Solving logistic system design problem considering various biomass feedstock using two metaheuristic optimization methods

Bioenergy has become an important source of energy; it can be used as an alternative to fossil fuel energy, and it offers significant potential to alleviate climate change by reducing greenhouse gas emissions caused by the burning of fossil fuels. The Energy Independence and Security Act decrees the use of 21 billion gallons of advanced biofuel including 16 billion gallons of cellulosic biofuels by the year 2022. It is easy to observe that biomass can make a considerable contribution meet the energy demands. On the other hand, the supply of sustainable energy is one of the main challenges that must be met in the coming years if biomass is to alleviate the reliance on fossil fuels. In many ways, biomass is a unique renewable resource because in comparison to other renewable energy options, biomass can be easily stored and transported. This thesis presents two different models for the design optimization of the life-cycle of biomass logistics system through bio-inspired metaheuristic optimization considering multiple types of feedstocks. This work compares the performance and solutions obtained by two types of metaheuristic approaches: genetic algorithm and bee colony optimization. Compared to precise mathematical optimization methods, metaheuristics does not guarantee that a global optimal solution can be found on some types of problems. Similar problems to the one presented in this thesis have been previously solved using linear programming, mixed integer linear programming, and mixed integer programming methods. However, depending on the type of problem, these mathematical methods might require exponential computation time, which can result prices that are too high for practical purposes. Therefore, this thesis develops two types of metaheuristic approaches for the design optimization of the life cycle logistics system considering multiple types of feedstocks