Shaping Regional Industry-Infrastructure Networks An Agent Based Modelling Framework

Transcription

1 2006 IEEE Conference on Systems, Man, and Cybernetics October 8-11, 2006, Taipei, Taiwan Shaping Regional Industry-Infrastructure Networks An Agent Based Modelling Framework Igor Nikolic, and Gerard P.J. Dijkema Abstract Around the world, Regional Development Authorities (RDA s) have the responsibility for sustainable regional industry-infrastructure network development. The structure, content and behaviour of these Large Scale Socio-Technical systems or λ-systems, however, emerge from the operational, tactical and strategic decisions by a variety of other stakeholders over which the RDA has no control. A generic Agent Based Modelling Framework has been developed wherein (a) any λ-system can be represented as a Multi-Agent System and (b) their evolution can be simulated subject to a variety of external regimes. Using this generic Framework, the development of a real greenfield industry/infrastructure network in the Dutch Eemsdelta is modelled and simulated to support the RDA and other stakeholders involved. First results are presented I. INTRODUCTION A. Background and Objectives EGIONAL clusters of industrial activity are the R backbone of modern economies. Around the world, Regional Development Authorities (RDA s) are responsible for their continued prosperity [1]. In the Eemsdelta, for example, located in the very North of Holland, Groningen Seaports is responsible for two industrial areas, the Eemshaven and Delfzijl-Oosterhorn. The latter hosts chemical, polymer and metal companies with energy-intensive facilities that face severe global competition. In the Eemshaven, a new bio-based cluster is developing. Such regional clusters are Large Scale Socio-Technical systems or λ-systems [2],[3],[4],[5]. In a single λ-system a physical network or Technosphere is intricately connected with a social network or Sociosphere. The former comprises manufacturing installations linked by infrastructures; the latter consists of operating companies, investors, governments, RDAs, customers, suppliers, service providers etc. and their relations. Manuscript received March 15, The authors acknowledge the support received from the Next Generation Infrastructures Foundation ( This work is executed under the Dynamics and Design of Flexible Material Processing Networks project of the Understanding Complex Infrastructures sub-programme. The support of the CostaDue ( project organization and Groningen Seaports is gratefully acknowledged. Igor Nikolic is with the Energy and Industry section of the Technology, Policy and Management Faculty, Delft University of Technology, The Netherlands, Jaffalaan 5, 2600 GA Delft (phone: ; fax: i.nikolic@ tudelft.nl). Gerard P.J. Dijkema is with the same organization. (phone: ; fax: g.p.j.dijkema@ tudelft.nl). To date, knowledge on the evolution of λ-systems is largely qualitative; quantitative simulation of λ-system evolution appears to be lacking [5]. Therefore, we are investigating the modelling of regional industry-infrastructure networks as λ-systems to allow the simulation of the inception and evolution of Greenfield industrial regions and the possibilities for transition of Brownfield industrial regions. By thus elucidating λ-system evolution may help investors and governments to shape the emergence, growth and transition of prosperous regional clusters. The foundations of this work have been published in [6][7]and are extensively described in [5]. In this paper, firstly a succinct overview is given of the status of relevant knowledge domains that provide the foundations of the work. Secondly, the Action-Oriented Industrial Ecology Framework is presented, with a focus on the regional industrial clusters in the Eemsdelta, the Netherlands. Finally, first simulation results from using the Agent-Based Modelling Framework for the Eemsdelta-case are presented and conclusions drawn. B. Foundations The theoretical foundations of the AOIE framework for the analysis and shaping of λ-systems evolution include the Industrial Ecology Paradigm, Complex Adaptive Systems theory, Mathematical Network Theory, Agent Based Modelling, Gaming, Knowledge Engineering and Artificial Intelligence. These will be briefly introduced. The body of knowledge on analysis of λ-systems has only recently emerged. In industrial ecology (IE), useful paradigms have been postulated on the required state of λ-systems (sustainability) and preferred structure (networked ecosystems) [8]. In Complex Systems theory [5] it has been realised that evolving networked systems exhibit or develop so-called emergent characteristics system structure, behaviour and performance that were not a-priori postulated or designed into these systems they emerge when nodes and links connect and grow into a network [9][10]. Since network components exhibit behaviour in response to their environment the external world or other network components, they are adaptive. This is the case in evolving regional clusters. In the growing IE body-of-knowledge analysis methods on the present state of the physical part of λ-systems abound [11]. In the situation where the dynamics of systems are important, and where system design, not analysis is called for, IE approaches are largely inadequate [12][13][5]. Action-oriented Industrial Ecology requires one to shift focus and address Sociosphere and Technosphere in concert. By recognising the inherent complexity of λ-systems one may /06/$ IEEE

2 start analysing their evolution. A modelling strategy must be used that, whilst built on solid technological foundations, allow emergence of steered but un-programmed system evolution. The Sociosphere of λ-systems can be represented as a Multi-Agent System, where a variety actors takes part in operational, tactical or strategic decision-making on the λ-system. These decisions may directly affect the Technosphere or part thereof, or change its structure or content via changing relations, contracts in the Sociosphere. One example, for instance is the change of ownership of production facilities. While the physical infrastructure has remained largely unchanged, the organisation, job profiles and system performance have changed considerably. Modelling a λ-system as a Multi-Agent System results in an Agent-Based Model (ABM) suitable to generate simulated runs of system evolution using suitable software such as Repast [14] In any ABM, when communication between two agents is simulated, a formal interface is required to prevent ambiguity about data. This traditionally has been done on a case-by-case basis. The Artificial Intelligence community has addressed this problem by developing ontologies for knowledge representation. Ontologies are formal descriptions of entities and their properties, relationships, constraints and behaviour that are not only machine-readable but also machine-understandable. The meaning is stored not only in subclass-relationships ( is a, e.g., apple is a fruit, red is a colour) but also in property relationships ( has a, e.g., an apple has a red colour). In other words: an ontology contains explicit formal specifications of the terms in the domain and relations among them [15]. As indicated above, the actual mechanics of a λ-system's evolution is the decision making at Agent level. Ontologies provide a representation for the entities that decisions are made on. This separates domain knowledge (captured in the ontology) from the operational knowledge (decision making rules). The decision making rules are the intelligence of the Agents. There are several ways to formally model the intelligence of agents [16]. Rule Based Reasoning [17] is commonly used in expert systems to model tacit knowledge. In cases where agents are optimising or exhibit strategic behaviour mathematical game theory can be used to model agent behaviour. Mathematical optimisation is used to complement the Expert System approach. Game theoretical concepts that can be used are Stackelberg and Inverse Stackelberg games [18][19], Nash bargaining games [20] etc. These games can be played to reach local optima, Pareto optima or system level optima. C. Knowledge management The foundations above provide the building blocks for a Framework for AOIE. Therein, the elucidation and shaping of λ-systems evolutionary dynamics starts with the application of knowledge engineering principles. Any λ-system is addressed as a knowledge application that requires its own language and vocabulary (Figure 1) for adequate representation and simulation. Fig. 1. Action Oriented Industrial Ecology Framework (an elaborate explanation of the Framework is available in [5]). This AOIE framework allows for data collected through a social process in the knowledge application loop to be formalized and modelled in the lower, simulation loop. By using a carefully constructed generic ontology, it is possible to capture case specific information, while retaining interoperability between cases. Next to providing the structure, the ontology serves as a database of knowledge instances. This knowledge base is used to configure the ABM simulation. D. Simulation structure In the simulation step, the system decomposition formalised in the ontology is translated into an Agent Based structure. The set-up of the knowledge application (decomposition and formalisation method) provides the format of the problem structure. Examples of decision making processes and required data in the four levels of an agent are presented below. At the Identity Level, the components define the Agents Identity. This level answers questions such as : Which Technology do I implement? ; Which main economic model do I use? ; How and When do I want to change these components? ; The technical Identity of the Agent describes whether this is a Production, Infrastructure, Government etc. type of Agent. It selects the technical components from a subset of all possible technological components available in the model. At the Strategic Level, the following types of questions are answered: What is the general functioning of the technology unimplemented? ; What is the economic profile of this Technology? ; Which specific version of the Technology should I implement, considering the long term market developments for products and resources and the costs of the technology?

3 At this level the Reference Resources and Reference Products are defined for the technology [13]. This effectively describes the identity of the process, such as a process for making automotive fuels from crude oil, conforming to the Ontology. All relevant economic data on (investment, running, and decommissioning costs, current and expected market prices etc) are considered at this level At the Tactical Level, the medium term decisions are made. Exampes of questions answered are: Which specific technical configuration will I employ, at which capacity level do I operate the process? ; What are my costs and earnings?, When do I invest to optimise or scale the process at hand? Most technologies are scalable, with minimum and maximum capacities. Many others are able to switch between operational modes. For example, Combined Heat and Power plants can alter the ration of power and heat produced. Each transition in state has an associated cost and time. Other Economic data at this level are prices of Resources and products At the Operational Level, very short term, practical decisions are made. They answer questions of the Agents like: Do I have the Resources that I need to produce my Products? ; Do I have enough cash flow to afford the Resources, and am I making enough for sales of my Products? ; How do I have to adapt my Sourcing and Sales contracts to optimise my cash flow? At this level the Technology is represented by a transformation matrix. In it the exact stoichiometry of the process is encoded. It states that for each unit of Resources, x units of Product can be produced, requiring y units of utilities and producing z units of waste. The availability of resources, cash flow issues and contracts are considered at this level. E. Agent Network Evolution Resulting from the interactions between different levels within an Agent, in the process of following its Decision Making, Agents are capable of creating many different types of interactions. This is summed up in Figure 3. Fig. 4. Multidimensional agent network. In figure 3 we see four levels of connections between Agents. These are: (1) Social relations at the Identity level. Do we belong to the same social group? (2) Cooperation/Alliances at the Strategic level. Who do we trust? (3) Infrastructural connections at the Tactical level. Which pipeline we connect to, and which bank do we use? (4) Contracts and resource/product streams at the Operational level. Which specific resource are we buying? Each of these connections has its own properties such as duration, distance, price, capacity strength, etc. Exact details are specified in the Ontology. From these interactions between the Agents a complex, a multidimensional network emerges as illustrated in figure 4. This network has emergent structure and dynamics. Different Agents communicate at different levels, limit each others decision space and behaviour. Since Agents influence and depend on each other, they form a coupled fitness landscape of each other [21]. Running the discrete simulation, the life-cycle of Agents is followed and the evolution of the network in time is observed. II. EEMSDELTA CASE Fig. 3. Types of connections between agents. A. Case description In the Eemsdelta both the RDA and the provincial government foster economic development o the Groningen region. This amounts to the question How to attract interesting companies to the region and increase its prosperity? The provincial government and the RDA have recognized the emerging interest and potential for biomass based energy and chemicals production. In order to facilitate this, the CostaDue project has been created to facilitate and steer the realisation of a bio-based industrial cluster in the Eemsdelta. The Eemsdelta hosts the Eemshaven harbour, and the Delfzijl port complex, which is one of the main heavy processing sites in the Netherlands. A unique property of this location is that it ample opportunities for Greenfield development and synergy are present. In order to explore the technological options possibilities for the bio-based cluster CostaDue project organization has

4 organized a social process, involving some 60 key stakeholders. This has resulted in a list of 17 realistic technological options. The authors were invited to support this process by developing a position paper on cluster development. has been performed (see figure 6.). It is obvious that the mix of technological options identified in the CostaDue social process leads to to creation of 4 distinct clusters. B. Simulation A proof-of-concept model has been presented elsewhere [5][7]. In this paper the approach is ilustrated in a real case study. In the simulation the design space for an industrial cluster is explored using the technological options identified in the CostaDue social process [22]. This process was primarily concerned with qualitative exploration of technological options suitable for a bio-based cluster. As a consequence, the simulation addresses the possible emerging network structures by connecting material and energy flows. In the simulation presented, only the operational level is implemented. The tactical, strategic and identity levels are not considered. These levels will be explored in a later stadium. In total, 17 technological options have been identified as desirable for the region [22]. These options have been described in the ontology, and initialised as agents. During the simulation, agents are selected at random and added the to the region. Agents reason about the types of resources needed to produce their products, and attempt to establish material and energy flow connections with agents supplying them. This in effect creates a supply chain network. C. Results After adding a number of agents to the network the structure of the possible of mass and energy flows emerges, as presented in figure 5. Fig. 5. Possible mass flow network. This representation does not offer a lot of information as far as structure is concerned. Considering that the goal of the RDA is to create a well connected cluster, a cluster analysis using the Fruchterman-Reingold graph layout algorithm [23] Fig. 6. Cluster analysis on the mass flow network. D. Interpretation and Discussion In Industrial Ecology a high level of connectedness between companies is seen as a desired cluster characteristic. Thus, at first glance the cluster analysis (figure 6) suggests that the outcome of the CostaDue social process has been unsuccessful in providing a nucleus for cluster development. The goal of the process is to foster economic development of the region by creating a bio-based cluster. Analysis shows that with the given technological options it is not possible to form a single closely integrated cluster. However, in this case, the RDA is the port authority. An industrial cluster that is not fully connected in terms of material flows implies that a large part of the materials converted need to be imported or exported. Considering that the tax on the amount of materials that pass through the harbour is an important source of income for the port authority, this is actually a positive outcome. Furthermore, considering the relatively small number of individual companies, and the relatively low diversity in technology types, four different clusters reduce the interdependencies, and increase the regions robustness. The question then rises, what would the drivers be for a company to choose the Groningen seaport location as a place of business, if they cannot attain plentiful and cheap resources locally. This creates an interesting conflict of interest that will be investigated further in future work. In addition, what would happen in case of a company exhibits interests in the region which, once established, would create connectivity between sub-clusters. A first impression of such an event, is obtained by adding

5 a forest-based industry agent to the network. This leads to an expected increase in connectivity, and a thus reduction in the number of clusters from 4 to 3. See figure 7. Fig. 7. Network clusters, addition of a suitable extra agent. III. CONCLUSIONS AND OUTLOOK A generic Agent Based Modelling Framework has been developed wherein (a) any λ-system can be represented as a Multi-Agent System and (b) their evolution can be simulated subject to a variety of external regimes. The development of a real greenfield bio-based industry network in the Dutch Eemsdelta is simulated to support the RDA and other stakeholders involved. The first results, based on mass and energy flow connections only suggest emergence of four distinct clusters. From an Industrial Ecology perspective, this is an sub optimal situation. However, considering that the RDA is the port authority that levies taxes on port imports and exports, limited clustering may be favourable. Additional simulations confirmed that the results offer guidelines for searching other potential clustering partners. Amongst others, this will be elaborated in future work by exploring the Tactical, Strategic, and Identity levels of Agents. REFERENCES [1] G.P.J. Dijkema, D.J. van Zanten, and J. Grievink, Public roles and private interests in petrochemical clusters. Model-based decision support of the Regional Development Board, in Trans IChemE, Part A: Chemical Engineering Research and Design, 83 (A6), , [2] W.E. Bijker, T.P. Hughes, and T.J. Pinch, The Social construction of technological systems: new directions in the sociology and history of technology, Cambridge, Mass.: MIT Press, [3] T.P. Hughes, The evolution of large technological systems, in: Bijker WE, Hughes T, Pinch TJ (eds.), The social construction of technological systems. New directions in the sociology and history of technology. Cambridge: MIT Press; pp , [4] I. Nikolic, G.P.J. Dijkema, K.H. van Dam, and Z. 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