Penalty Function with Memory for Discrete Optimization via Simulation with Stochastic Constraints

DOI10.1287/opre.2015.1417zbMath1338.90285OpenAlexW2174751983MaRDI QIDQ2795877

Chuljin Park, Seong-Hee Kim

Publication date: 22 March 2016

Published in: Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/opre.2015.1417

zbMATH Keywords

discrete optimization via simulationstochastic constraintspenalty function with memory

Mathematics Subject Classification ID

Integer programming (90C10) Stochastic programming (90C15)

Related Items (6)

Rapid screening algorithms for stochastically constrained problems ⋮ Metamodel-based simulation optimization considering a single stochastic constraint ⋮ Self-adjusting the tolerance level in a fully sequential feasibility check procedure ⋮ Efficient optimization algorithms for surgical scheduling under uncertainty ⋮ Pareto set estimation with guaranteed probability of correct selection ⋮ An asymptotically optimal strategy for constrained multi-armed bandit problems

Uses Software

• R-SPLINE

Cites Work

• A model reference adaptive search method for stochastic global optimization
• Feature Article: Optimization for simulation: Theory vs. Practice
• Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization
• A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization
• Fully sequential procedures for comparing constrained systems via simulation
• A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation
• A combined procedure for optimization via simulation
• Accelerating the convergence of random search methods for discrete stochastic optimization
• Finding feasible systems in the presence of constraints on multiple performance measures
• Simulation-Based Discrete Optimization of Stochastic Discrete Event Systems Subject to Non Closed-Form Constraints
• Approximate Simulation Budget Allocation for Selecting the Best Design in the Presence of Stochastic Constraints
• A modification of the stochastic ruler method for discrete stochastic optimization

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