A discrete particle swarm optimization for lot-streaming flowshop scheduling problem

2008 ◽  
Vol 191 (2) ◽  
pp. 360-373 ◽  
Author(s):  
Chao-Tang Tseng ◽  
Ching-Jong Liao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Discrete Particle Swarm Optimization ◽  
Lot Streaming ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Flowshop Scheduling Problem

scholarly journals The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

2019 ◽  
Vol 20 (2) ◽  
pp. 105
Author(s):  
Ikhlasul Amallynda
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms 

scholarly journals The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

2019 ◽  
Vol 20 (2) ◽  
pp. 1
Author(s):  
Ikhlasul Amallynda
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms 

Discrete Particle Swarm Optimization Algorithm for Lot-Streaming No-Wait Flow Shop Scheduling Problem

2012 ◽  
Vol 538-541 ◽  
pp. 863-868 ◽  
Author(s):  
Lin Yang ◽  
Yu Xia Pan
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Lot Streaming ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Shop Scheduling ◽  
No Wait

This paper proposed discrete particle swarm optimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals . After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.

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