An Effective Hybrid Optimization Algorithm for Scheduling of Virtual Manufacturing Cells

While the concept of virtual manufacturing cells (VMCs) was introduced in the 1980s, its scheduling has been considered recently. In a VMC, machines are allocated to the jobs in order to response quickly to unpredictable demands in dynamic environment, but machines are not reconfigured physically for creating new cells. In this paper, a hybrid genetic algorithm has been applied for solving the scheduling of VMCs. Proposed HGA was combined with a local search method, called Great Deluge Algorithm. Since the parameters of heuristic and metaheuristic algorithms have a great influence on the performance of the search, parameter tuning is used for handling the problems in an efficient manner. Hence, a TOPSIS-based parameters tuning is proposed, which not only considers the number of fitness function evaluation, but also aims to minimize the running time of the presented heuristics. In order to investigate the performance of the suggested approach, a computational analysis on the problem is performed. Extensive experimental results showed that the proposed HGA outperformed the basic GA in terms of average runtimes and average value of objective function

کلیدواژه ها:

Virtual manufacturing cells (VMCs) ، Scheduling ، Genetic algorithm (GA) ، Great Deluge Algorithm (GDA) ، Parameter tunning