| Title |
An investigation into modelling approaches for industrial symbiosis: A literature review and research agenda |
| ID_Doc |
18412 |
| Authors |
Demartini, M; Tonelli, F; Govindan, K |
| Title |
An investigation into modelling approaches for industrial symbiosis: A literature review and research agenda |
| Year |
2022 |
| Published |
|
| DOI |
10.1016/j.clscn.2021.100020 |
| Abstract |
Industrial symbiosis is a promising business model that pushes companies to move from linear production to a circular path. However, this transformation is complex because it requires companies to rethink and redesign their supply chains by considering the involvement of new stakeholders, the introduction of waste and their impact on product development, economic investments, and technological resources. As a result, companies need innovative modelling tools able to capture, investigate, and quantify the results of these changes and to work on the strategic planning, design, implementation, and management of their industrial symbiosis networks. This paper presents a systematic literature review to investigate the most used modelling approaches to analyse industrial symbiosis, and to research their characteristics in terms of simulation methods, interaction mechanisms, and simulations software. Findings from our research suggest that an Agent-Based System Dynamics hybrid approach is an appropriate method for industrial symbiosis design and analysis. The paper also presents a theoretical framework that aims at supporting researchers and practitioners approaching industrial symbiosis and, in conclusion, a research agenda is suggested for scholars. |
| Author Keywords |
Industrial symbiosis; Hybrid modelling and simulation approach; Literature review; System dynamics; Agent -based modelling; Research agenda |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001044649000011 |
| WoS Category |
Operations Research & Management Science |
| Research Area |
Operations Research & Management Science |
| PDF |
https://doi.org/10.1016/j.clscn.2021.100020
|