In this paper, a manufacturing supply chain system composed by a single-product machine, a buffer and a stochastic demand is considered. A stochastic fluid model is adopted to describe the system and to take into acco...In this paper, a manufacturing supply chain system composed by a single-product machine, a buffer and a stochastic demand is considered. A stochastic fluid model is adopted to describe the system and to take into account stochastic delivery times. The objective of this paper is to evaluate the optimal buffer level used in hedging point policy taken into account planned delivery times, machine failures and random demands. This optimal buffer allows minimizing the sum of inventory, transportation, lost sales and late delivery costs. Infinitesimal perturbation analysis method is used for optimizing the proposed system. Using the stochastic fluid model, the trajectories of buffer level are studied and the infinitesimal perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm, which determines the optimal buffer level in the presence of planned delivery time. Also in this work, we discuss the advantage of the use of the infinitesimal perturbation analysis method comparing to classical simulation methods.展开更多
The event-driven paradigm offers an alternative to the time-driven paradigm for modelling,sampling,estimation,control and optimization.This has come about largely as a consequence of systems being increasingly network...The event-driven paradigm offers an alternative to the time-driven paradigm for modelling,sampling,estimation,control and optimization.This has come about largely as a consequence of systems being increasingly networked,wireless and consisting of distributed communicating components.The key idea is that control actions need not be dictated by time steps taken by a“clock”;rather,an action should be triggered by an“event”which may be a well-defined condition on the system state,including the possibility of a simple time step,or a random state transition.We provide an overview of recent developments in event-driven approaches and focus on two areas to illustrate their value.First,in distributed systems,we describe how event-driven,rather than synchronous,communication can guarantee convergence in cooperative distributed optimization while provably maintaining optimality.Second,in hybrid systems where events naturally decompose state trajectories into different discrete states(modes),we review the theory of infinitesimal perturbation analysis(IPA)which offers an event-driven“IPA calculus”for evaluating(or estimating in the case of stochastic systems)gradients of performance metrics,thus facilitating the solution of a large class of control and optimization problems.展开更多
文摘In this paper, a manufacturing supply chain system composed by a single-product machine, a buffer and a stochastic demand is considered. A stochastic fluid model is adopted to describe the system and to take into account stochastic delivery times. The objective of this paper is to evaluate the optimal buffer level used in hedging point policy taken into account planned delivery times, machine failures and random demands. This optimal buffer allows minimizing the sum of inventory, transportation, lost sales and late delivery costs. Infinitesimal perturbation analysis method is used for optimizing the proposed system. Using the stochastic fluid model, the trajectories of buffer level are studied and the infinitesimal perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm, which determines the optimal buffer level in the presence of planned delivery time. Also in this work, we discuss the advantage of the use of the infinitesimal perturbation analysis method comparing to classical simulation methods.
基金This work was supported in part by National Science Foundation[grant number CNS-1139021]Air Force Office of Scientific Research[grant number FA9550-12-1-0113]+1 种基金Office of Naval Research[grant number N00014-09-1-1051]Army Research Office[grant number W911NF-11-1-0227].
文摘The event-driven paradigm offers an alternative to the time-driven paradigm for modelling,sampling,estimation,control and optimization.This has come about largely as a consequence of systems being increasingly networked,wireless and consisting of distributed communicating components.The key idea is that control actions need not be dictated by time steps taken by a“clock”;rather,an action should be triggered by an“event”which may be a well-defined condition on the system state,including the possibility of a simple time step,or a random state transition.We provide an overview of recent developments in event-driven approaches and focus on two areas to illustrate their value.First,in distributed systems,we describe how event-driven,rather than synchronous,communication can guarantee convergence in cooperative distributed optimization while provably maintaining optimality.Second,in hybrid systems where events naturally decompose state trajectories into different discrete states(modes),we review the theory of infinitesimal perturbation analysis(IPA)which offers an event-driven“IPA calculus”for evaluating(or estimating in the case of stochastic systems)gradients of performance metrics,thus facilitating the solution of a large class of control and optimization problems.
