Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

Parallel hybrid metaheuristics for the flexible job shop problem

A parallel approach to flexible job shop scheduling problem is presented in this paper. We propose two double-level parallel metaheuristic algorithms based on the new method of the neighborhood determination. Algorithms proposed here include two major modules: the machine selection module refer to executed sequentially, and the operation scheduling module executed in parallel. On each level a m...

Solving the Flexible Job Shop Problem on Multi-GPU

We propose the new framework of the distributed tabu search metaheuristic designed to be executed using a multi-GPU cluster, i.e. cluster of nodes equipped with GPU computing units. We propose a hybrid single-walk parallelization of the tabu search, where hybridization consists in examining a number of solutions from a neighborhood concurrently by several GPUs (multi-GPU). The methodology is de...

Solving the continuous flow-shop scheduling problem by metaheuristics

Continuous flow-shop scheduling problems circumscribe an important class of sequencing problems in the field of production planning. The problem considered here is to find a permutation of jobs to be processed sequentially on a number of machines under the restriction that the processing of each job has to be continuous with respect to the objective of minimizing the total processing time (flow...

A Metaheuristic Hybridization Within a Holonic Multiagent Model for the Flexible Job Shop Problem