We describe a novel approach for designing network control algorithms that incorporate traffic models. Traffic models can be viewed as stochastic predictions about the future network state, and can be used to generate traces of potential future network behavior. Our approach is to use such traces to heuristically evaluate candidate control actions using a technique called hindsight optimization...

The berth allocation problem (BAP) arising in maritime container terminals has received great attention in the literature over recent years. It has been largely modeled as an integer mathematical programming formulation to be adopted at a tactical level, where detailed equipment and manpower schedules, as well as real-time operational conditions are not explicitly modeled. In this paper, decisi...

This paper presents an off-line optimal trajectory planning for differential-drive rover through simulation of the dynamic model. The paper starts with the model dynamics of an actual rover built in our space science and technology lab (SSTLab) and controlled by simple PD controllers. Next, the proposed optimization technique used is presented which is called Incremented Particle-Swarm Optimiza...

Simulation Optimization (SO) is the use of mathematical optimization techniques in which the objective function (and/or constraints) could only be numerically evaluated through simulation. Many of the proposed SO methods in the literature are rooted in or originally developed for deterministic optimization problems with available objective function. We argue that since evaluating the objective ...

The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistic...

As a distributed parallel computing, cloud computing has an absolute advantage in accessing and processing of huge amount of data. How to assign all these virtual cloud computing resources to the user is a key technical issues, scholars have proposed greedy algorithm, FCFS, and other variety of algorithms to solve this problem. However, the algorithms just build a local optimal solution, there ...

An efficient exploration of the configuration space of a biopolymer is essential for its structure modeling and prediction. In this study, the authors propose a new Monte Carlo method, fragment regrowth via energy-guided sequential sampling (FRESS), which incorporates the idea of multigrid Monte Carlo into the framework of configurational-bias Monte Carlo and is suitable for chain polymer simul...

This survey considers the optimization of simulated systems. The simulation may be either deterministic or random. The survey reflects the author’s extensive experience with simulationoptimization through Kriging (or Gaussian process) metamodels using a frequentist (non-Bayesian) approach. The analysis of Kriging metamodels may use bootstrapping. The survey discusses both parametric bootstrappi...

We propose fully sequential indifference-zone selection procedures that are specifically for use within an optimizationvia-simulation algorithm when simulation is costly and partial or complete information on solutions previously visited is maintained. Sequential Selection with Memory guarantees to select the best or near-best alternative with a user-specified probability when some solutions ha...

Everyday robotics are challenged to deal with autonomous product handling in applications like logistics or retail, possibly causing damage on the items during manipulation. Traditionally, most approaches try to minimize physical interaction with goods. However, we propose to take into account any unintended motion of objects in the scene and to learn manipulation strategies in a self-supervise...