One metric of the intelligence of a system is its ability to perform tasks in the face of dynamic changes to the environment. This requires that an autonomous system be capable of responding appropriately to such changes. One such response is to effectively adapt the allocation of resources from planning to execution. By adapting the resource allocation between cognition and execution, an intel...

This paper investigates the integrated production and imperfect preventive maintenance planning problem. The main objective is to determine an optimal combined production and maintenance strategy that concurrently minimizes production as well as maintenance costs during a given finite planning horizon. To enhance the quality of the solution and improve the computational time, we reconsider the ...

We propose a multi-stage stochastic mixed integer programming formulation for the assignment of surgeries to operating rooms (ORs) over a finite planning horizon. We consider the demand for surgery and the duration of surgery to be random variables. The objective is to minimize three competing criteria: expected cost of surgery cancellations, postponements, and OR overtime. We discuss propertie...

Planning for distributed agents with partial state information is considered from a decisiontheoretic perspective. We describe generalizations of both the MDP and POMDP models that allow for decentralized control. For even a small number of agents, the finite-horizon problems corresponding to both of our models are complete for nondeterministic exponential time. These complexity results illustr...

We consider the dynamic order acceptance and capacity planning problem under limited regular and non-regular resources. The goal consists of maximizing the expected profits of the accepted projects within a finite problem horizon. Capacity planning is a useful tool to support tactical decisions such as due-date quotation, price quotation and hiring non-regular capacity. The way the projects are...

This paper addresses the short-term capacity planning problem in a make-to-order (MTO) operation environment. A mathematical model is presented to aid an operations manager in an MTO environment to select a set of potential customer orders to maximize the operational profit such that all the selected orders are fulfilled by their deadline. With a given capacity limit on each source for each res...

We develop an emission permits trading model where covered firms can (1) utilize rolling planning horizons to deal with uncertainty and (2) exhibit bounded responsiveness supply-side control policies. calibrate the reproduce annual market outcomes in EU ETS over 2008–2018 show that a finite horizon reconciles banking dynamics discount rates implied by futures contracts’ yield curves. It also re...

For a given problem, the optimal Markov policy over a finite horizon is a conditional plan containing a potentially large number of branches. However, there are applications where it is desirable to strictly limit the number of decision points and branches in a plan. This raises the question of how one goes about finding optimal plans containing only a limited number of branches. In this paper,...

We address in this paper the challenge of solving a nonhomogeneous infinite horizon Markov Decision Process (MDP) problem. More precisely, we seek an algorithm that, when given a finite subset of the problem’s potentially infinite data set, delivers an optimal first period policy. Such an algorithm can thus recursively generate within a rolling horizon procedure an infinite horizon optimal solu...

This paper steps back from the standard infinite horizon formulation of reinforcement learning problems to consider the simpler case of finite horizon problems. Although finite horizon problems may be solved using infinite horizon learning algorithms by recasting the problem as an infinite horizon problem over a state space extended to include time, we show that such an application of infinite ...