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