SearcharxivSearch

arXiv · 1402.5391

Exact Simulation for Assemble-To-Order Systems

Abstract

We develop exact simulation (also known as perfect sampling) algorithms for a family of assemble-to-order systems. Due to the finite capacity, and coupling in demands and replenishments, known solving techniques are inefficient for larger problem instances. We first consider the case with individual replenishments of items, and derive an event based representation of the Markov chain that allows applying existing exact simulation techniques, using the monotonicity properties or bounding chains. In the case of joint replenishments, the state space becomes intractable for the existing methods. We propose new exact simulation algorithms, based on aggregation and bounding chains, that allow a significant reduction of the state space of the Markov chain. We also discuss the coupling times of considered models and provide sufficient conditions for linear (in the single server replenishment case) or quadratic (many server case) complexity of our algorithms in terms of the total capacity in the system.

Explore related subjects

Keep this discovery

BibTeXRIS

Ana Bušić, Emilie Coupechoux. 2014-02-21. Exact Simulation for Assemble-To-Order Systems. https://arxiv.org/abs/1402.5391

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection

In this paper, we study averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection. First, we derive a general averaging principle applicable to such equations under minimal assumptions. Subsequently, since the coefficients of the obtained averaged equation still depend on the small scaling parameter $\e$, we impose either periodic or asymptotic conditions on the coefficients, thereby obtain two distinct averaged equations whose coefficients are independent of $\e$ and establish two averaging principles. Stopping times and Khasminskii's time discretization schemes play an important role. Finally, a concrete example is provided to illustrate the applicability and validity of the theoretical results.

math.PR

Spectral properties of Random Matrices

We give the theoretical foundations of random matrix theory through the definitions of a random matrix, a random probability measure and the corresponding empirical spectral distribution. The technical tool we use is the Stieltjes transform method through which we prove optimal convergence of the empirical spectral distribution of random sample covariance matrices to the deterministic Marchenko-Pastur distribution. We also give new results about the rigidity of the eigenvalues of this random sample covariance matrix and the rate of their convergence. We then define the Dyson equation method to prove new local laws about a random matrix model that interpolates between the Marchenko-Pastur distribution, the elliptical law and the circular law. Through our work these local laws can be considered universal.

math.PR

Moments approach for the elephant random walk

We discuss the method of moments for the one-dimensional elephant random walk (ERW). We first derive a differential recurrence relation for the characteristic function of the ERW, which yields a corresponding system of recurrence relations for its moments. We then obtain asymptotic approximations for the moments in each of the three parameter regimes of the ERW. Finally, by establishing the convergence of the moments and verifying the corresponding moment-determinacy conditions, we identify the limiting distributions of the ERW in each regime.

math.PR