Optimization of green logistics using genetic algorithm approach
Green Logistics (GL) becomes critical in Supply Chain Management (SCM) due to it having less of an impact to the environment. Green Logistics optimization refers to the determination depot quantity, decreasing uncovered demand and CO2 emission reduction. To date, application of Genetic Algorithm (GA) has been proven to be very efficient and reliable in solving optimization problems; it is also capable of operating simultaneously with multiple solutions. Basically, GA imitates the natural evolution of a population with initial solutions. In this thesis a modified Genetic Algorithm is proposed to solve the multiple objective GL optimization problem. MATLAB software is used to validate and evaluate the proposed model. This work forms the basis for solving many other similar problems that occur in manufacturing and service industries. The final solution to this multiple objective problem is reached by using a set of Pareto solutions.
Villarreal, Gabriela Iturate, "Optimization of green logistics using genetic algorithm approach" (2015). ETD Collection for University of Texas, El Paso. AAI1593111.