A hybrid harmony search algorithm with efficient job sequence scheme and variable neighborhood search for the permutation flow shop scheduling problems

2017 ◽  
Vol 65 ◽  
pp. 178-199 ◽  
Author(s):  
Fuqing Zhao ◽  
Yang Liu ◽  
Yi Zhang ◽  
Weimin Ma ◽  
Chuck Zhang
2012 ◽  
Vol 29 (02) ◽  
pp. 1250012 ◽  
Author(s):  
KAI-ZHOU GAO ◽  
QUAN-KE PAN ◽  
JUN-QING LI ◽  
YU-TING WANG ◽  
JING LIANG

This paper presents a hybrid harmony search (HHS) algorithm for solving no-wait flow shop scheduling problems with total flowtime criterion. First, an initial harmony memory (HM) is formed by taking advantage of the NEH heuristic. Second, the harmony memory is divided into several small groups and each group executes its evolution process independently. At the same time, groups share information reciprocally by dynamic re-grouping mechanism. Third, to stress the balance between the global exploration and local exploration, a variable neighborhood search algorithm is developed and embedded in the HHS algorithm. In addition, a speed-up method is applied to reduce the running time requirement. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed HHS algorithm is superior to the recently published hybrid DE-based (HDE) algorithm and hybrid particle swarm optimization (HPSO) algorithm in terms of effectiveness and efficiency.


2019 ◽  
Vol 9 (7) ◽  
pp. 1353 ◽  
Author(s):  
Ko-Wei Huang ◽  
Abba Girsang ◽  
Ze-Xue Wu ◽  
Yu-Wei Chuang

The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.


2019 ◽  
Vol 85 ◽  
pp. 105861 ◽  
Author(s):  
Iyad Abu Doush ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah ◽  
Eugene Santos ◽  
Abdelaziz I. Hammouri ◽  
...  

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