Monte Carlo (importance) sampling within a benders decomposition algorithm for stochastic linear programs

Stochastic Linear Programs

Monte Carlo (Importance) Sampling within a Benders Decomposition Algorithm for Stochastic Linear Programs Extended Version: Including Results of Large-Scale Problems

10.21236/ada236419 ◽

1991 ◽

Cited By ~ 6

Author(s):

Gerd Infanger

Keyword(s):

Monte Carlo ◽

Importance Sampling ◽

Large Scale ◽

Benders Decomposition ◽

Linear Programs ◽

Extended Version ◽

Large Scale Problems ◽

Monte Carlo Importance Sampling ◽

Stochastic Linear Programs

Large-Scale Stochastic Linear Programs: Importance Sampling and Benders Decomposition

10.21236/ada234962 ◽

1991 ◽

Cited By ~ 27

Author(s):

George B. Dantzig ◽

Gerd Infanger

Keyword(s):

Importance Sampling ◽

Large Scale ◽

Benders Decomposition ◽

Linear Programs ◽

Stochastic Linear Programs

An Accelerated Benders Decomposition Algorithm for Solving a Double-Type Double-Standard Maximal Covering Location Problem

SN Operations Research Forum ◽

10.1007/s43069-020-00050-z ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Ashkan Fakhri ◽

Antonios Fragkogios ◽

Georgios K. D. Saharidis

Keyword(s):

Benders Decomposition ◽

Location Problem ◽

Double Standard ◽

Decomposition Algorithm ◽

Maximal Covering ◽

Large-scale reverse supply chain network design: An accelerated Benders decomposition algorithm

Computers & Industrial Engineering ◽

10.1016/j.cie.2018.05.057 ◽

2018 ◽

Vol 124 ◽

pp. 545-559 ◽

Cited By ~ 9

Author(s):

Ahmed Alshamsi ◽

Ali Diabat

Keyword(s):

Supply Chain ◽

Network Design ◽

Large Scale ◽

Benders Decomposition ◽

Decomposition Algorithm ◽

Supply Chain Network Design ◽

Supply Chain Network ◽

Reverse Supply Chain ◽

An effective benders decomposition algorithm for solving the distributed permutation flowshop scheduling problem

Computers & Operations Research ◽

10.1016/j.cor.2020.105006 ◽

2020 ◽

Vol 123 ◽

pp. 105006 ◽

Cited By ~ 1

Author(s):

Alper Hamzadayı

Keyword(s):

Benders Decomposition ◽

Decomposition Algorithm ◽

Scheduling Problem ◽

Flowshop Scheduling ◽

Permutation Flowshop ◽

Flowshop Scheduling Problem

Logic-based benders decomposition algorithm for contamination detection problem in water networks

Computers & Operations Research ◽

10.1016/j.cor.2019.104840 ◽

2020 ◽

Vol 115 ◽

pp. 104840

Author(s):

F. Hooshmand ◽

F. Amerehi ◽

S.A. MirHassani

Keyword(s):

Benders Decomposition ◽

Decomposition Algorithm ◽

Water Networks ◽

Detection Problem ◽

Developing benders decomposition algorithm for a green supply chain network of mine industry: Case of Iranian mine industry

Operations Research Perspectives ◽

10.1016/j.orp.2018.11.002 ◽

2018 ◽

Vol 5 ◽

pp. 371-382

Author(s):

Mahdi Yousefi Nejad Attari ◽

Ali Ebadi Torkayesh

Keyword(s):

Supply Chain ◽

Benders Decomposition ◽

Decomposition Algorithm ◽

Green Supply Chain ◽

Supply Chain Network ◽

A Benders’ decomposition algorithm with combinatorial cuts for the multi-manned assembly line balancing problem

European Journal of Operational Research ◽

10.1016/j.ejor.2019.05.001 ◽

2019 ◽

Vol 278 (3) ◽

pp. 796-808 ◽

Cited By ~ 8

Author(s):

Adalberto Sato Michels ◽

Thiago Cantos Lopes ◽

Celso Gustavo Stall Sikora ◽

Leandro Magatão

Keyword(s):

Assembly Line ◽

Benders Decomposition ◽

Assembly Line Balancing ◽

Decomposition Algorithm ◽

Line Balancing ◽

Assembly Line Balancing Problem

A Modified Benders Decomposition Algorithm to Solve Second-Order Cone AC Optimal Power Flow

IEEE Transactions on Smart Grid ◽

10.1109/tsg.2017.2776407 ◽

2019 ◽

Vol 10 (2) ◽

pp. 1713-1724 ◽

Cited By ~ 5

Author(s):

Zhao Yuan ◽

Mohammad Reza Hesamzadeh

Keyword(s):

Power Flow ◽

Optimal Power Flow ◽

Benders Decomposition ◽

Second Order ◽

Decomposition Algorithm ◽

Optimal Power ◽

Second Order Cone ◽

05/00471 Monte Carlo importance sampling optimization for system reliability applications

Fuel and Energy Abstracts ◽

10.1016/s0140-6701(05)80472-2 ◽

2005 ◽

Vol 46 (1) ◽

pp. 62

Keyword(s):

Monte Carlo ◽

Importance Sampling ◽

System Reliability ◽

Monte Carlo Importance Sampling ◽

Sampling Optimization