Solving Flow Shop Scheduling Problem Considering Waiting Time Using Genetic Algorithm

2010 ◽  
Vol 139-141 ◽  
pp. 1684-1687 ◽  
Author(s):  
Hong Bing Wang ◽  
Ai Jun Xu ◽  
Dong Feng He ◽  
Lin Yao
Keyword(s):  
Genetic Algorithm ◽  
Continuous Casting ◽  
Waiting Time ◽  
Flow Shop ◽  
Fitness Function ◽  
Flow Shop Scheduling ◽  
Initial Population ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Steel Making

The real production scheduling problem between steel-making and continuous-casting can be modeled as Flow Shop Scheduling Problem. Waiting time must be considered for the reason of the temperature decrease of molten steel. The genetic algorithm with an initial population generation mechanism and a piecewise fitness function is proposed for solving the problem. The test results show that the proposed algorithm can find better solutions. A real case of steel-making and continuous-casting scheduling problem is computed and it shows the algorithm is effective.

scholarly journals An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1661
Author(s):  
Dayong Han ◽  
Qiuhua Tang ◽  
Zikai Zhang ◽  
Zixiang Li
Keyword(s):  
Continuous Casting ◽  
Optimization Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling ◽  
Migrating Birds Optimization ◽  
Migrating Birds

Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study.

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