Optimal policy with general signal extraction

Optimal Policy with General Signal Extraction

Optimal nonlinear policy: signal extraction with a non-normal prior

We study optimal merger policy in a dynamic model in which the presence of scale economies implies that firms can reduce costs through either internal investment in build- ing capital or through mergers. The model, which we solve computationally, allows firms to invest or propose mergers according to the relative profitability of these strategies. An antitrust authority is able to block mergers at some cost. We examine the optimal policy when the antitrust authority can commit to a policy rule and when it cannot commit, and consider both consumer value and aggregate value as possible objectives of the antitrust authority. We find that optimal policy can differ substantially from what would be best considering only welfare in the period the merger is proposed. We also find that the abil- ity to commit can lead to a significant welfare improvement. In general, antitrust policy can greatly affect firms` optimal investment behavior, and firms` investment behavior can in turn greatly affect the antitrust authority`s optimal policy.

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Stochastic optimal control of agrochemical pollutant loads in reservoirs for irrigation

Determining the optimum replacement policy for Holstein dairy herds in Iran

Optimal and Near Optimal Control of a Two-Part-Type Stochastic Manufacturing System with Dynamic Setups

Optimal fiscal substitutes for the exchange rate in monetary unions

Financial Shocks and Optimal Policy

Testing for tax smoothing in a general equilibrium model of growth

Optimal causal policies maximizing the time-average reward over a semi-Markov decision process (SMDP), subject to a hard constraint on a time-average cost, are considered. Rewards and costs depend on the state and action, and contain running as well as switching components. It is supposed that the state space of the SMDP is finite, and the action space compact metric. The policy determines an action at each transition point of the SMDP. Under an accessibility hypothesis, several notions of time average are equivalent. A Lagrange multiplier formulation involving a dynamic programming equation is utilized to relate the constrained optimization to an unconstrained optimization parametrized by the multiplier. This approach leads to a proof for the existence of a semi-simple optimal constrained policy. That is, there is at most one state for which the action is randomized between two possibilities; at all other states, an action is uniquely chosen for each state. Affine forms for the rewards, costs and transition probabilities further reduce the optimal constrained policy to ‘almost bang-bang’ form, in which the optimal policy is not randomized, and is bang-bang except perhaps at one state. Under the same assumptions, one can alternatively find an optimal constrained policy that is strictly bang-bang, but may be randomized at one state. Application is made to flow control of a birth-and-death process (e.g., an M/M/s queue); under certain monotonicity restrictions on the reward and cost structure the preceding results apply, and in addition there is a simple acceptance region.

Optimal joint replenishment, delivery and inventory management policies for perishable products

The time needed to complete a software development project is hard to estimate in practice. In addition to the uncertainty about the duration of the individual development steps, software projects often suffer from unexpected rework and delays. As a result, project scheduling remains a difficult task for the managers of software projects. In this paper, we compute optimal scheduling strategies for a set of sample software projects. The computations are based on a stochastic Markov decision model for software projects which focuses on capturing the feedback between concurrent development activities. The optimal strategies are computed using a variant of the value iteration algorithm from stochastic dynamic programming. Since the underlying process model is stochastic, the strategies are stochastically optimal, that is, they minimize the expected project duration. The ultimate goal of this research is to develop guidelines for managers how to schedule their software projects under uncertainty in the best possible way. The sample projects are similar, but differ in certain characteristics of the project or product, such as the strength of the coupling between the components or the degree of specialization of the teams on the tasks. By using a set of related projects, we can study how the project characteristics influence the optimal scheduling decisions in a project. In addition to computing the optimal scheduling policies for the sample projects, we use extensive simulations to compare the performance of the optimal policy against the so-called list policies for each given setting. List policies are a simple, but commonly used class of scheduling policies. For our sample projects, we find that the best list policy in general is not optimal. The performance gap is the larger the higher the degree of specialization of the teams is. On the other hand, the stronger the coupling between the components, the smaller is the improvement which the optimal policy achieves over the best list policy.

This paper considers the standard deterministic inventory system for two products with a capacity constraint and describes how to find the optimal policy amongst all policies which have fixed order quantities. This involves the idea of staggered initial orders and periodic policies, and includes the classical Lagrange multiplier technique and the equal order intervals method as special cases. It is shown that the usual Lagrange multiplier technique will never produce the optimal policy (in the class described above) except in the trivial case, when the capacity constraint is satisfied by the optimal unconstrained policy.

Reaching Goals by a Deadline: Digital Options and Continuous-Time Active Portfolio Management