A Quantum Bio parasitic Genetic Algorithm for Solving a Hybrid Scheduling Problem of Flow Shop and Two Stage Transportation

In order to solve an integration scheduling problem of flow shop and two stage transportation, we consider various constraints involving production and distribution, and build a mixed integer programming model. In this model, the production operation is flow shop scheduling, while the distribution operation consists two stages. In the first stage, the jobs are conveyed from the warehouse to the workshop by a crane, and in the second stage, the finished goods are transported to the customers by the carriers. According to the features of the above integrated scheduling problem, we propose a quantum bio parasitic genetic algorithm (QBGA) based on quantum theory and parasitic theory. Firstly, a coding method with transport batches and production order is designed to ensure that each individual is the feasible solution of fully coordinating both production capacity and transportation capacity. At the same time, two populations, the host and the parasitic, are built to perform the mechanisms of both parasitic and the anti parasitic so as to increase the genetic diversity and accelerate the algorithm convergence speed. Finally, simulation experiments illustrate the efficiency of QBGA in this work.