Linear programming assisted (not embedded) genetic algorithm for flexible jobshop scheduling with lot streaming

The hybridization of metaheuristics with other techniques for optimization has been one of the most interesting trends. The focus of research on metaheuristics is also becoming problem oriented rather than algorithm oriented. This has led researchers to try combining different algorithmic components in order to design more powerful algorithms. In this paper, we developed a linear programming assisted genetic algorithm for solving a flexible jobshop lot streaming problem. The genetic algorithm searches over both discrete and continuous variables in the problem solution space. A linear programming is used to assist the genetic algorithm by further refining promising solutions in a population periodically through determining the optimal values of the continuous variables corresponding to those promising solutions. This is different from one common way of hybridization referred to as linear programming embedded metaheuristics where the algorithm searches only over the integer variables and a linear programming subproblem is solved corresponding to every solution visited, which can be computationally prohibitive. Numerical examples showed that the proposed linear programming assisted (not embedded) genetic algorithm is superior to the embedded approach and as well as to a resource intensive multi-population pure parallel genetic algorithm.