A hybrid genetic tabu search algorithm for distributed flexible job shop scheduling problems

The distributed flexible job-shop scheduling problem (DFJSP) is an extension of the flexible job shop scheduling problem, which is a famous NP-complete combinatorial optimization problem. DFJSP consists of three subproblems, assigning jobs to appropriate factories, scheduling operations to suitable machines, and determining the operation sequence on machines. Due to the complexity of DFJSP, there is only a little research on the problem. There are several benchmark sets proposed to test the performance of methods for solving DFJSP. However, many instances remain unresolved after a long time. This paper proposes a hybrid genetic tabu search algorithm (HGTSA) for DFJSP. The proposed HGTSA combines the global search ability of the genetic algorithm (GA) and the local search ability of the tabu search (TS) well. Two genetic operators are designed based on the critical factory to discrete the population. A new neighborhood structure is introduced into TS to search for more space of neighborhood solutions. To evaluate the performance of HGTSA, this paper compares it with four stateof-the-art algorithms on 69 benchmark instances. The experimental results demonstrate that HGTSA outperforms these comparison algorithms in terms of solution quality and computation efficiency. In particular, HGTSA has found 13 newly upper bounds of these benchmark instances.

Automated summary

This paper proposes a hybrid genetic tabu search algorithm for the distributed flexible job-shop scheduling problem, which outperforms other comparison algorithms in terms of solution quality and computation efficiency.