A Hybrid of Multi-Objective Optimization and System Dynamics Simulation for Straw-to-Electricity Supply Chain Management under the Belt and Road Initiatives
The Belt and Road Initiative (BRI) provides immense opportunities for agro-waste utilization among countries situated along the routes. However, there is a lack of design of motivational mechanisms to put it into managerial practice. This study uses agro-straw as the typical agro-waste to structure a hybrid of multi-objective optimization and system dynamics simulation for optimizing the structure of straw-to-electricity supply chain and designing motivational mechanisms to enhance its
... ility. Since existing studies on the design of motivation mechanisms mainly stressed static motivation, two different dynamic subsidy mechanisms are devised in this study to facilitate the stable collaboration among stakeholders involved in the supply chain. A case study is provided to demonstrate the hybrid method. Discussion about the limitations of the study lays the foundation for further improvement. emissions in the supply chain for promoting its sustainable development. Consequently, this study designs motivational mechanisms that finely adjust the level of supply chain engagement by different stakeholders to steer supply chain operations towards stability. The remaining parts of the study are structured as follows: Section 2 gives a literature review regarding supply chain management of straw biomass utilization. Section 3 presents the optimization and system dynamics model. Section 4 addresses the case background and data source. Results and discussions are given in Section 5. Lastly, conclusions and research limitations are expressed in Section 6. Literature Review Currently, studies on the supply chain management of straw biomass utilization focus on two aspects: supply chain network optimization and design of motivational mechanisms for supply chain stakeholders . Mobini et al.  used discrete event simulations and integrated factors, such as the equilibrium moisture content and carbon emissions, to optimize the logistic routes of the straw biomass supply chain. Meanwhile, Yu et al.  computed and decomposed straw collection costs and incorporated the GIS model for planning the allocation of biomass power plants. Zhao and Li  conducted a similar study, where costs of logistics and associated carbon emissions were used as the objective functions to construct a 0-1 bi-objective integer programming model for determining the allocation of biomass power plants. The GIS model was also employed by Chiueh et al.  who analyzed the impacts of straw drying pre-treatment on the supply chain cost and carbon emissions to optimize the transportation routes. Then, Delivand et al.  adopted GIS and multi-objective decision-making to optimize the logistic network for southern Italy's straw-to-electricity supply chain based on the logistic cost minimization. Roni et al.  built a hub-and-spoke supply chain network and undertook supply chain structure optimization by using biomass co-firing as the energy source. Turki et al.  further transformed the hub-and-spoke supply chain into a closed-loop supply chain, and proposed optimization model to enhance its sustainability. Vance et al.  used P-graph Framework with minimizing cost, ecological footprint, and energy input to design a reverse supply chain based on agro-waste electricity generation. Examining the costs of minimal biomass electricity generation was also an objective for Singh  . He also used factors such as average fuel distribution or straw collection as the constraints for determining the optimum capacity of biomass power plants and the straw collection radius. In terms of the motivational mechanism design for supply chain stakeholders, Yan et al.  devised a subsidy scheme for biomass power plants based on the principal-agent theory while considering the impacts of straw collection and storage costs on the plants. The agent-based approach was also employed by Luo et al.  to combine with game theory for analyzing the villagers' willingness of providing straw feedstocks for the biomass-based power supply chain. Xue and Wang  built a dynamic model based on the game theory to redesign the motivation mechanisms.