Sustainable biomass supply chain optimization
Creators: name: Zahraee, Seyedmojib
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 350909
subject: Supply chains
classificationCode: 350999
subject: Transportation, logistics and supply chains not elsewhere classified

description: The supply of sustainable energy is one of the most serious challenges that humankind will confront over the coming decades, particularly because of the need to address climate change. Biomass renewable energy is a valuable energy source as an alternative to fossil fuels. The main barriers in biomass and biofuel development are the high cost of feedstock, lack of reliable supply, and other uncertainties, such as demand and raw material availability and plantation. The further expansion of biomass supplies to meet global energy demand can significantly affect environmental and socio-economic improvements in rural and metropolitan communities. The commercialization of the biomass industry is highly dependent on having a sustainable biomass supply chain (BSC). BSC planning can be carried out at strategic, tactical, and operational levels. These three decision levels must be integrated to address the knowledge gap. The strategic decision-making level refers to a decision where long-term investment is involved. Tactical decision-making level refers to medium-term decision decisions, usually monthly or weekly, within the constraint of a strategic decision. Operational decision-making level usually refers to a decision over a short time frame, ranging from hourly to weekly, that is within the limit of a tactical decision. Under each decision-making level, different approaches are applied to solving the BSC problems. The sustainability of BSCs is another critical issue that requires simultaneous consideration of economic, environmental, and social factors. The main contribution of this thesis is to develop and integrate dynamic simulation modeling and mathematical optimization methods by combining the three decision levels and three sustainability pillars. This thesis aims to establish a BSC optimized network to minimizes logistic and transportation costs (economic level), minimizes air carbon dioxide (CO2) emissions from transportation and production (environmental level), and promote job creation (social level). In order to achieve this goal, first, the key parameters in the optimal design of a sustainable BSC are identified. Second, dynamic and discrete agent-based simulation models to assess the economic and environmental effect of the BSC in the local and global context are developed. Then, using two evolutionary algorithms, a multi-objective mathematical model is developed to maximize the economic factor and environmental emissions.
 
In the first step, dynamic simulation modeling was implemented to assess the economic and environmental effects of the BSC in local and global contexts. The results showed that changing transportation and production technology can reduce CO2 emissions. In addition, results showed that the highest energy consumption and water use are for producing and transporting three products: biocompost, cellulose, and activated carbon. Those three products also have the highest sulfur oxides emissions. There is a need for well-planned management of the water–energy nexus in preprocessed production compared to intermediate and final production of the BSC. Results also suggested that governments should reduce greenhouse gas (GHG) emissions from production processes.

The global BSC using maritime transportation is one of the main sectors that has not been addressed in the published literature. The findings indicated that ship technology, size, and capacity affect BSC transportation costs and emissions from the global perspective. For example, variation in particulate matter emissions is low compared to pollutant gas emissions, such as GHG, nitrogen oxides, and sulfur dioxide. Therefore, low transportation costs and low use of carbon fuels are two priorities for the maritime industry. The findings of the dynamic simulation modeling can assist governments in developing policies and strategies for developing a sustainable biomass industry in the local and global markets. This dissertation presented geographic information system–integrated agent-based modeling to identify the optimal transportation mode and suppliers’ locations for biomass energy considering the road and rail networks. First, the optimal location of biomass energy plants was examined using a geographic information system–based location allocation model that minimized transportation distance and biomass delivery cost. The results showed that the total biomass delivery cost was mainly attributed to transportation cost, which confirmed the significant correlation between transportation distance and biomass delivery cost.

In the next step, two advanced evolutionary algorithms—nondominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO)—were adapted and tested using BSC data for Malaysia. The developed models were implemented to maximize profit, minimize environmental emissions, and promote job creation. The optimization approach indicated that the maximum profit of the proposed supply chain design is equal to $1.35*1010 in example 1 and $7*109 in example 2, which includes the highest emissions. The extended example achieves a better target value by reducing the 40% profit to reach the minimum amount of emissions from transportation and production in the BSC. In addition, five performance parameters were used to assess the performances of both algorithms. The nest central processing unit time is related to the MOPSO algorithm, which is equal to 697.86 and 2125.90 seconds for each example, respectively, compared to NSGA-II. Moreover, results showed that the NSGA‐II algorithm could obtain more Pareto solutions. Finally, the technique for order of preference by similarity to ideal solution was implemented to investigate the trade-off optimum design points obtained from the optimization algorithms solutions using two-objective problems. Results show that MOPSO was superior to NSGA-II; however, the NSGA‐II algorithm produced more Pareto solutions.

The COVID-19 pandemic that occurred during the middle of the PhD candidature provided an opportunity to explore the effects of the pandemic on the BSC. A literature synthesis was conducted to investigate the economic, environmental, and social effects of the COVID-19 pandemic on the biomass sector and provide practical recommendations for governments and stakeholders to consider the resiliency of the BSC. Since the effect of the COVID-19 pandemic on BSC is not the major focus of this dissertation, findings from the literature synthesis are attached in Appendix A.
 
Future research could address the uncertainty in other parameters of the model proposed in this study, for instance, in the quality of biomass, equipment failure and repair times, product demand, and prices. Such research must estimate the probability distribution functions for uncertain parameters and incorporate them into the simulation model.