Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm

This paper presents a novel hybrid algorithm (TABC) that combines the artificial bee colony (ABC) and tabu search (TS) to solve the hybrid flow shop (HFS) scheduling problem with limited buffers. The objective is to minimize the maximum completion time. Unlike the original ABC algorithm, in TABC, each food source is represented by a string of job numbers. A novel decoding method is embedded to tackle the limited buffer constraints in the schedules generated. Four neighborhood structures are embedded to balance the exploitation and exploration abilities of the algorithm. A TS-based self-adaptive neighborhood strategy is adopted to impart to the TABC algorithm a learning ability for producing neighboring solutions in different promising regions. Furthermore, a well-designed TS-based local search is developed to enhance the search ability of the employed bees and onlookers. Moreover, the effect of parameter setting is investigated by using the Taguchi method of design of experiment (DOE) to determine the suitable values for key parameters. The proposed TABC algorithm is tested on sets of instances with large scales that are generated based on realistic production. Through a detailed analysis of the experimental results, the highly effective and efficient performance of the proposed TABC algorithm is contrasted with the performance of several algorithms reported in the literature.