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Using the genetic algorithm to reduce tardiness by tightening the deadline date for stochastic processing

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Abstract

Tardiness time constraints with an unknown due date, which have a broad range of applications in the manufacturing, mechanical, electrical, and other industries, are crucial in the research domains. Suppose a scheduling problem where the goal for assigning due dates is to create those as tight as feasible, but the goal for sequencing jobs is to minimize their tardiness. In the instance of a stochastic single-machine model with uniformly distributed task durations, we develop a variant of this market. This paper clarifies how to set a strict deadline and reduce job tardiness by determining the best order of the projects through two distinct phases. We create a genetic algorithm approach expected to find tightness of the due date of the issue and then compare it against a heuristic solution. These algorithms perform better than heuristic methods, and they also fit for small-scale non-parallel machine tardiness scheduling problems, according to numerical computational results focused on the various machine scheduling problems.

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Data availability

Data for this article were gathered from Geebon Small Scale Industry in Chennai.

Abbreviations

B j :

Service-level target

SD:

Standard deviation for jth job

VR:

Variance

CVR:

Cumulative variance

t j :

Square root of the CVR

M j :

Cumulative mean

[B j R]:

Smallest integer greater than or equal to BjR

GM:

Genetic algorithm

SEPT:

Shortest expected processing time

LPT:

Longest processing time

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Authors and Affiliations

  1. Department of Mathematics, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India

    E. Janaki & A. Mohamed Ismail

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  1. E. Janaki
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  2. A. Mohamed Ismail
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Janaki, E., Ismail, A.M. Using the genetic algorithm to reduce tardiness by tightening the deadline date for stochastic processing. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08728-2

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