scholarly journals A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems

2010 ◽  
Vol 1 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Mohammad Kazem Sayadi ◽  
Reza Ramezanian ◽  
Nader Ghaffari-Nasab
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Makespan Minimization ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

Minimizing makespan and total flow time in permutation flow shop scheduling problems using modified gravitational emulation local search algorithm

2016 ◽  
Vol 232 (3) ◽  
pp. 534-545 ◽  
Author(s):  
R Sanjeev Kumar ◽  
KP Padmanaban ◽  
M Rajkumar
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Local Search Algorithm ◽  
Scheduling Problems ◽  
Total Flow ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

Permutation flow shop scheduling is a part of production scheduling problems. It allows “n” jobs to be processed on “m” machines. All the jobs are processed in all the machines, and the sequence of jobs being processed is the same in all the machines. It plays a vital role in both automated manufacturing industries and nondeterministic polynomial hard problem. Gravitational emulation local search algorithm is a randomization-based concept algorithm. It is used iteratively as the local search procedure for exploring the local optimum solution. Modified gravitational emulation local search algorithm is used for both exploring and exploiting the optimum solution for permutation flow shop scheduling problems. In this work, modified gravitational emulation local search algorithm is proposed to solve the permutation flow shop scheduling problems with the objectives such as minimization of makespan and total flow time. The computational results show that the performance solution of the proposed algorithm gives better results than the previous author’s approaches. Statistical tools are also used for finding out a relationship that exists between the two variables (makespan and total flow time) and to evaluate the performance of the proposed approach against the previous approaches in the literature.

scholarly journals A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems

2019 ◽  
Vol 9 (7) ◽  
pp. 1353 ◽  
Author(s):  
Ko-Wei Huang ◽  
Abba Girsang ◽  
Ze-Xue Wu ◽  
Yu-Wei Chuang
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

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.

scholarly journals Hybrid Flow Shop Scheduling Problems Using Improved Fireworks Algorithm for Permutation

2020 ◽  
Vol 10 (3) ◽  
pp. 1174 ◽  
Author(s):  
Xuelian Pang ◽  
Haoran Xue ◽  
Ming-Lang Tseng ◽  
Ming K. Lim ◽  
Kaihua Liu
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Gaussian Mutation ◽  
Fireworks Algorithm ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Hybrid Flow Shop Scheduling ◽  
Permutation Flow Shop

Prior studies are lacking which address permutation flow shop scheduling problems and hybrid flow shop scheduling problems together to help firms find the optimized scheduling strategy. The permutation flow shop scheduling problem and hybrid flow shop scheduling problems are important production scheduling types, which widely exist in industrial production fields. This study aimed to acquire the best scheduling strategy for making production plans. An improved fireworks algorithm is proposed to minimize the makespan in the proposed strategies. The proposed improved fireworks algorithm is compared with the fireworks algorithm, and the improvement strategies include the following: (1) A nonlinear radius is introduced and the minimum explosion amplitude is checked to avoid the waste of optimal fireworks; (2) The original Gaussian mutation operator is replaced by a hybrid operator that combines Cauchy and Gaussian mutation to improve the search ability; and (3) An elite group selection strategy is adopted to reduce the computing costs. Two instances from the permutation flow shop scheduling problem and hybrid flow shop scheduling problems were used to evaluate the improved fireworks algorithm’s performance, and the computational results demonstrate the improved fireworks algorithm’s superiority.

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