Enhanced Biogeography-Based Optimization for Flow-Shop Scheduling

2018 ◽  
pp. 295-306 ◽  
Author(s):  
Yi-Chen Du ◽  
Min-Xia Zhang ◽  
Ci-Yun Cai ◽  
Yu-Jun Zheng
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Shop Scheduling

A hybrid neural network for prediction of flow shop scheduling performance

2015 ◽  
Author(s):  
J. R. Yang ◽  
W. Xu ◽  
M. Rajora ◽  
A. H. Shih ◽  
H. Z. Tan ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Neural Network ◽  
Shop Scheduling

An efficiency improvement of flexible flow shop scheduling in automotive part company

2020 ◽  
pp. 1-14
Author(s):  
Waraporn Fangrit ◽  
Hwa Jen Yap ◽  
Mukhtar Fatihu Hamza ◽  
Siow-Wee Chang ◽  
Keem Siah Yap ◽  
...  
Keyword(s):  
Tabu Search ◽  
Flow Shop ◽  
Simulation Software ◽  
Flow Shop Scheduling ◽  
Current Status ◽  
Flexible Flow Shop ◽  
Shop Scheduling ◽  
Scheduling Method ◽  
Automotive Part ◽  
Flexible Flow Shop Scheduling

Flexible flow shop is becoming more interested and applied in industries due to its impact from higher workloads. Flexible flow shop scheduling problem is focused to minimize the makespan. A metaheuristic model based on Hybrid Tabu Search is developed to overcome this problem. Firstly, Hybrid Tabu Search is modelled based on the factory data. The Earliest Due Date rule is used as the scheduling method for the current status. FlexSim simulation software is used to evaluate the Hybrid Tabu Search model. The outcome is validated with two different basic heuristic solutions; Campbell, Dudek and Smith’s and Gupta’s heuristics. It is found that the proposed model can improve the job sequence based on makespan criteria.

scholarly journals A cooperated shuffled frog-leaping algorithm for distributed energy-efficient hybrid flow shop scheduling with fuzzy processing time

2021 ◽  
Author(s):  
Jingcao Cai ◽  
Deming Lei
Keyword(s):  
Energy Efficient ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Distributed Energy ◽  
Shuffled Frog Leaping Algorithm ◽  
Shop Scheduling ◽  
Fuzzy Processing Time ◽  
Hybrid Flow Shop Scheduling ◽  
Shuffled Frog Leaping

AbstractDistributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention; however, DHFSP with uncertainty and energy-related element is seldom studied. In this paper, distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with fuzzy processing time is considered and a cooperated shuffled frog-leaping algorithm (CSFLA) is presented to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption simultaneously. Iterated greedy, variable neighborhood search and global search are designed using problem-related features; memeplex evaluation based on three quality indices is presented, an effective cooperation process between the best memeplex and the worst memeplex is developed according to evaluation results and performed by exchanging search times and search ability, and an adaptive population shuffling is adopted to improve search efficiency. Extensive experiments are conducted and the computational results validate that CSFLA has promising advantages on solving the considered DEHFSP.

scholarly journals A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling

2021 ◽  
Vol 11 (11) ◽  
pp. 4837
Author(s):  
Mohamed Abdel-Basset ◽  
Reda Mohamed ◽  
Mohamed Abouhawwash ◽  
Victor Chang ◽  
S. S. Askar
Keyword(s):  
Local Search ◽  
Search Strategy ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Discrete Problem ◽  
Mutation Operator ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Local Search Strategy ◽  
Generalized Normal Distribution

This paper studies the generalized normal distribution algorithm (GNDO) performance for tackling the permutation flow shop scheduling problem (PFSSP). Because PFSSP is a discrete problem and GNDO generates continuous values, the largest ranked value rule is used to convert those continuous values into discrete ones to make GNDO applicable for solving this discrete problem. Additionally, the discrete GNDO is effectively integrated with a local search strategy to improve the quality of the best-so-far solution in an abbreviated version of HGNDO. More than that, a new improvement using the swap mutation operator applied on the best-so-far solution to avoid being stuck into local optima by accelerating the convergence speed is effectively applied to HGNDO to propose a new version, namely a hybrid-improved GNDO (HIGNDO). Last but not least, the local search strategy is improved using the scramble mutation operator to utilize each trial as ideally as possible for reaching better outcomes. This improved local search strategy is integrated with IGNDO to produce a new strong algorithm abbreviated as IHGNDO. Those proposed algorithms are extensively compared with a number of well-established optimization algorithms using various statistical analyses to estimate the optimal makespan for 41 well-known instances in a reasonable time. The findings show the benefits and speedup of both IHGNDO and HIGNDO over all the compared algorithms, in addition to HGNDO.

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