Abstract
To deal with the multi-objective hybrid flow Shop Scheduling Problem (HFSP), an improved genetic algorithms based on parallel sequential moving and variable mutation rate is proposed. Compared with the traditional GA, the algorithm proposed in this paper uses the two-point mutation rule based on VMR to find the global optimum which can make the algorithm jump out of the local optimum as far as possible, once it falls into the local optimum quickly. Decoding rules based on parallel sequential movement ensures that the artifact can start processing in time, so that the buffer between stages in the flow-shop is as little as possible, and the production cycle is shortened. Finally, a program was developed with the actual data of a workshop to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above.
Highlights
The Hybrid Flow Shop Scheduling Problem (HFSP) [1,2,3,4] is one of the important branches of production scheduling and combinatorial optimization problems
Wang Shengyao[8] proposed a distribution estimation algorithm based on permutation encoding and decoding method for HFSP
The above researches show that HFSP can be solved by different kinds of intelligent algorithms, and local optimal solutions can be obtained
Summary
The Hybrid Flow Shop Scheduling Problem (HFSP) [1,2,3,4] is one of the important branches of production scheduling and combinatorial optimization problems. NENAD [5] proposed a variable neighborhood search algorithm, which extends the search range by changing the neighborhood structure set several times and obtains the local optimal solution. The above researches show that HFSP can be solved by different kinds of intelligent algorithms, and local optimal solutions can be obtained. Genetic algorithms are apt to fall into local optimum[9] and none of them can solve the problem with both the goals of minimizing the maximum completion time and the penalty for tasks delay at the same time
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