this paper presents a new formulation for warehouse inventory management in a stochastic situation. the primary source of this formulation is derived from fp model, which has been proposed by fletcher and ponnambalam for reservoir management. the new proposed mathematical model is based on the first and the second moments of storage as a stochastic variable. using this model, the expected value...

Robust optimization considers optimization problems with uncertainty in the data. The common data model assumes that the uncertainty can be represented by an uncertainty set. Classic robust optimization considers the solution under the worst case scenario. The resulting solutions are often too conservative, e.g., they have high costs compared to non-robust solutions. This is a reason for the de...

Detection of moving objects around a mobile robot is important for safe navigation. This paper presents a robust technique for detecting moving objects using a laser ranger mounted on a mobile robot. After the initial alignment of the two consecutive laser scans, each laser reading is segmented and classified according to object type, stationary, non-stationary or indeterminate. Laser reading s...

In this paper, we analyze the performance guarantee of the forward Wagner-Whitin algorithm with rolling horizons. We establish several properties for the solution returned by the algorithm and show that the ratio of the heuristic solution to the optimal cost is bounded above by the degree of speculative motive. Further, we show that the bound is actually tight.

Bayesian inference requires all unknowns to be represented by probability distributions, which awkwardly implies that the probability of an event for which we are completely ignorant (e.g., that the world’s greatest boxer would defeat the world’s greatest wrestler) must be assigned a particular numerical value such as 1/2, as if it were known as precisely as the probability of a truly random ev...

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for...

Robust design has been gaining wide attention and its applications have been extended to making reliable decisions when designing complex engineering systems under a multidisciplinary design environment. Though the usefulness of robust multidisciplinary design is widely acknowledged, its implementation is rare. 1 One of the reasons is due to the complexity and computational burden associated wi...

Bayesian inference requires all unknowns to be represented by probability distributions, which awkwardly implies that the probability of an event for which we are completely ignorant (e.g., that the world’s greatest boxer would defeat the world’s greatest wrestler) must be assigned a particular numerical value such as 1/2, as if it were known as precisely as the probability of a truly random ev...

Actuators of robot operate in the joint-space while the end-effect or of robot is controlled in the task-space. Therefore, designing a control system for a robotic system in the task-space requires the jacobian matrix information for transforming joint-space to task-space, which suffers from uncertainties. This paper deals with the robust task-space control of electrically driven robot manipula...