homogenization observed to date is recognized as a result of former incentive structures. Considerable reductions in local economic potentials for agricultural biogas power plants without external heat use, are estimated. New plants are likely to be manure-based and due to the lower energy density of such feedstock, wider supply chains are expected although optimal plant size will be smaller. The new support scheme will therefore most likely eliminate past distortions but also slow down investments in agricultural biogas plants.

This study reports the thermal analysis of a novel thermal energy storage based on high temperature phase change material used to improve efficiency in waste-to-energy plants. Current waste-to-energy plants efficiency is limited by the steam generation cycle which is carried out with boilers composed by water-walls (i.e. radiant evaporators), evaporators, economizers and superheaters. Although being well established this technology is subjected to limitations related with high temperature corrosion and fluctuation in steam production due to the non-homogenous composition of solid waste; this leads to increased maintenance costs and limited plants availability and electrical efficiency. The proposed solution in this paper consists of replacing the typical refractory brick installed in the combustion chamber with a PCM-based refractory brick capable to store a variable heat flux and to release it on demand as a steady heat flux. By means of this technology it is possible to avoid steam production fluctuation, to increase temperature of superheated steam over current corrosion limits (450°C) without using coated superheaters and to increase the electrical efficiency beyond 34%. In the current paper a detailed thermo-mechanical analysis has been carried out in order to compare the performance of the PCM-based refractory brick against the traditional ones. The PCM considered in this paper is aluminium and its alloys whereas its container consists of high density ceramics (such as Al2O3, AlN and Si3N4); the different coefficient of linear thermal expansion for the different materials requires a detailed thermo-mechanical analysis to be carried out to ascertain the feasibility of the proposed technology. NRF (Natl Research Foundation, S’pore) Accepted version

Agents interaction about reputation has to deal with semantic interoperability issues, which can be handled by different approaches using different levels of expressiveness. Previous experiments have already been conducted in order to investigate the effects of a more expressive communication language on agents' reputation evaluation accuracy, but their analyses disregard the possible correlations among reputation models' attributes. Here, we propose the use of a multivariate statistical approach in order to take into account such correlations and to encourage the social simulation community to analyze its experimental outputs using formal mathematical approaches. We also applied the presented approach to the experimental results previously analyzed using a univariate statistical approach. Our analysis corroborate with the latter showing that, in most cases, there is benefit in using a more expressive communication language.

The spread of infectious diseases can be impacted by human behavior, and behavioral decisions often depend implicitly on a planning horizon—the time in the future over which options are weighed. We investigate the effects of planning horizons on epidemic dynamics. We developed an epidemiological agent-based model (along with an ODE analog) to explore the decision-making of self-interested individuals on adopting prophylactic behavior. The decision-making process incorporates prophylaxis efficacy and disease prevalence with the individuals’ payoffs and planning horizon. Our results show that for short and long planning horizons individuals do not consider engaging in prophylactic behavior. In contrast, individuals adopt prophylactic behavior when considering intermediate planning horizons. Such adoption, however, is not always monotonically associated with the prevalence of the disease, depending on the perceived protection efficacy and the disease parameters. Adoption of prophylactic behavior reduces the epidemic peak size while prolonging the epidemic and potentially generates secondary waves of infection. These effects can be made stronger by increasing the behavioral decision frequency or distorting an individual’s perceived risk of infection.

The role of enabling industrial symbiosis can be played by a Facilities Management (FM) provider, when it embraces a district-oriented FM approach. This consists on moving from traditional solutions for single enterprises to technical solutions specifically designed for clusters of firms. As an economic actor, a FM provider naturally pursuits profitability of its services/products, and this has been highlighted by literature as a necessary condition for successful evolution towards industrial symbiosis. An optimization model has been developed as a decision support tool for a FM provider when configuring energy-based kernels of industrial symbiosis. It is applied to a case study in North-Eastern Italy to analyze feasibility of a combined heat and power system with district heating serving a cluster of firms, where different services (heating and cooling) and renewable technologies are considered in system design, and additional concentrating solar unit explicitly introduced as an option.

In the last decades, we have experienced a rapid increase in the number of available online e-services. Agent-based computing has been advocated as a natural computational model to automate the interaction with those services, thus enabling the formation of multiagent systems. In these latter, agents may use trust and reputation as the main control mechanism and they usually exchange such information in order to accelerate reputation evaluation. However, due to the semantic heterogeneity of the different reputation models, agents interaction about reputation has to deal with interoperability issues. Therefore, this paper presents some experiments using SOARI, an architecture that enables the semantic interoperability among agents that have heterogeneous reputation models. Such experiments were conducted using two reputation testbeds and three agent reputation models in order to analyze the accuracy of the agents reputation evaluation in the presence of a more expressive communication apparatus, as well as the effect of the heterogeneity among reputation models on this accuracy.

Norms, in their many forms, are all around us. The social world that we inhabit is saturated with them. As others have elegantly put it, ‘from forceps to grave, human life is wrapped in a tightly woven tapestry of rules, standards, and expectations that govern every aspect of social behaviour’ (Anderson & Dunning, 2014). Not only are norms widespread, but they also have powerful effects on our individual and social behaviour, often for the better but sometimes for the worse (Ellickson, 1991; Fehr & Fischbacher, 2004; Henrich et al., 2001). Actions taken within the context of protection rackets are arguably not exempt. Domain-specific norms, regarding paying or supporting behaviour, and domain-general norms such as reciprocity and fairness may shape behaviour there too.

Mafias can be considered as criminal organisations that are in the business of producing, promoting, and selling protection. Here, we describe the Palermo Scenario, an agent-based model of protection rackets aimed to deepen our understanding of protection rackets, and help policymakers to evaluate methods for destabilising them. Additionally, since the system is explicitly specified, we can use it to investigate the entire causal pathway from cause to effect: not only from actions to Mafia destabilisation, but also the intermediate actions along the path and actors' internal mental representations among the population.

The aim of this chapter is to summarise the problems incurred during the phases of calibrating and validating the extortion racket models used by the GLODERS project. The chapter starts with the discussion of the data availability and summarises shortly the contents of Sect. 4.3. It continues with a discussion of what parameterisation, calibration, sensitivity analysis and validation have to do with each other and ends up with a discussion of the validity of the GLODERS models.

Mafias are highly powerful and deeply entrenched organised criminal groups that cause both economic and social damage. Overcoming, or at least limiting, their harmful effects is a societally beneficial objective, which renders its dynamics understanding an objective of both scientific and political interests. We propose an agent-based simulation model aimed at understanding how independent and combined effects of legal and social norm-based processes help to counter mafias. Our results show that legal processes are effective in directly countering mafias by reducing their activities and changing the behaviour of the rest of population, yet they are not able to change people’s mind-set that renders the change fragile. When combined with social norm-based processes, however, people’s mind-set shifts towards a culture of legality rendering the observed behaviour resilient to change.

SUMMARY The paper summarizes a project for an experimental energy system to be developed as a strategic measure to enhance global competitiveness of the Industrial Development Area (ASI) of Aragona - Favara in Agrigento Province. The aim of the project is to achieve a self-sufficient system (“Energy Island”) fed only with bio-oil from local crops. Planned duration of the experiment is four years, and the final goal is to establish new and profitable enterprises taking advantage from a cost-effective and sustainable energy supply chain: planned activities, in fact, involve farming and oil production ventures as well as the energy intensive food industries already settled in the area. Industrial-scale benefits will be achieved by expanding experimental plants, in line with the original concept, once satisfactory profitability has been demonstrated on small scale. Strategic principles of the project, in particular the “short supply chain” concept, are illustrated first. The Industrial Development Area of AragonaFavara is then examined as to its energy demand, which should be met by renewable energy. The supply side is subsequently analyzed, focusing on how a short supply chain for oleaginous crops (sunflower and rapeseed) could be structured by appointing some set-aside agricultural areas to industrial crops. Incentives and national legislation on renewable energy are presented to outline market opportunities for energy and for produced biofuel once the experimental phase is concluded. Technical details of the project are then given, with process description and an account of how the experimental plants have been sized, deriving energy and mass flow balances and estimating avoided GHG emissions within the experimental timeframe. Agronomic and energy conversion activities related to construction and operation of the experimental systems are illustrated and future developments and strategic implications are outlined. The project has been conceived by the CIFRA - Interdepartment Center for Environmental Research and Education of Udine University - on behalf and with the cooperation of the Unione Industriali - Agrigento.

Em sistemas multiagentes (SMA), a impossibilidade de controle do comportamento interno dos agentes propicia a emergência de dilemas sociais. Uma das possíveis abordagens para resolver tais dilemas é a abordagem social, que tem no conceito de reputação seu principal mecanismo de controle. Para agilizar a obtenção da reputação, torna-se essencial que os agentes cooperem, trocando informações a respeito de reputação. Assim, a partir de diversas observações, avaliações e troca de informações sobre reputação pode-se observar o surgimento da ordem social no sistema global. Entretanto, a heterogeneidade semântica entre os diferentes modelos de reputação torna a interação de agentes um problema, principalmente em SMA abertos. Este trabalho propõe uma arquitetura baseada em serviços, denominada SOARI, para o tratamento deste problema. Esta arquitetura apóia o mapeamento e tradução de conceitos entre ontologias de modelos de reputação distintos através do uso de uma ontologia comum no domínio da reputação. Para ilustrar o uso da arquitetura, foram realizados diversos experimentos nos domínios de comércio eletrônico e de avaliação de obras de arte. Neste último caso, analisou-se os impactos causados nos agentes como conseqüência da habilitação de uma maior expressividade e heterogeneidade na comunicação sobre reputação. In Multiagent Systems (MAS), controlling the internal agent\'s behavior is impossible and therefore social dilemma may emerge. One possible approach to solve the problem is the social approach, which considers reputation as its main control mechanism. In order to accelerate the reputation evaluation, it is essential that the cooperating agents could exchange information about reputation. Thus, by using observations, evaluations and information exchange about reputation the social order in the global system may occur. However, the semantic heterogeneity of different reputation models makes agent interaction a problem, especially in open MAS. This work proposes a service oriented architecture, named SOARI, to deal with this problem. This architecture supports concept mapping and translation among different reputation model ontologies by using a common ontology. In order to illustrate its deployment, several experiments in the electronic commerce and art appraisal domains were performed. In the latter it was analyzed the effects caused on the agents as a consequence of a more expressive and heterogeneous communication about reputation.

In the Mansfeld region (Central Germany) copper mining contributed to an enormous pollution of the environment. Metal- and sulphate-bearing sediments and leachates emerge from the former copper smelters and mining waste heaps, spread along local rivers and finally reach the Saale river. A sulphur isotope study on water and stream sediments was performed along the River "Böse Sieben" and from its tributaries to determine the different sulphur sources. Four major sulphur sources exist in the area: metal sulphide mineralisations (Kupferschiefer), met alliferous sulphidic flue dust, slag, and anhydrite and gypsum of Permian and Triassic age. We obtained delta34S(SO4)-values in water samples varying from +4 per thousand to -18 per thousand CDT, clearly reflecting the input of sulphate from different sources. Sulphate from the oxidation of sulphidic mining residues is restricted to the mining area and cannot be traced for more than 5 km downstream. The major source for sulphate is the dissolution of gypsum and anhydrite. The sulphur isotope composition in dissolved and sedimentary adsorbed sulphate differs only slightly from each other. Microbial dissimilatory sulphate reduction can not be excluded in the shallow sediment layers.

The exploitation of Agent-Based Social Simulation (ABSS) full capabilities often requires massive computing power and tools in order to support the achievement of breakthrough results in social sciences. Lately, this issue has being addressed by the release of several high-performance computing agent-based simulation tools; however, they have not been used for exploring critical issues, such as ABSS results invariance and universality. Hence, in order to advance this topic, this chapter provides an invariance analysis, considering scale and topology, of a model that incorporates the concepts of trust and coalition formation, in which agents are placed on a square lattice interacting locally with their neighbors and forming coalitions. By varying the environment size, its topology, as well as the neighborhood topology, it is identified in the experimental scenario that apparently the only parameter that affects the simulation dynamics is the neighborhood topology.

© The Author(s) 2013. Punishment plays a crucial role in favoring and maintaining social order. Recent studies emphasize the effect of the norm-signaling function of punishment. However, very little attention has been paid so far to the potential of group punishment. We claim that when inflicted by an entire group, the recipient of punishment views it as expressing norms. The experiments performed in this work provide evidence that humans are motivated not only by material incentives that punishment imposes but also by normative information that it conveys. The same material incentive has a different effect on the individuals’ future compliance depending on the way it is implemented, having a stronger effect when it also conveys normative information. We put forward the hypothesis that by inflicting equal material incentives, group punishment is more effective in enhancing compliance than uncoordinated punishment, because it takes advantage of the norm-signaling function of punishment. In support of our hypothesis, we present cross-methodological data, that is, data obtained through agent-based simulation and laboratory experiments with human subjects. The combination of these two methods allows us to provide an explanation for the proximate mechanisms generating the cooperative behavior observed in the laboratory experiment., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: MacNorms project (Intramurales de frontera CSIC—PIF08-007) The Institute of Cognitive Science and Technologies (ISTC-CNR, Rome), the European University Institute, the Spanish Ministerio de Economía y Competitividad (Grant: ECO2011-29847-C02-01), the Generalitat de Catalunya (Grant: 2009 SGR 820 and Grant 2009SGR1434), the Antoni Serra Ramoneda Research Chair (UAB-CatalunyaCaixa) for supporting the authors.

Supply chain management (SCM) has increased its importance in the last decades, accordingly demanding new approaches to support its decision making processes. Simulation has been advised as an adequate approach for fulfilling such demand. However, develop monolithic simulation models representing the whole supply chain can be costly and time consuming. In the iron ore supply chain in which the seaports have the same features, the use of generic models and distributed simulation may be a real alternative in order to reduce the development time and costs. This paper presents a distributed simulation model of the maritime logistics in an iron ore supply chain applied to support fleet management decisions. Such model was used to perform an experiment in order to determine the maximum possible cargo volume supported by a ship fleet.