Simulated Annealing Genetic Algorithm and its Application in Mixed-Model Assembly Line Design

2010 ◽  
Vol 136 ◽  
pp. 64-68 ◽  
Author(s):  
Yan Jiang ◽  
Xiang Feng Li ◽  
Dun Wen Zuo ◽  
Guang Ming Jiao ◽  
Shan Liang Xue
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Local Search ◽  
Assembly Line ◽  
Mixed Model ◽  
Simulated Annealing Genetic Algorithm ◽  
Simple Genetic Algorithm ◽  
Mixed Model Assembly Line ◽  
Line Design ◽  
Mixed Model Assembly

Simple genetic algorithm has shortcomings of poor local search ability and premature convergence. To overcome these disadvantages, simulated annealing algorithm which has good local search ability was combined with genetic algorithm to form simulated annealing genetic algorithm. The tests by two commonly used test functions of Shaffer’s F6 and Rosenbrock show that simulated annealing genetic algorithm outperforms the simple genetic algorithm both in convergence rate and convergence quality. Finally, the simulated annealing genetic algorithm was firstly applied in a practical problem of balancing and sequencing design of mixed-model assembly line, once again, the solution results show that simulated annealing genetic algorithm outperforms the simple genetic algorithm. Meanwhile, it provides a new algorithm for solving the design problem of mixed-model assembly line.

scholarly journals Real-Time Scheduling of Mixed Model Assembly Line with Large Variety and Low Volume Based on Event-Triggered Simulated Annealing (ETSA)

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chunzhi Cai ◽  
Shulin Kan
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Mixed Model ◽  
Scheduling Problem ◽  
Real Time Scheduling ◽  
Scheduling Scheme ◽  
Time Scheduling ◽  
Low Volume ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

In the contemporary industrial production, multiple resource constraints and uncertainty factors exist widely in the actual job shop. It is particularly important to make a reasonable scheduling scheme in workshop manufacturing. Traditional scheduling research focused on the one-time global optimization of production scheduling before the actual production. The dynamic scheduling problem of the workshop is getting more and more attention. This paper proposed a simulated annealing algorithm to solve the real-time scheduling problem of large variety and low-volume mixed model assembly line. This algorithm obtains three groups of optimal solutions and the optimal scheduling scheme of multiple products, with the shortest product completion time and the lowest cost. Finally, the feasibility and efficiency of the model are proved by the Matlab simulation.

Balancing a mixed-model assembly line with unskilled temporary workers: algorithm and case study

2018 ◽  
Vol 38 (4) ◽  
pp. 511-523 ◽  
Author(s):  
Dongwook Kim ◽  
Dug Hee Moon ◽  
Ilkyeong Moon
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Temporary Workers ◽  
Labor Costs ◽  
Content Type ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

PurposeThe purpose of this paper is to present the process of balancing a mixed-model assembly line by incorporating unskilled temporary workers who enhance productivity. The authors develop three models to minimize the sum of the workstation costs and the labor costs of skilled and unskilled temporary workers, cycle time and potential work overloads.Design/methodology/approachThis paper deals with the problem of designing an integrated mixed-model assembly line with the assignment of skilled and unskilled temporary workers. Three mathematical models are developed using integer linear programming and mixed integer linear programming. In addition, a hybrid genetic algorithm that minimizes total operation costs is developed.FindingsComputational experiments demonstrate the superiority of the hybrid genetic algorithm over the mathematical model and reveal managerial insights. The experiments show the trade-off between the labor costs of unskilled temporary workers and the operation costs of workstations.Originality/valueThe developed models are based on practical features of a real-world problem, including simultaneous assignments of workers and precedence restrictions for tasks. Special genetic operators and heuristic algorithms are used to ensure the feasibility of solutions and make the hybrid genetic algorithm efficient. Through a case study, the authors demonstrated the validity of employing unskilled temporary workers in an assembly line.

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