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.
DOCUMENT
From the publisher's site: Abstract: This paper describes the implementation of an agile autonomous agent-based manufacturing system based on a grid. This grid contains production machines, represented by agents, capable to perform certain production steps. Products to be made are also represented by agents. Many different products can be made in parallel, each product having its own sequence of production steps. The whole manufacturing is based on interaction of agents living in a distributed environment. This paper explains the basic design considerations and includes a simple example as a proof of concept.
MULTIFILE
This paper describes an agent-based software infrastructure for agile industrial production. This production is done on special devices called equiplets. A grid of these equiplets connected by a fast network is capable of producing a variety of different products in parallel. The multi-agent-based underlying systems uses two kinds of agents: an agent representing the product and an agent representing the equiplet.
MULTIFILE
The Dutch main water systems face pressing environmental, economic and societal challenges due to climatic changes and increased human pressure. There is a growing awareness that nature-based solutions (NBS) provide cost-effective solutions that simultaneously provide environmental, social and economic benefits and help building resilience. In spite of being carefully designed and tested, many projects tend to fail along the way or never get implemented in the first place, wasting resources and undermining trust and confidence of practitioners in NBS. Why do so many projects lose momentum even after a proof of concept is delivered? Usually, failure can be attributed to a combination of eroding political will, societal opposition and economic uncertainties. While ecological and geological processes are often well understood, there is almost no understanding around societal and economic processes related to NBS. Therefore, there is an urgent need to carefully evaluate the societal, economic, and ecological impacts and to identify design principles fostering societal support and economic viability of NBS. We address these critical knowledge gaps in this research proposal, using the largest river restoration project of the Netherlands, the Border Meuse (Grensmaas), as a Living Lab. With a transdisciplinary consortium, stakeholders have a key role a recipient and provider of information, where the broader public is involved through citizen science. Our research is scientifically innovative by using mixed methods, combining novel qualitative methods (e.g. continuous participatory narrative inquiry) and quantitative methods (e.g. economic choice experiments to elicit tradeoffs and risk preferences, agent-based modeling). The ultimate aim is to create an integral learning environment (workbench) as a decision support tool for NBS. The workbench gathers data, prepares and verifies data sets, to help stakeholders (companies, government agencies, NGOs) to quantify impacts and visualize tradeoffs of decisions regarding NBS.
Elektrisch rijden staat aan de vooravond van een schaalsprong. De ambitie van zowel de Nederlandse overheid als internationale overheden is om binnen nu en 12 jaar alleen nog maar elektrische auto’s nieuw op de markt toe te laten. De elektrisch vervoer (EV) keten staat voor de grote uitdaging om deze schaalsprong op tijd met voldoende laadinfrastructuur te faciliteren. Nederlandse ketenpartners willen, net als de afgelopen jaren, koploper blijven op het gebied van EV-laadinfrastructuur en daarom goed voorbereid zijn op deze schaalsprong. De centrale praktijkvraag van de EV-ketenpartners is “Hoe kan de toekomstige laadbehoefte voor elektrische voertuigen in een snel groeiende markt met nieuwe gebruikersgroepen goed worden ingevuld?” Het doel van Future Charging is om bij te dragen aan de doorbraak van elektrisch rijden door kennis over de laadbehoefte van nieuwe gebruikersgroepen te ontwikkelen en toekomstig laadgedrag in een agent-based model te simuleren. Simulaties geven EV-ketenpartners concrete inzichten in effecten van toekomstscenario’s op het gebruik van laadinfrastructuur, de impact op het elektriciteitsnet en openbare ruimte. Deze kennis ondersteunt EV-ketenpartners bij de uitrol van toekomstbestendige laadinfrastructuur. In totaal brengt dit project 17 consortiumpartners bij elkaar waarmee de volledige EV-keten voor laadinfrastructuur vertegenwoordigd is: gemeenten, netbeheerders, laadpaal-exploitanten, energiebedrijven en gebruikers. De partners bieden hiermee een rijke praktijkomgeving waar continu kan worden geleerd over de veranderende laadbehoefte van verschillende gebruikersgroepen en in verschillende ruimtelijke settings: van grootstedelijk tot “laden in de regio”. Sinds 2014 beheert en monitort de Hogeschool van Amsterdam de laaddata voor G4/MRA-E. Meer dan 8,5 miljoen laadsessies zijn opgeslagen in een professioneel datawarehouse en middels beveiligde accounts toegankelijk voor onderzoek. Future Charging slaat de brug tussen theorie over laadbehoefte, laadgedrag en agent-based simuleren en de praktijk van laadinfrastructuur. Het resultaat is een praktisch toepasbaar simulatiemodel waarmee ontwerpstudies en praktijkcases worden doorgerekend.
Due to the exponential growth of ecommerce, the need for automated Inventory management is crucial to have, among others, up-to-date information. There have been recent developments in using drones equipped with RGB cameras for scanning and counting inventories in warehouse. Due to their unlimited reach, agility and speed, drones can speed up the inventory process and keep it actual. To benefit from this drone technology, warehouse owners and inventory service providers are actively exploring ways for maximizing the utilization of this technology through extending its capability in long-term autonomy, collaboration and operation in night and weekends. This feasibility study is aimed at investigating the possibility of developing a robust, reliable and resilient group of aerial robots with long-term autonomy as part of effectively automating warehouse inventory system to have competitive advantage in highly dynamic and competitive market. To that end, the main research question is, “Which technologies need to be further developed to enable collaborative drones with long-term autonomy to conduct warehouse inventory at night and in the weekends?” This research focusses on user requirement analysis, complete system architecting including functional decomposition, concept development, technology selection, proof-of-concept demonstrator development and compiling a follow-up projects.
