Abstract
This paper proposes a novel disruptive innovation-like algorithm (DILA) for the problem of scheduling a single machine with sequence-dependent family setup times to minimize the total tardiness. The proposed algorithm is derived from Christensen’s (1997) theory of disruptive innovation. Based on this theory, a DILA is proposed, which first generates two initial populations: the mainstream market population and the emerging market population. Then, to improve the quality of solutions in the populations, three phases are created within a generation. Finally, the DILA allows the populations to evolve for several generations until the termination condition is met. Two populations constructed in DILA are to overcome the weakness of premature. In addition, two functions—the member-added function and member-removed function—implemented in DILA are added to increase the diversity. The DILA was tested on a dataset of 1440 observations from the literature. The computational results confirm that DILA is very effective.
Highlights
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
This paper has proposed a novel DILA to solve the problem of single-machine scheduling with sequence-dependent family setup times while minimizing the total tardiness of jobs
The DILA allows the population to evolve for several generations until the maximum execution time is met
Summary
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 considered SMSDFS problem is 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 authors use these three heuristics, ILS_BASIC, ILS_DP, and ILS_DP + PR, and the HGA (hybrid genetic algorithm) [10] to obtain solutions for a set of 1440 benchmark instances These benchmark instances are used to access the performance of the proposed DILA. The remainder of this paper is organized as follows: Section 2 provides a detailed description of the problem, Section 3 describes the proposed algorithms, Section 4 presents the computational experiments and reports the results, and Section 5 concludes the paper
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