This paper deals with an inventory system with one central warehouse and a number of identical retailers. We consider perishable-on-theshelf items; that is, all items have a fixed shelf life and start to age on their arrival at the retailers. Each retailer faces Poisson demand and employs (1, T) inventory policy. Although demand not met at a retailer is lost, the unsatisfied demand at the centr...

In this paper I consider how the structure of domestic tax policy can a¤ect trade policy when the former is determined through voting by the public and the latter through lobbying by …rms. I …nd that in these circumstances trade policy may be more liberal due to the pressures placed on the lobbying capabilities of …rms by the use of domestic taxes. In particular a domestic income tax reduces th...

California is a leader among states in its efforts to cut greenhouse gas emissions. Under the California Global Warming Solutions Act of 2006 (Assembly Bill 32), the state has set itself on a course to reduce its greenhouse gas emissions to 1990 levels by the year 2020. In addition to its cap-and-trade program, California aims to accomplish this objective via a large assortment of complementary...

This paper presents an algorithm to compute an optimal (s, S) policy under standard assumptions (stationary data, well-behaved one-period costs, discrete demand, full backlogging, and the average-cost criterion). The method is iterative, starting with an arbitrary, given (s, S) policy and converging to an optimal policy in a finite number of iterations. Any of the available approximations can t...

Eligibility traces have been shown to speed reinforcement learning, to make it more robust to hidden states, and to provide a link between Monte Carlo and temporal-difference methods. Here we generalize eligibility traces to off-policy learning, in which one learns about a policy different from the policy that generates the data. Off-policy methods can greatly multiply learning, as many policie...

Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are usually prohibitively expensive. A common approach is to use importance sampling techniques for compensating for the bias caused by the difference between data-sampling policies and the target policy. However, existing o...

This paper presents the analysis of a continuous review perishable inventory system wherein the life time of each item follows an exponential distribution. The operating policy is (s,S) policy where the ordered items are received after a random time which follows exponential distribution. Primary arrival follows Poisson distribution and they may turnout to be posit...

This paper provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, to construct a partition space that has a smaller number of states than the original MDP. As a result, learning policies on the partition space should be faster than on the original state space. The technique p...