| Abstrakt: |
A reasonable and efficient scheduling strategy is the key to improving the shop management level of the manufacturing enterprises. It can not only maximize the current resource capacity, but also greatly increase the production capacity of the enterprise. Job shop scheduling problem (JSSP) is one of the most representative scheduling problems and one of the most difficult combinatorial optimization problems. The key development direction in this field is to study the production bottleneck problem while optimizing and upgrading the production combination. This paper proposes an improved meta-heuristic algorithm to minimize maximum completion time and average flow time in the manufacturing industry. For this algorithm, a novel two-way decoding method was first adopted in the improved algorithm, and then, a parallel search process based on global search and local search was designed; finally, two different functions, namely receive functions and influence functions were designed according to the bottleneck identification and Pareto selection strategy in the production scheduling. The research results show that the proposed meta-heuristic algorithm has obvious advantages in stability, distribution, optimal value and calculation time. The research findings provide a new theoretical basis for further effectively improving the JSSP problem in the manufacturing industry while considering the bottleneck identification problems. [ABSTRACT FROM AUTHOR] |
