A new approach to sequential decision problems under uncertainty named dynamic focus programming is proposed with the theory of choice. In programming, there are two distinct evaluation systems: Positive and negative ones. Each possible path consisting a sequence from initial stage final associated states examined. positive system, for each in stage, if starting it can bring about relatively lo...

in recent decades, information technology systems and tools play an essential role in improving the method for responding the requests from managers. decision support system as an important tool has the fundamental role in organizations. in the team of software project allocating people to tasks has a noticeable effect on profits. a decision support system can help managers in making appropriat...

Censored (or truncated) observations are quite prevalent in practice. How does the existence of imperfect observations affect optimal decisions? We consider this problem in the setting of a general finite-horizon Bayesian sequential decision process. We first prove a general derivative result that resembles the classic envelope theorem. We then show that for a class of Bayesian sequential decis...

Abstract. Any intelligent system performing evidence-based decision making under time pressure must negotiate a speed-accuracy trade-off. In computer science and engineering, this is typically modeled as minimizing a Bayes-risk functional that is a linear combination of expected decision delay and expected terminal decision loss. In neuroscience and psychology, however, it is often modeled as m...

This study aimed at presenting a method for formulating optimal production, repair and replacement policies. The system was based on the production rate of defective parts and machine repairs and then was set up to optimize maintenance activities and related costs. The machine is either repaired or replaced. The machine is changed completely in the replacement process, but the productio...

In this paper, we demonstrate the use of stochastic dynamic programming to solve over-constrained scheduling problems. In particular, we propose a decision method for efficiently calculating, prior to start of execution, the optimal decision for every possible situation encountered in sequential, predictable, overconstrained scheduling domains. We present our resuits using an example problem fr...

In this paper, we demonstrate the use of stochas-tic dynamic programming to solve over-constrained scheduling problems. In particular, we propose a decision method for eeciently calculating, prior to start of execution, the optimal decision for every possible situation encountered in sequential, predictable, over-constrained scheduling domains. We present our results using an example problem fr...

In this paper we describe research in the areas of maintenance and warranty decision-making for gas turbines used in power plants. We first frame the problem within a larger context of equipment monitoring, diagnostics and maintenance. We then focus on describing and modeling economic issues related to warranty maintenance service from the perspectives of both the consumer (power plant owner) a...

In this study, we deal with a model to study the effect of PM carried out by both parties on a repairable product sold under a non-renewing, free-repaired warranty policy. During the warranty period, the seller will repair the deteriorated but repairable product and charges no fee to the buyer. After the warranty period, the deteriorated product is also repaired by the seller but the buyer will...

Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (approximately) optimal dynamic programming solutions. Much work in recent years has focused on improving the efficiency of these dynamic programming algorithms by exploiting symmetries and factored or relational representat...