Introducing preferences in scheduling applications

Srinath, Nitin; Yilmazlar, I. Ozan; Kurz, Mary E.; Taaffe, Kevin

doi:10.1016/j.cie.2021.107831

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…If the outer task is executable, the start and the end time of it in the available time window can be determined by MPA. In the MPA, this paper takes the heuristic rules relating to preference, delay, and random strategies [30,31]. When the GAPE and MPA are terminated, the best solution in all the population is used as the final task sequence, and the final schedulable task sequence is obtained through an EH.…”

Section: Algorithm Designmentioning

confidence: 99%

Mission Scheduling of Multi-Sensor Collaborative Observation for Space Surveillance Network

Long,

Cai,

Yang

et al. 2023

J. of Syst. Eng. Electron.

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With increased dependence on space assets, scheduling and tasking of the space surveillance network (SSN) are vitally important. The multi-sensor collaborative observation scheduling (MCOS) problem is a multi-constraint and high-conflict complex combinatorial optimization problem that is nondeterministic polynomial (NP)-hard. This research establishes a sub-time window constraint satisfaction problem (STWCSP) model with the objective of maximizing observation profit. Considering the significant effect of genetic algorithms (GA) on solving the problem of resource allocation, an evolution heuristic (EH) algorithm containing three strategies that focus on the MCOS problem is proposed. For each case, a task scheduling sequence is first obtained via an improved GA with penalty (GAPE) algorithm, and then a mission planning algorithm (heuristic rule) is used to determine the specific observation time. Compared to the model without sub-time windows and some other algorithms, a series of experiments illustrate the STWCSP model has better performance in terms of total profit. Experiments about strategy and parameter sensitivity validate its excellent performance in terms of EH algorithms.

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Section: Algorithm Designmentioning

confidence: 99%