—In this paper, scheduling problem of flowshopwith the criterion of minimizing the total flow time has beenconsidered. An effective hybrid Quantum GeneticAlgorithm and Variable Neighborhood Search (QGA-VNSor QGAVNS) has been proposed as solution of Flow ShopScheduling Problem (FSSP). First, the QGA is consideredfor global search in optimal solution and then VNS has beenintegrated for enhancing the local search capability. Anadaptive two-point crossover and quantum interferenceoperator (QIC) has been used in quantum chromosomes,which is based on the probability learning and quality ofsolution at each iteration. Further, a Longest CommonSequence (LCS) method has been adopted to construct theneighborhood solutions for intensifying local search withVNS. The neighborhood solutions will be based on thecommon sequence similar to the longest common sequencein global solution in each iteration, represented as LCSg.After selection of individual, VNS will be applied furtherexploring the local search space based on LCSneighborhood solutions. Results and comparisons withdifferent algorithms based on the famous benchmarksdemonstrates the effectiveness of proposed QGA-VNS.
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