S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
This paper investigates the single-machine inverse scheduling problem with adjusted due-dates (SISPAD) which has a strong background in practical industries. In the SISPAD, the parameters values are uncertain, and the objective is to obtain the optimal schedule sequence through minimal adjusting processing parameters or the job sequence for a promising target. First, a SISPAD mathematical model is devised to handle uncertain processing parameters and scheduling problem at the same time. Then, this paper proposes three hybrid algorithms (HVNG) that combine variable neighborhood search (VNS) algorithm and genetic algorithm by using series, parallel, and insert structure for solving the SISPAD. In the proposed HVNG, a well-designed encoding strategy is presented to achieve processing operator and job parameter simultaneous optimization. To improve the diversity and quality of the individuals, a double non-optimal scheduling method is designed to construct initial population. Compared to the fixed neighborhood structure in regular VNS, a dynamic neighborhood set update mechanism is utilized to exploit the potential search space. In addition, three different neighborhood structures are used in the HVNG algorithm. Finally, two set public problem instances are provided for the HVNG algorithm. Empirical studies demonstrate that the proposed algorithm significantly outperforms its rivals.
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The authors acknowledge the National Natural Science Foundation of China (Grants: 51605267, 51775216), the Natural Science Foundation of Shandong Province, China (Grant: ZR2016EEQ07), the Colleges and Universities of Shandong Province Science and Technology Plan Projects (Grant: J16LB04), and Program for HUST Academic Frontier Youth Team.
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
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