Evolutionary multi-objective blocking lot-streaming flow shop scheduling with interval processing time

This illustrates the flowchart of our proposed algorithm. First, the population is partly initialized using solutions obtained by a variable single-objective heuristic search algorithm, and the rest solutions are randomly generated. Second, a new crossover operator is developed utilizing valuable information embedded in non-dominated solutions and differentiation between parents. Third, choose solutions at a certain probability from the temporary population, keep the better ones based on their distance to the ideal point to form the offspring population. Last, merge the temporary population with the parent populations into a combined population, perform non-dominated sorting and calculate the crowding distance. The better individuals are selected based on the Pareto dominance and the crowded distance to form the parent population of the next generation. In this way, the exploration capability of the crossover operator and the exploitation ability of the ideal-point assisted local search are both considered in this algorithm. 117