Two new approximation schemes for maximizing the weighted number of just-in-time jobs in a multi-machine proportionate flow shop

We propose two new fully polynomial-time approximation schemes for maximizing the weighted number of just-in-time jobs in a multi-machine proportionate flow shop. Both are set up using recently proposed frameworks for the construction of this type of approximation schemes for monotone dynamic programming formulations, and are faster by a linear factor with respect to the number of jobs, up to log terms, compared to the state-of-the-art fully polynomial-time approximation scheme for the problem.

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Publication Details

Journal
Discrete Applied Mathematics
Published
2025-08-11
DOI
https://doi.org/10.1016/j.dam.2025.08.008
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

Two new approximation schemes for maximizing the weighted number of just-in-time jobs in a multi-machine proportionate flow shop

Nir Halman, Stanisław Gawiejnowicz
Discrete Applied Mathematics
Scheduling and Optimization Algorithms
article

Two new approximation schemes for maximizing the weighted number of just-in-time jobs in a multi-machine proportionate flow shop

Nir Halman, Stanisław Gawiejnowicz
article en

Abstract

We propose two new fully polynomial-time approximation schemes for maximizing the weighted number of just-in-time jobs in a multi-machine proportionate flow shop. Both are set up using recently proposed frameworks for the construction of this type of approximation schemes for monotone dynamic programming formulations, and are faster by a linear factor with respect to the number of jobs, up to log terms, compared to the state-of-the-art fully polynomial-time approximation scheme for the problem.

Discrete Applied MathematicsVol. 377
Bar-Ilan University (IL), Adam Mickiewicz University in Poznań (PL)
United States-Israel Binational Science Foundation, Israel Science Foundation
Decent work and economic growth
Openalex Percentile: Top 72%
Scheduling and Optimization Algorithms
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