Modeling and Optimizing the Logistic Routing of Biomass Side-Flows for Circular Bioeconomy : Applying a Genetic Algorithm-Based Discrete Event Simulation Approach
Eloranta, Kasper (2023)
Eloranta, Kasper
2023
Tuotantotalouden DI-ohjelma - Master's Programme in Industrial Engineering and Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2023-10-12
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202309298538
https://urn.fi/URN:NBN:fi:tuni-202309298538
Tiivistelmä
The acceleration of climate change has led regulators to impose constraints and incentives, encouraging both companies and individuals to align their actions with the goals of a green transition. There's a growing imperative to reduce waste and emissions while simultaneously maximizing the utilization of waste and by-products as valuable resources. These trends are driving societies toward adopting principles of the Circular Economy (CE). As the consumption of fossil energy sources needs to be reduced, the development and production of renewable energy sources become essential. Biogas, a form of renewable energy, offers an efficient way to utilize agricultural bio-based waste and side-flows, contributing to waste reduction and providing an alternative to fossil fuels. This approach aligns with the principles of the Circular Bioeconomy (CBE). However, establishing a circular supply chain for biogas production from biomasses is a complex endeavor. To ensure effective and sustainable operations within this system, logistics planning is crucial. This involves determining where, when, and how much biomass should be imported and transported for biogas production. It presents a multi-objective optimization problem, aiming to guarantee continuous biogas production while minimizing the costs associated with transporting biomass flows and other occurred inefficiencies.
This study models and simulates a circular supply chain that produces biogas from agricultural biowastes and side-flows. The supply chain in question encompasses a biogas plant, which is currently in the design phase but will later be constructed in the Kanta-Häme region of Finland. It also involves farms located in nearby areas, which are potential sources of biomass. The modeling aspect aims to optimize the logistics of collecting and transporting various types of biomasses from the farms to the biogas plant for use in the biogas production process. To achieve this optimization, a simulation model that utilizes a genetic algorithm (GA) has been developed. The objectives of the study include generating knowledge about both general and case-specific factors that should be considered when planning logistic routing to align with the principles of the Circular Bioeconomy (CBE). Another objective is to create an optimized routing plan for collecting resources for biogas production at the plant. Additionally, this study seeks to contribute to understanding of how time-critical factors, such as the quality of biomasses in terms of their energy potential, impact the optimization of logistic routing.
The study successfully maps the biomass potentials near the biogas plant and develops a methodology for optimizing the logistic routing of biomasses using the simulation model and the genetic algorithm. The study achieves the objective of generating an optimized routing proposal for the biogas plant in the case study, ensuring a continuous biogas production process. However, it is important to note that the results of the study may be suboptimal due to the complexity of the optimization problem and the time constraints of the research. The time-critical nature of biomasses should be considered when planning logistic systems for biogas production to avoid unnecessary costs. Limitations of the study include the use of non-farm-specific datasets, simplifying assumptions made in the simulation model, and time constraints that influenced the optimization stopping criteria and the potential for suboptimal results. Further research on this topic is necessary, either with more advanced methods or by allocating more computational time for optimization.
This study models and simulates a circular supply chain that produces biogas from agricultural biowastes and side-flows. The supply chain in question encompasses a biogas plant, which is currently in the design phase but will later be constructed in the Kanta-Häme region of Finland. It also involves farms located in nearby areas, which are potential sources of biomass. The modeling aspect aims to optimize the logistics of collecting and transporting various types of biomasses from the farms to the biogas plant for use in the biogas production process. To achieve this optimization, a simulation model that utilizes a genetic algorithm (GA) has been developed. The objectives of the study include generating knowledge about both general and case-specific factors that should be considered when planning logistic routing to align with the principles of the Circular Bioeconomy (CBE). Another objective is to create an optimized routing plan for collecting resources for biogas production at the plant. Additionally, this study seeks to contribute to understanding of how time-critical factors, such as the quality of biomasses in terms of their energy potential, impact the optimization of logistic routing.
The study successfully maps the biomass potentials near the biogas plant and develops a methodology for optimizing the logistic routing of biomasses using the simulation model and the genetic algorithm. The study achieves the objective of generating an optimized routing proposal for the biogas plant in the case study, ensuring a continuous biogas production process. However, it is important to note that the results of the study may be suboptimal due to the complexity of the optimization problem and the time constraints of the research. The time-critical nature of biomasses should be considered when planning logistic systems for biogas production to avoid unnecessary costs. Limitations of the study include the use of non-farm-specific datasets, simplifying assumptions made in the simulation model, and time constraints that influenced the optimization stopping criteria and the potential for suboptimal results. Further research on this topic is necessary, either with more advanced methods or by allocating more computational time for optimization.