We address the inventory planning problem in process networks under uncertainty through stochastic programming models. The scope of inventory planning requires the formulation of multiperiod models to represent the time-varying conditions of industrial process, but the multistage stochastic programming formulations are often too large to solve. We propose a policy-based approximation of the mul...

In most stochastic inventory models, such as continuous review models and periodic review models, it has been assumed that the stockout period during a cycle is small enough to be neglected so that the average number of cycles per year can be approximated as D/Q, where D is the average annual demand and Q is the order quantity. This assumption makes the problem more tactable, but it should not ...

In most stochastic inventory models, such as continuous review models and periodic review models, it has been assumed that the stockout period during a cycle is small enough to be neglected so that the average number of cycles per year can be approximated as D/Q, where D is the average annual demand and Q is the order quantity. This assumption makes the problem more tactable, but it should not ...

In this paper, we consider a variety of models for dealing with demand uncertainty for a joint dynamic pricing and inventory control problem in a make-to-stock manufacturing system. We consider a multi-product capacitated, dynamic setting, where demand depends linearly on the price. Our goal is to address demand uncertainty using various robust and stochastic optimization approaches. For each o...

A limit theorem for the total current in the asymmetric simple exclusion process (ASEP) with step initial condition is proved. This extends the result of Johansson on TASEP to ASEP.