International Workshop on Synergy of Parallel Computing, Optimization and Simulation, Genoa, Italien

Abbrevation

PaCOS

City

Genoa

Country

Italy

Start Date

2017-07-17

End Date

2017-07-21

Abstract

Most real–world problems are complex and stochastic and, thus, hard to approach. Such problems originate from a wide range of areas including manufacturing and production, logistics and supply chain management, healthcare, and many more. Simulation and optimization have traditionally been considered separately as alternative approaches to deal with such problems. The recent advances in computational power have promoted the proliferation of hybrid techniques that combine both approaches. The challenge is to design efficient and effective hybridization mechanisms taking advantage of great details provided by simulation as well as the ability of optimization methods to provide (near–)optimal solutions. On the other hand, SO applications can/might become increasingly large (parameter, variable, and objective spaces) and complex (cross–disciplinary and mixed discrete–continuous optimization), requiring the use of parallel computing techniques for efficient and effective execution. However, the design and implementation of parallel SO methods raise several issues such as problem decomposition, parallel sampling, synchronization between simulation and optimization processes, load balancing, scalability, etc. This workshop seeks to provide an opportunity for researchers to present their original contributions on the joint use of advanced single– and multi–objective optimization methods, simulation and distributed and/or parallel multi/many– core computing, and any related issues. The PaCOS Workshop topics include the following: Topics related to synergy between Simulation and Optimization include, but not limited to: – Statistical selection methods such as ranking and selection, and multiple comparison procedures; – Black–box search methods that directly make use of the simulation estimates of the objective function like random search algorithms, simulation–based single– and multi–objective metaheuristics (e.g. local search or evolutionary algorithms) (also called simheuristics), probability distribution model–based methods; – Meta–model based methods such as first– and second–order regression models and neural networks; – Gradient–based methods like stochastic approximation, etc. – Computational/theoretical studies reporting results on solving complex problems using SO techniques. – SO applications including healthcare, manufacturing, logistics, biological applications, advanced big data analytics, engineering design, etc. – Software frameworks for the design and implementation of SO techniques. Topics related to synergy between Simulation–Optimization and parallel computing include, but not limited to: – Parallelization techniques and advanced data structures for SO methods. – Parallel mechanisms for the hybridization of optimization and simulation. – Implementation issues of parallel SO on multi–core processors, accelerators, clusters, grids/clouds, etc. – Energy– and thermal–aware implementation of parallel SO methods.