SINCE typical mobile robotic vehicles have mobility sensors (such as LADAR or stereo) that can only acquire data up to a few tens of meters, a navigation system has no knowledge about the world beyond this sensing horizon. As a result, path planners that rely only on this knowledge to compute paths are unable to anticipate obstacles sufficiently early and has no choice than to plan inefficient ...

A model predictive control (MPC) strategy is developed to determine the optimal solution of the short-term refinery production planning problem. The main objective of the proposed algorithm is to maximize the total profit and to minimize the costs regarding the refinery process over a planning horizon. The refinery planning problem is solved in discrete time over a pre-determined prediction hor...

Constrained Markov decision processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various functions. In this paper, we consider the setting of episodic fixed-horizon CMDPs. We propose an online algorithm which leverages linear programming formulation repeated optimistic planning for finite-horizon CMDP prov...

This paper considers the scenario of supply chain with multiple products and multiple suppliers, all of which have limited capacity. We assume that received items from suppliers are not of perfect quality. Items of imperfect quality, not necessarily defective, could be used in another inventory situation. Imperfect items are sold as a single batch, prior to receiving the next shipment, at a dis...

We consider a dynamic facility location model in which the objective is to find a planning horizon, x*, and a first period decision, XI*, such that XI* is a first period decision for at least one optimal policy for all problems with planning horizons equal to or longer than x*. In other words, we seek a planning horizon, "r*, such that conditions after x* do not influence the choice of the opti...

This paper presents a receding horizon planning algorithm for vision-based navigation using bearings-only SLAM that adapts the planning horizon to the velocity of the information gained about the environment. Bearings-only SLAM has an inherent dynamic observability property such that specific motion is needed in order to determine the relative positions between the camera and obstacles. Thus, t...

This paper considers finite-horizon optimal control for multi-agent systems subject to additive Gaussiandistributed stochastic disturbance and a chance constraint. The problem is particularly difficult when agents are coupled through a joint chance constraint, which limits the probability of constraint violation by any of the agents in the system. Although prior approaches [1][2] can solve such...

This paper compares the implications of short and long horizon planning in dynamic optimization problems with the structure of a standard one-sector growth model if agents have incomplete knowledge about the production function. Agents know the output and rate of return at the current capital stock and use an estimation of the production function based on this knowledge to determine current con...