Modified Cuckoo Search Algorithm for Solving Permutation Flow Shop Problem

2016 ◽  
pp. 714-721
Author(s):  
Hong-Qing Zheng ◽  
Yong-Quan Zhou ◽  
Cong Xie
Keyword(s):  
Flow Shop ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Permutation Flow Shop ◽  
Flow Shop Problem

scholarly journals Adaptation and parameters studies of CS algorithm for flow shop scheduling problem

2021 ◽  
Vol 11 (3) ◽  
pp. 2266
Author(s):  
Driss Belbachir ◽  
Fatima Boumediene ◽  
Ahmed Hassam ◽  
Ltéfa Ghomri
Keyword(s):  
Completion Time ◽  
Flow Shop ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Flow Shop Scheduling ◽  
Total Completion Time ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Permutation Flow Shop

Scheduling concerns the allocation of limited resources overtime to perform tasks to fulfill certain criterion and optimize one or several objective functions. One of the most popular models in scheduling theory is that of the flow-shop scheduling. During the last 40 years, the permutation flow-shop sequencing problem with the objective of makespan minimization has held the attraction of many researchers. This problem characterized as Fm/prmu/Cmax in the notation of Graham, involves the determination of the order of processing of n jobs on m machines. In addition, there was evidence that m-machine permutation flow-shop scheduling problem (PFSP) is strongly NP-hard for m ≥3. Due to this NP-hardness, many heuristic approaches have been proposed, this work falls within the framework of the scientific research, whose purpose is to study Cuckoo search algorithm. Also, the objective of this study is to adapt the cuckoo algorithm to a generalized permutation flow-shop problem for minimizing the total completion time, so the problem is denoted as follow: Fm | | Cmax. Simulation results are judged by the total completion time and algorithm run time for each instance processed.

scholarly journals Improved cuckoo search algorithm and its application to permutation flow shop scheduling problem

2020 ◽  
Vol 14 ◽  
pp. 174830262096240
Author(s):  
Lieping Zhang ◽  
Yanlin Yu ◽  
Yingxiong Luo ◽  
Shenglan Zhang
Keyword(s):  
Flow Shop ◽  
Search Algorithm ◽  
Dynamic Balance ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Flow Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Balance Factor ◽  
Adaptive Step

Aiming at the problem that the standard cuckoo search algorithm relies on Levy flights, which leads to the step-size randomness of the search process, a self-adaptive step cuckoo search algorithm based on dynamic balance factor is proposed in our paper. First, two parameters are introduced in our paper, which were iteration number ratio parameter and adaptability ratio parameter. Then, a dynamic balance factor parameter is introduced to adjust the weight number of iteration number ratio parameter and adaptability ratio parameter. Finally, parameter skewness value calculation method and self-adaptive step strategy were proposed combined with the dynamic balance factor. Six typical test functions are used to test the performance of the proposed algorithm, the standard cuckoo search algorithm and the self-adaptive step cuckoo search algorithm which relies only on the iterative times. The test results show that the proposed algorithm had good convergence speed and accuracy. Meanwhile, taking the permutation flow shop scheduling problem as an example, eight operators of Car benchmark class are used as the test data to compare the performance of three algorithms, the effectiveness and superiority of the algorithm in solving the permutation flow shop scheduling problem are verified.

An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm

2021 ◽  
Vol 13 (6) ◽  
pp. 168781402110236
Author(s):  
Wenbin Gu ◽  
Zhuo Li ◽  
Min Dai ◽  
Minghai Yuan
Keyword(s):  
Flow Shop ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

The flow shop scheduling problem has been widely studied in recent years, but the research on multi-objective flow shop scheduling with green indicators is still relatively limited. It is urgent to strengthen the research on effective methods to solve such interesting problems. To consider the economic and environmental factors simultaneously, the paper investigates the multi-objective permutation flow shop scheduling problems (MOPFSP) which minimizes the makespan and total carbon emissions. Since MOPFSP is proved to be a NP-hard problem for more than two machines. A hybrid cuckoo search algorithm (HCSA) is proposed to solve the problems. Firstly, a largest-order-value method is proposed to enhance the performance of HCS algorithm in the solution space of MOPFSP. Then, an adaptive factor of step size is designed to control the search scopes in the evolution phases. Finally, a multi-neighborhood local search rule is addressed in order to find the optimal sub-regions obtained by the HCSA. Numerical experiments show that HCSA can solve MOPFSP efficiently.

Using Cuckoo Search Algorithm for Hybrid Flow Shop Scheduling Problems Under Makespan Criterion

2018 ◽  
pp. 379-400
Author(s):  
M. K. Marichelvam ◽  
Ömür Tosun
Keyword(s):  
Parallel Machines ◽  
Flow Shop ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Hybrid Flow Shop Scheduling

In this chapter, cuckoo search algorithm (CSA) is used to solve the multistage hybrid flow shop (HFS) scheduling problems with parallel machines. The objective is the minimization of makespan. The HFS scheduling problems are proved to be strongly non-deterministic polynomial time-hard (NP-hard). Proposed CSA algorithm has been tested on benchmark problems addressed in the literature against other well-known algorithms. The results are presented in terms of percentage deviation (PD) of the solution from the lower bound. The results indicate that the proposed CSA algorithm is quite effective in reducing makespan because average PD is observed as 1.531, whereas the next best algorithm has result of average PD of 2.295, which is, in general, nearly 50% worse, and other algorithms start from 2.645.

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