Optimal budget allocation policy for tabu search in stochastic simulation optimization

DOI10.1016/j.cor.2022.106046OpenAlexW4306740563MaRDI QIDQ2108143

Andrea Matta, Chunlong Yu, Nadia Lahrichi

Publication date: 19 December 2022

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2022.106046

zbMATH Keywords

metaheuristicstabu searchranking and selectionsimulation optimizationoptimal computing budget allocation

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Uses Software

• EGO
• SimOpt

Cites Work

• Unnamed Item
• Unnamed Item
• A parallel tabu search for solving the primal buffer allocation problem in serial production systems
• Particle swarm optimization for function optimization in noisy environment
• Differentiated service inventory optimization using nested partitions and MOCBA
• Large deviations techniques and applications.
• Efficient global optimization of expensive black-box functions
• Using tabu search to schedule activities of stochastic resource-constrained projects
• Simulation budget allocation for further enhancing the efficiency of ordinal optimization
• Determining buffer location and size in production lines using tabu search
• A tabu search algorithm for the vehicle routing problem with discrete split deliveries and pickups
• Future paths for integer programming and links to artificial intelligence
• Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem
• Fully Sequential Procedures for Large-Scale Ranking-and-Selection Problems in Parallel Computing Environments
• Ranking and Selection for Steady-State Simulation: Procedures and Perspectives
• Sequential Sampling to Myopically Maximize the Expected Value of Information
• On the Convergence Rates of Expected Improvement Methods
• Selecting a Selection Procedure
• A Knowledge-Gradient Policy for Sequential Information Collection
• A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation
• Finding Optimal (s, S) Policies Is About As Simple As Evaluating a Single Policy
• On two-stage selection procedures and related probability-inequalities
• A fully sequential procedure for indifference-zone selection in simulation
• Efficient Ranking and Selection in Parallel Computing Environments
• Simulation optimization using the cross-entropy method with optimal computing budget allocation
• Tabu-search simulation optimization approach for flow-shop scheduling with multiple processors — a case study
• Ranking and Selection as Stochastic Control
• Optimal Computing Budget Allocation to Select the Nondominated Systems—A Large Deviations Perspective
• A lower bound for the correct subset-selection probability and its application to discrete-event system simulations
• Complete expected improvement converges to an optimal budget allocation
• Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data
• Efficient Dynamic Simulation Allocation in Ordinal Optimization
• A Tabu Search Heuristic for the Vehicle Routing Problem with Stochastic Demands and Customers
• A Single-Sample Multiple Decision Procedure for Ranking Means of Normal Populations with known Variances
• Handbook of metaheuristics
• Scheduling using tabu search methods with intensification and diversification

This page was built for publication: Optimal budget allocation policy for tabu search in stochastic simulation optimization