A. A. Juan Perez, J. Panadero, C. Bayliss, L. Martins, A. Freixes, D. Raba
We review the main ideas behind the concept of ‘agile optimization’ (AO), and provide several examples of applications in the area of transportation and logistics (T&L). AO represent a novel paradigm that follows the following principles: (i) real-time execution; (ii) easy to implement and run using parallelization techniques; (iii) flexibility to deal with different problems and variants; (iv) parameter-less; and (v) specifically designed to run iteratively every few seconds or minutes as new streams of data arrive in a dynamic and connected environment. AO represents a breakthrough with respect to traditional optimization, simulation, and machine learning methods, which typically require long computation times –and, therefore, cannot efficiently deal with T&L scenarios using unmanned and self-driving vehicles and characterized by their dynamism and uncertainty. AO is based on the hybridization of biased-randomized heuristics and parallel computing.
Keywords: agile optimization, heuristics, biased randomization, transport and logistics, smart cities
Scheduled
GT10-2 Transport
September 5, 2019 12:00 PM
I3L10. Georgina Blanes building