PaCOS 2018 - International Workshop on the Synergy of Parallel Computing, Optimization and Simulation (PaCOS 2018)

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.
Simulation-optimization (SO) approaches include, but are 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.