A SIMULATED ANNEALING BASED BEAM SEARCH ALGORITHM FOR THE FLOW-SHOP SCHEDULING PROBLEM

2008 ◽  
Vol 22 (01) ◽  
pp. 65-75 ◽  
Author(s):  
FENG JIN ◽  
SHI-JI SONG ◽  
CHENG WU
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Beam Search ◽  
Shop Scheduling

Beam search algorithm, as an adaptation of branch and bound method, is regarded as one of the effective approaches in solving combinational optimization problems. In this paper, a new beam search algorithm for the large-scale permutation flow shop scheduling problem (FSP) is proposed. A new branching scheme is addressed and compared with the traditional branching scheme. With the new branching scheme, the number of partial schedules in the search tree can be greatly reduced. Based on a simple simulated annealing algorithm, partial schedules are globally evaluated. Numerical experiments show that good solutions of large-scale FSPs could be found with the proposed algorithm in a short time.

scholarly journals A Hybrid Discrete Bacterial Memetic Algorithm with Simulated Annealing for Optimization of the Flow Shop Scheduling Problem

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1131
Author(s):  
Anita Agárdi ◽  
Károly Nehéz ◽  
Olivér Hornyák ◽  
László T. Kóczy
Keyword(s):  
Simulated Annealing ◽  
Production Scheduling ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Research Work ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.

scholarly journals An improved simulated annealing algorithm based on residual network for permutation flow shop scheduling

2020 ◽  
Author(s):  
Yang Li ◽  
Cuiyu Wang ◽  
Liang Gao ◽  
Yiguo Song ◽  
Xinyu Li
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Structure Design ◽  
Flow Shop Scheduling ◽  
Residual Network ◽  
Design Algorithm ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

Abstract The permutation flow shop scheduling problem (PFSP), which is one of the most important scheduling types, is widespread in the modern industries. With the increase of scheduling scale, the difficulty and computation time of solving the problem will increase exponentially. Adding the knowledge to intelligent algorithms is a good way to solve the complex and difficult scheduling problems in reasonable time. To deal with the complex PFSPs, this paper proposes an improved simulated annealing (SA) algorithm based on residual network (SARes). First, this paper defines the neighborhood of the PFSP and divides its key blocks. Second, the Residual Network (ResNet) is used to extract and train the features of key blocks. And, the trained parameters are stored in the SA algorithm to improve its performance. Afterwards, some key operators, including the initial temperature setting and temperature attenuation function of SA algorithm, are also modified. After every new solution is generated, the parameters trained by the ResNet are used for fast ergodic search until the local optimal solution found in the current neighborhood. Finally, the most famous benchmarks including part of TA benchmark are selected to verify the performance of the proposed SARes algorithm, and the comparisons with the-state-of-art methods are also conducted. The experimental results show that the proposed method has achieved good results by comparing with other algorithms. This paper also conducts experiments on network structure design, algorithm parameter selection, CPU time and other problems, and verifies the advantages of SARes algorithm from the aspects of stability and efficiency.

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