Solve Job Shop Scheduling Problem With Sequence Dependent Setup Time Using A Simulated Annealing Algorithm With Different Neighborhood Search Search Structure

2016 ◽  
pp. 1
Author(s):  
Mohammad Mahmoud Hamed
Keyword(s):  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Setup Time ◽  
Neighborhood Search ◽  
Job Shop Scheduling Problem ◽  
Sequence Dependent Setup Time ◽  
Annealing Algorithm ◽  
Sequence Dependent Setup ◽  
Search Structure

scholarly journals An improved memetic algorithm for the flexible job shop scheduling problem with transportation times

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1518-1528
Author(s):  
Guohui Zhang ◽  
Jinghe Sun ◽  
Xixi Lu ◽  
Haijun Zhang
Keyword(s):  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Optimal Solutions ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Annealing Algorithm ◽  
Transportation Times ◽  
Improved Memetic Algorithm

In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divided into general solutions and local optimal solutions according to the elite library. The general solutions are executed by the simulated annealing algorithm to improve the quality, and the local optimal solutions are executed by the mutation operation to increase the diversity of the solution set. Comparison experiments with the improved genetic algorithm show that the improved memetic algorithm has better search performance and stability.

scholarly journals A Mathematical Model and a Simulated Annealing Algorithm for Balancing Multi-manned Assembly Line Problem with Sequence-Dependent Setup Time

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Wucheng Yang ◽  
Wenming Cheng
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Simulated Annealing Algorithm ◽  
Assembly Line Balancing ◽  
Setup Time ◽  
Mixed Integer ◽  
Sequence Dependent ◽  
Sequence Dependent Setup Time ◽  
Annealing Algorithm ◽  
Sequence Dependent Setup

Multi-manned assembly lines have been widely applied to the industrial production, especially for large-sized products such as cars, buses, and trucks, in which more than one operator in the same station simultaneously performs different tasks in parallel. This study deals with a multi-manned assembly line balancing problem by simultaneously considering the forward and backward sequence-dependent setup time (MALBPS). A mixed-integer programming is established to characterize the problem. Besides, a simulated annealing algorithm is also proposed to solve it. To validate the performance of the proposed approaches, a set of benchmark instances are tested and the lower bound of the proposed problem is also given. The results demonstrated that the proposed algorithm is quite effective to solve the problem.

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