Akademik Veri Yönetim Sistemi
Designing engineering structures using stochastic optimization methods, Levent Aydin,H Seçil Artem,Selda Oterkus, Editör, CRC, New York , Florida, ss.10-23, 2020
This chapter presents the review of the seven most preferred stochastic optimization methods in detail for use in different industrial areas such as engineering, construction, automotive, textile, and biomedical. These include Genetic Algorithm (GA), Simulated Annealing, Differential Evolution, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony and Markov Chain Monte Carlo. Many industrial, economic, biological, and engineering problems can be accepted as stochastic systems, such as communication area, signal processing, geography, aerospace, banking. Stochastic optimization is process based on minimizing or maximizing the value of a statistical or mathematical function when one or more than one input parameters depends on random variables. GA is an adaptive search algorithm that mainly depends on evolutionary algorithm and generates high-quality solutions for some complex engineering and optimization problems. The chapter considers one-track bike-sharing systems with transshipment, multi-stage, and two-stage stochastic optimization models are proposed to specify the optimal number of bikes to appoint to every station at the beginning of the service.