The rolling horizon method has been proposed to address the integrated production planning and scheduling optimization problem. Since the method can generally result in small-scale optimization model and fast solution, it has been used in a number of applications in realistic industrial planning and scheduling problems. In this paper, it is first pointed out that the incorporation of valid prod...

This paper investigates a multi-period rectilinear distance 1-center location problem considering a lineshaped barrier, in which the starting point of the barrier follows the uniform distribution function. In addition, the existing points are sensitive to demands and locations. The purpose of the presented model is to minimize the maximum barrier distance from the new facility to the existing f...

Abstract. This paper derives a portfolio decomposition formula when the agent maximizes utility of her wealth at some finite planning horizon. The financial market is complete and consists of multiple risky assets (stocks) plus a risk free asset. The stocks are modelled as exponential Brownian motions with drift and volatility being Itô processes. The optimal portfolio has two components: a myo...

This paper considers undiscounted Markov Decision Problems. For the general multichain case, we obtain necessary and sufficient conditions which guarantee that the maximal total expected reward for a planning horizon of n epochs minus n times the long run average expected reward has a finite limit as n -* oo for each initial state and each final reward vector. In addition, we obtain a character...

Solving finite-horizon Markov Decision Processes with stationary policies is a computationally difficult problem. Our dynamic dual decomposition approach uses Lagrange duality to decouple this hard problem into a sequence of tractable sub-problems. The resulting procedure is a straightforward modification of standard non-stationary Markov Decision Process solvers and gives an upper-bound on the...

We propose a novel baseline regret minimization algorithm for multi-agent planning problems modeled as finite-horizon decentralized POMDPs. It guarantees to produce a policy that is provably at least as good as a given baseline policy. We also propose an iterative belief generation algorithm to efficiently minimize the baseline regret, which only requires necessary iterations so as to converge ...

Decision-theoretic planning with risk-sensitive planning objectives is important for building autonomous agents or decisionsupport agents for real-world applications. However, this line of research has been largely ignored in the artificial intelligence and operations research communities since planning with risksensitive planning objectives is much more complex than planning with risk-neutral ...