scholarly journals A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Le Zhang ◽  
Jinnan Wu
Keyword(s):  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Flowshop Scheduling ◽  
Stochastic Variable ◽  
Permutation Flowshop ◽  
Hybrid Metaheuristic ◽  
Benchmark Instances ◽  
Flowshop Scheduling Problem

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.

scholarly journals Synergy of Genetic Algorithm with Extensive Neighborhood Search for the Permutation Flowshop Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Rong-Chang Chen ◽  
Jeanne Chen ◽  
Tung-Shou Chen ◽  
Chien-Che Huang ◽  
Li-Chiu Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Hybrid Genetic Algorithms ◽  
Combinatorial Optimization Problems ◽  
Flowshop Scheduling Problem

The permutation flowshop scheduling problem (PFSP) is an important issue in the manufacturing industry. The objective of this study is to minimize the total completion time of scheduling for minimum makespan. Although the hybrid genetic algorithms are popular for resolving PFSP, their local search methods were compromised by the local optimum which has poorer solutions. This study proposed a new hybrid genetic algorithm for PFSP which makes use of the extensive neighborhood search method. For evaluating the performance, results of this study were compared against other state-of-the-art hybrid genetic algorithms. The comparisons showed that the proposed algorithm outperformed the other algorithms. A significant 50% test instances achieved the known optimal solutions. The proposed algorithm is simple and easy to implement. It can be extended easily to apply to similar combinatorial optimization problems.

A Hybrid Differential Evolution Algorithm Based on Dynamic Variable Neighborhood Search for Permutation Flowshop Scheduling Problem

2016 ◽  
Vol 835 ◽  
pp. 847-857 ◽  
Author(s):  
Wen Bo Liu
Keyword(s):  
Differential Evolution ◽  
Variable Neighborhood Search ◽  
Differential Evolution Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Dynamic Variable ◽  
Permutation Flowshop ◽  
Evolution Algorithm ◽  
Flowshop Scheduling Problem

Permutation flowshop scheduling problem (PFSP) is a classical NP-hard combinatorial optimization problem, which provides a challenge for evolutionary algorithms.Since it has been shown that simple evolutionary algorithms cannot solve the PFSP efficiently, local search methods are often adopted to improve the exploitation ability of evolutionary algorithms. In this paper, a hybrid differential evolution algorithm is developed to solve this problem. This hybrid algorithm is designed by incorporating a dynamic variable neighborhood search (DVNS) into the differential evolution. In the DVNS, the neighborhood is based on multiple moves and its size can be dynamically changed from small to large so as to obtain a balance between exploitation and exploration. In addition, a population monitoring and adjusting mechanism is also incorporated to enhance the search diversity and avoid being trapped in local optimum.Experimental results on benchmark problems illustrated the efficiency of the proposed algorithm.

An Effective Evolutionary Hybrid for Solving the Permutation Flowshop Scheduling Problem

2017 ◽  
Vol 25 (1) ◽  
pp. 87-111 ◽  
Author(s):  
Mehrdad Amirghasemi ◽  
Reza Zamani
Keyword(s):  
Memetic Algorithm ◽  
Critical Path ◽  
Sampling Technique ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Processing Times ◽  
Local Searches ◽  
Benchmark Instances ◽  
Flowshop Scheduling Problem

This paper presents an effective evolutionary hybrid for solving the permutation flowshop scheduling problem. Based on a memetic algorithm, the procedure uses a construction component that generates initial solutions through the use of a novel reblocking mechanism operating according to a biased random sampling technique. This component is aimed at forcing the operations having smaller processing times to appear on the critical path. The goal of the construction component is to fill an initial pool with high-quality solutions for a memetic algorithm that looks for even higher-quality solutions. In the memetic algorithm, whenever a crossover operator and possibly a mutation are performed, the offspring genome is fine-tuned by a combination of 2-exchange swap and insertion local searches. The same with the employed construction method; in these local searches, the critical path notion has been used to exploit the structure of the problem. The results of computational experiments on the benchmark instances indicate that these components have strong synergy, and their integration has created a robust and effective procedure that outperforms several state-of-the-art procedures on a number of the benchmark instances. By deactivating different components enhancing the evolutionary module of the procedure, the effects of these components have also been examined.

scholarly journals A Hybrid Algorithm for the Permutation Flowshop Scheduling Problem without Intermediate Buffers

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Xiaobo Liu ◽  
Kun Li ◽  
Huizhi Ren
Keyword(s):  
Hybrid Algorithm ◽  
Scatter Search ◽  
Neighborhood Search ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Reference Set ◽  
Dynamic Strategy ◽  
Flowshop Scheduling Problem

This paper deals with the permutation flowshop scheduling problem without intermediate buffers and presents a hybrid algorithm based on the scatter search and the variable neighborhood search. In the hybrid algorithm, the solutions with good quality and diversity are maintained by a reference set of scatter search, and the search at each generation starts from a solution generated from the reference set so as to improve the search diversity while guaranteeing the quality of the initial solution. In addition, a variable neighbourhood based on the notion of job-block is developed, and the neighbourhood size can adaptively change according to the construction of the job-block. Such a dynamic strategy can help to obtain a balance between search depth and diversity. Extensive experiments on benchmark problems are carried out and the results show that the proposed hybrid algorithm is powerful and competitive with the other powerful algorithms in the literature.

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