Single-machine scheduling with fixed energy recharging times to minimize the number of late jobs and the number of just-in-time jobs: A parameterized complexity analysis

We study single-machine scheduling problems where processing each job requires both processing time and rechargeable energy. Subject to a predefined energy capacity, energy can be recharged after each job during a fixed recharging period. Our focus is on two due date-related scheduling criteria: min...

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Bibliographic Details
Published inEuropean journal of operational research Vol. 324; no. 1; pp. 40 - 48
Main Authors Yu, Renjie, Oron, Daniel
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.07.2025
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Summary:We study single-machine scheduling problems where processing each job requires both processing time and rechargeable energy. Subject to a predefined energy capacity, energy can be recharged after each job during a fixed recharging period. Our focus is on two due date-related scheduling criteria: minimizing the number of late jobs and maximizing the weighted number of jobs completed exactly at their due dates. This study aims to analyze the parameterized tractability of the two problems and develop fixed-parameter algorithms with respect to three natural parameters: the number of different due dates vd, the number of different processing times vp, and the number of different energy consumptions ve. Following the proofs of NP-hardness across several contexts, we demonstrate that both problems remain intractable when parameterized by vd and vp. To complement our results, we show that both problems become fixed-parameter tractable (FPT) when parameterized by ve and vd, and are solvable in polynomial time when both ve and vp are constant. •Single machine scheduling problems with renewable energy constraints are considered.•Minimizing the number of late jobs is shown to be strongly NP-hard.•Minimizing the weighted number of Just in Time jobs is shown to be strongly NP-hard.•The complexity of both problems is investigated under fixed parameter settings.•Efficient algorithms are developed for parameter combinations with upper bounds.
ISSN:0377-2217
DOI:10.1016/j.ejor.2025.01.007