Branch-and-bound algorithms using fuzzy heuristics for solving large-scale flow-shop scheduling problems

2003 ◽  
pp. 21-35
Author(s):  
Jinliang Cheng ◽  
Hiroshi Kise ◽  
George Steiner ◽  
Paul Stephenson
Keyword(s):  
Branch And Bound ◽  
Large Scale ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Branch And Bound Algorithms ◽  
Large Scale Flow ◽  
Fuzzy Heuristics

FLOW SHOP SCHEDULING WITH REINFORCEMENT LEARNING

2013 ◽  
Vol 30 (05) ◽  
pp. 1350014 ◽  
Author(s):  
ZHICONG ZHANG ◽  
WEIPING WANG ◽  
SHOUYAN ZHONG ◽  
KAISHUN HU
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Process ◽  
Decision Process ◽  
Large Scale ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Reward Function ◽  
Markov Decision

Reinforcement learning (RL) is a state or action value based machine learning method which solves large-scale multi-stage decision problems such as Markov Decision Process (MDP) and Semi-Markov Decision Process (SMDP) problems. We minimize the makespan of flow shop scheduling problems with an RL algorithm. We convert flow shop scheduling problems into SMDPs by constructing elaborate state features, actions and the reward function. Minimizing the accumulated reward is equivalent to minimizing the schedule objective function. We apply on-line TD(λ) algorithm with linear gradient-descent function approximation to solve the SMDPs. To examine the performance of the proposed RL algorithm, computational experiments are conducted on benchmarking problems in comparison with other scheduling methods. The experimental results support the efficiency of the proposed algorithm and illustrate that the RL approach is a promising computational approach for flow shop scheduling problems worthy of further investigation.

scholarly journals Algoritma LPT-Branch and Bound Pada Penjadwalan Flexible Flowshop untuk Meminimasi Makespan

2019 ◽  
Vol 2 (1) ◽  
pp. 20
Author(s):  
Dana Marsetiya Utama
Keyword(s):  
Numerical Experiment ◽  
Branch And Bound ◽  
Processing Time ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Shop Scheduling ◽  
Branch And Bound Algorithms ◽  
Flexible Flowshop

This article discussed the problem of flow shop scheduling to minimize the makespan. The purpose of this article is to develop the LPT and Branch And Bound (LPT-Branch And Bound) algorithms to minimize the makespan. The proposed method is Longest Processing Time (LPT) and Branch And Bound. Stage settlement is divided into 3 parts. To proved the proposed algorithm, a numerical experiment was conducted by comparing the LPT-LN algorithm. The result of the numerical experiment shows that LPT-Branch And Bound's proposed algorithm is more efficient than the LPT-LN algorithm.

A jigsaw puzzle inspired algorithm for solving large-scale no-wait flow shop scheduling problems

2019 ◽  
Vol 50 (1) ◽  
pp. 87-100
Author(s):  
Fuqing Zhao ◽  
Xuan He ◽  
Yi Zhang ◽  
Wenchang Lei ◽  
Weimin Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Jigsaw Puzzle ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
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