This is a serious game called Re-Organise. It is a cooperativeboard game about creating closed loops in the circular economy. The game represents an agro-industrial park in which different types of companies aim to use each other's waste streams as a material and/or energy resource. To do so, the players need to collaborate and (often need to) invest in processing technologies. The game is licenced CC-BY. To use it, the supplementary materials can be downloaded for printing. We kindly ask you to cite this game according to the pure reference.
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The viability of novel network-level circular business models (CBMs) is debated heavily. Many companies are hesitant to implement CBMs in their daily practice, because of the various roles, stakes and opinions and the resulting uncertainties. Testing novel CBMs prior to implementation is needed. Some scholars have used digital simulation models to test elements of business models, but this this has not yet been done systematically for CBMs. To address this knowledge gap, this paper presents a systematic iterative method to explore and improve CBMs prior to actual implementation by means of agent-based modelling and simulation. An agent-based model (ABM) was co-created with case study participants in three Industrial Symbiosis networks. The ABM was used to simulate and explore the viability effects of two CBMs in different scenarios. The simulation results show which CBM in combination with which scenario led to the highest network survival rate and highest value captured. In addition, we were able to explore the influence of design options and establish a design that is correlated to the highest CBM viability. Based on these findings, concrete proposals were made to further improve the CBM design, from company level to network level. This study thus contributes to the development of systematic CBM experimentation methods. The novel approach provided in this work shows that agent-based modelling and simulation is a powerful method to study and improve circular business models prior to implementation.
Technical conditions and actor behavior both affect the evolution of Industrial Symbiosis Networks (ISNs) that exchange local materials and energy in a Circular Economy. In order to design interventions that shape ISNs toward financially robust exchanges, it is necessary to understand the effects of different actor behaviors during waste exchange negotiations. This study aimed to show to what extent and how the financial robustness of ISNs is influenced by negotiation behavior of ISN firms. We created an agent-based model based on empirical data and literature, in which the Theory of Planned Behavior (TPB) can be added to a tit-for-tat negotiation process. The model showed that the added self-evaluation and feedback to behavioral intention and behavior of actors is crucial for the sta-bility of ISNs. In addition, model simulations revealed divergent financial results for waste suppliers when we compare different design scenarios, indicating that the model contributes to understanding effects of design interventions in ISNs. In the future, we will calibrate the model with more empirical evidence, and ex-tend the experiments with other scenarios.
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
