A memetic algorithm for the flexible flow line scheduling problem with processor blocking

This paper introduces an efficient memetic algorithm (MA) combined with a novel local search engine, namely, nested variable neighbourhood search (NVNS), to solve the flexible flow line scheduling problem with processor blocking (FFLB) and without intermediate buffers. A flexible flow line consists of several processing stages in series, with or without intermediate buffers, with each stage having one or more identical parallel processors. The line produces a number of different products, and each product must be processed by at most one processor in each stage. To obtain an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches and optimization tools is extremely difficult. Our proposed MA employs a new representation, operators, and local search method to solve the above-mentioned problem. The computational results obtained in experiments demonstrate the efficiency of the proposed MA, which is significantly superior to the classical genetic algorithm (CGA) under the same conditions when the population size is increased in the CGA.