Abstract With the development of globalization, distributed manufacturing has become one of the main modes of manufacturing. The situation in which a number of heterogeneous factories coproduce a batch of jobs is ubiquitous in the field of distributed manufacturing. Compared with isomorphic factories, heterogeneous factories bring further difficulty in assigning jobs to factories. This paper considers the heterogeneity between factories in distributed flow-shop scheduling for the first time. This paper addresses a distributed heterogeneous no-wait flowshop scheduling problem (DHNWFSP) to minimize the makespan, where the factories have differences among them, including the number of machines, machine technology, raw material supply, and transportation conditions. In this problem, the numbers and types of machines in each factory are different, and this means that the jobs have to be processed through different processing paths. To effectively solve this DHNWFSP, a discrete artificial bee colony algorithm (DABC) is proposed. Firstly, to obtain a feasible neighborhood solution, four neighborhood search operators based on the characteristics of this problem are presented to search neighborhoods during the employed bee phase and onlooker bee phase. Then, a new method to accelerate the evaluation of the obtained neighborhood is proposed to reduce the computation time. Moreover, an efficient population update method is designed in the onlooker bee phase. Finally, a variable neighborhood descent (VND) algorithm based on four local-search methods is embedded into the scout bee phase to strengthen the local search ability of the overall algorithm. To validate the performance of the proposed algorithm, a series of numerical experiments are executed for small- and large-scale problems to compare the DABC with some state-of-art algorithms in terms of solving the DHNWFSP. The results show that the proposed DABC obtains the highest-quality solutions.