simulation-based optimization

A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department

  • Alberto De Santis
  • Tommaso Giovannelli
  • Stefano Lucidi
  • Mauro Messedaglia
  • Massimo Roma
    • Categories Applications - OR and Management Sciences, Integer Programming, Optimization of Simulated Systems Tags , , , ,

    Accurate modeling of the patient flow within an Emergency Department (ED) is required by all studies dealing with the increasing and well-known problem of overcrowding. Since Discrete Event Simulation (DES) models are often adopted with the aim of assessing solutions for reducing the impact of this worldwide phenomenon, an accurate estimation of the service time … Read more

    A Method for Convex Black-Box Integer Global Optimization

  • Jeffrey Larson
  • Sven Leyffer
  • Prashant Palkar
  • Stefan M. Wild
    • Categories Convex Optimization Tags , , ,

    We study the problem of minimizing a convex function on the integer lattice when the function cannot be evaluated at noninteger points. We propose a new underestimator that does not require access to (sub)gradients of the objective but, rather, uses secant linear functions that interpolate the objective function at previously evaluated points. These linear mappings … Read more

    Reinforcement Learning via Parametric Cost Function Approximation for Multistage Stochastic Programming

  • Saeed Ghadimi
  • Raymond Perkins
  • Warren B. Powell
    • Categories Applications - OR and Management Sciences, Optimization of Simulated Systems, Stochastic Programming Tags , , , ,

    The most common approaches for solving stochastic resource allocation problems in the research literature is to either use value functions (“dynamic programming”) or scenario trees (“stochastic programming”) to approximate the impact of a decision now on the future. By contrast, common industry practice is to use a deterministic approximation of the future which is easier … Read more

    Bi-objective Simulation Optimization on Integer Lattices using the Epsilon-Constraint Method in a Retrospective Approximation Framework

  • Kyle Cooper
  • Susan R. Hunter
  • Kalyani Nagaraj
    • Categories Integer Programming, Multi-Criteria Optimization, Optimization of Simulated Systems Tags , ,

    We consider multi-objective simulation optimization (MOSO) problems on integer lattices, that is, nonlinear optimization problems in which multiple simultaneous objective functions can only be observed with stochastic error, e.g., as output from a Monte Carlo simulation model. The solution to a MOSO problem is the efficient set, which is the set of all feasible decision … Read more

    Towards Simulation Based Mixed-Integer Optimization with Differential Equations

  • Martin Gugat
  • Günter Leugering
  • Martin Schmidt
  • Mathias Sirvent
  • David Wintergerst
  • Alexander Martin
    • Categories (Mixed) Integer Nonlinear Programming, Applications - Science and Engineering, Systems governed by Differential Equations Optimization Tags , , , ,

    We propose a decomposition based method for solving mixed-integer nonlinear optimization problems with “black-box” nonlinearities, where the latter, e.g., may arise due to differential equations or expensive simulation runs. The method alternatingly solves a mixed-integer linear master problem and a separation problem for iteratively refining the mixed-integer linear relaxation of the nonlinearity. We prove that … Read more

    SCORE Allocations for Bi-objective Ranking and Selection

  • Guy Feldman
  • Susan R. Hunter
    • Categories Global Optimization, Multi-Criteria Optimization, Stochastic Programming Tags , ,

    The bi-objective R&S problem is a special case of the multi-objective simulation optimization problem in which two conflicting objectives are known only through dependent Monte Carlo estimators, the decision space or number of systems is finite, and each system can be sampled to some extent. The solution to the bi-objective R&S problem is a set … Read more

    On Sampling Rates in Simulation-Based Recursions

  • Soumyadip Ghosh
  • Peter Glynn
  • Fatemeh Hashemi
  • Raghu Pasupathy
    • Categories Nonlinear Optimization, Optimization of Simulated Systems, Stochastic Programming Tags , , , ,

    We consider the context of “simulation-based recursions,” that is, recursions that involve quantities needing to be estimated using a stochastic simulation. Examples include stochastic adaptations of fixed-point and gradient descent recursions obtained by replacing function and derivative values appearing within the recursion by their Monte Carlo counterparts. The primary motivating settings are Simulation Optimization and … Read more

    A Taxonomy of Constraints in Black-Box Simulation-Based Optimization

  • Sébastien Le Digabel
  • Stefan M. Wild
    • Categories Nonlinear Optimization, Optimization Software and Modeling Systems Tags , ,

    The types of constraints encountered in black-box simulation-based optimization problems differ significantly from those addressed in nonlinear programming. We introduce a characterization of constraints to address this situation. We provide formal definitions for several constraint classes and present illustrative examples in the context of the resulting taxonomy. This taxonomy, denoted KARQ, is useful for modeling … Read more

    Simulation Optimization for the Stochastic Economic Lot Scheduling Problem with Sequence-Dependent Setup Times

  • Nils Löhndorf
  • Stefan Minner
  • Manuel Riel
    • Categories Optimization of Simulated Systems, Scheduling, Stochastic Approaches Tags , , , , ,

    We consider the stochastic economic lot scheduling problem (SELSP) with lost sales and random demand, where switching between products is subject to sequence-dependent setup times. We propose a solution based on simulation optimization using an iterative two-step procedure which combines global policy search with local search heuristics for the traveling salesman sequencing subproblem. To optimize … Read more

    Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming

  • Saeed Ghadimi
  • Guanghui Lan
    • Categories Convex and Nonsmooth Optimization, Stochastic Programming, Unconstrained Optimization Tags , , , ,

    In this paper, we introduce a new stochastic approximation (SA) type algorithm, namely the randomized stochastic gradient (RSG) method, for solving an important class of nonlinear (possibly nonconvex) stochastic programming (SP) problems. We establish the complexity of this method for computing an approximate stationary point of a nonlinear programming problem. We also show that this … Read more