## 1 Introduction  
In this paper, a novel disruptive innovation-like algorithm (DILA) is proposed for solving the single-machine scheduling problem with sequence-dependent family setup times (SMSDFS) to minimize the total tardiness. The candidate scheduling problem can be found in real-life manufacturing environments, such as the module process in Thin film transistor liquid crystal display (TFT LCD) manufacturing [1] and steel wire factories [2, 3]. In addition, the considered SMSDFS problem is also suitable for evaluating the performance of the DILA. This is because the single-machine scheduling problem can be used as the basis for the development of heuristics [4]. The concept of the proposed DILA was mainly derived from [PERSON]'s [5] theory of disruptive innovation. Disruptive innovation divides the market into a mainstream and an emerging market. The price and quality of the products offered by companies within the mainstream market are higher, which is not accepted by all consumers. Hence, another group of companies introduces disruptive innovation products into the emerging market to attract those consumers who do not accept the products offered in the mainstream market. Consumers in the emerging market have no choices other than the disruptive innovation products. However, with the improvement of the quality of the low-end products, mainstream consumers, who are more fastidious about the quality of products, also move away from the original value network to enter this emerging market. This also means that companies in the mainstream market that focus on the provision of high-end products cannot grow after a period of time. Thus, the emerging market gradually becomes a mainstream market. In addition, the companies in the emerging market will continue to self-develop and grow their business evolution and development processes as well as imitate the management approach of the companies with outstanding performance. Somecompanies will exit the market because of certain factors leading to business failure, while new companies will also join the market. The theory of disruptive innovation includes two markets: the mainstream market and the emerging market. The two markets used in DILA represent two populations: the mainstream market population and the emerging market population. Therefore, the DILA can be regarded as a kind of population-based algorithm. Previous studies on the development of algorithms, such as genetic algorithms, particle swarm optimization algorithms, and ant colony optimization algorithms, usually consider only one population. There are many members in each population, with each member representing a company. In other words, each company is a member, which is a solution, while the profitability performance of companies represents the quality of the solutions. There is a \"co-opetition\" relationship between companies, which allows them to learn from each other and to put more effort into moving toward profitability. In addition, members with poor performance may be eliminated from the population, and new members can be added to the population. Even without setup considerations, the single-machine total tardiness problem is proven to be NP-hard (non-deterministic polynomial-time hardness) [6], so the candidate problem is a NP-hard problem as well. It requires much computational time to find the optimal solution. Therefore, the development of heuristics that find near-optimal solutions in a reasonable amount of computational time has attracted the attention of many researchers in recent decades. The SMSDFS problem has been solved by several research studies. [PERSON] [7] examines heuristic solution procedures for scheduling jobs on a single machine to minimize the maximum lateness in the presence of setup times between different job families. [PERSON] et al. [2] develop a tabu search and [PERSON] et al. [3] present a simulated annealing algorithm to solve the SMSDFS problem of minimizing maximum lateness.