Applying “Peeling Onion” approach for competitive analysis in online scheduling with rejection

Ma, Ran; Guo, Sainan

doi:10.1016/j.ejor.2020.08.009

Cited by 15 publications

(3 citation statements)

References 32 publications

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“…For more references on off-line MSR problems, we refer the readers to a survey by Shabtay et al (2013). Some recent articles on MSR are Agnetis and Mosheiov (2017), Chen (2010), Gerstl and Mosheiov (2017), Hermelin et al (2019), Liu and Lu (2020), Liu (2020), Ma and Guo (2021), Mor et al (2020), Mor et al (2021), Oron (2021), Wang et al (2018), Zhang et al (2009), andZou andYuan (2020), among others.…”

Section: Literature Reviewmentioning

confidence: 99%

Order acceptance and scheduling with delivery under generalized parameters

et al. 2023

Naval Research Logistics

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Inspired by the concept of generalized due dates introduced by Hall (IIE Transactions, 18(2), 220–222), in this article we first define four new generalized parameters in machine scheduling: the generalized release dates (GRD), generalized processing times (GPT), generalized rejection costs (GRC), and generalized delivery times (GDT). We then study order acceptance and scheduling problems with delivery consideration under the generalized parameters of GRD, GPT, GRC, and GDT, respectively. The objective of each scheduling problem in consideration is to minimize the maximum delivery completion time of the accepted jobs plus the total rejection penalty cost of the rejected jobs. We show that two of the six problems studied in this article are weakly NP‐hard while the other four ones can be solved in polynomial time. For each NP‐hard problem, we provide a pseudo‐polynomial time algorithm, a 2‐approximation algorithm and a fully polynomial‐time approximation scheme.

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Section: Literature Reviewmentioning

confidence: 99%

Order acceptance and scheduling with delivery under generalized parameters

et al. 2023

Naval Research Logistics

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“…Such a technique compares the results obtained with an online strategy to the result that could have been obtained if one had known the exact length of usage time in advance, with the latter scenario represented by an optimal offline strategy. The competitive analysis method has been widely applied in the fields of finance [14,15], operations research [16][17][18][19], and computer science [20,21].…”

Section: Introductionmentioning

confidence: 99%

Competitive Analysis of the Online Leasing Problem for Scarce Resources

Chen

Feng

2023

IJERPH

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The leasing activities of enterprises are of positive significance for promoting a green and low-carbon economy. For scarce resources that can easily go into tight supply states due to changes in the external market environment, the sudden change of their price is a common phenomenon in the leasing market. This paper provides an online leasing model based on the characteristics of scarce resources in which the resource might have a sudden price surge, and the length of its usage time cannot be known in advance. An online leasing strategy ON was then developed to achieve the minimum possible acquisition cost for the scarce resource. This strategy was proven to be the optimal online strategy through competitive analysis. Computational experiments were conducted to evaluate the performance of the proposed online strategy. Furthermore, sensitivity analysis of the problem parameters shows that increasing resource reserves and reducing the price-to-rent ratio are effective ways to improve the performance of enterprise leasing operation management for scarce resources.

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“…Due to the difficulty of directly proving the competitive ratio of DSBPT, we have incorporated flexibility into algorithm DSBPT and introduced an auxiliary flexible online algorithm called "Flexible Delayed Shortest Basic Processing Time" (FDSBPT). Based on the online algorithm FDSBPT, we have extended a method known as "Peeling Onion" that inspired by Ma [15,16] for competitive ratio analysis. Generally, when proving the competitive ratio, we will use a non-dominant instance with the worst competitive ratio and regular structures.…”

Section: Introductionmentioning

confidence: 99%

A best possible algorithm for an online scheduling problem with position-based learning effect

Zhang

2022

JIMO

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<p style='text-indent:20px;'>In this paper, we focus on an online scheduling problem with position-based learning effect on a single machine, where the jobs are released online over time and preemption is not allowed. The information about each job <inline-formula><tex-math id="M1">\begin{document}$ J_j $\end{document}</tex-math></inline-formula>, including the basic processing time <inline-formula><tex-math id="M2">\begin{document}$ p_j $\end{document}</tex-math></inline-formula> and the release time <inline-formula><tex-math id="M3">\begin{document}$ r_j $\end{document}</tex-math></inline-formula>, is only available when it arrives. The actual processing time <inline-formula><tex-math id="M4">\begin{document}$ p_j' $\end{document}</tex-math></inline-formula> of each job <inline-formula><tex-math id="M5">\begin{document}$ J_j $\end{document}</tex-math></inline-formula> is defined as a function related to its position <inline-formula><tex-math id="M6">\begin{document}$ r $\end{document}</tex-math></inline-formula>, i.e., <inline-formula><tex-math id="M7">\begin{document}$ p_j' = p_j(\alpha-r\beta) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M8">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M9">\begin{document}$ \beta $\end{document}</tex-math></inline-formula> are both nonnegative learning index. Our goal is to minimize the sum of completion time of all jobs. For this problem, we design a deterministic polynomial time online algorithm <i>Delayed Shortest Basic Processing Time</i> (DSBPT). In order to facilitate the understanding of the online algorithm, we present a relatively common and simple example to describe the execution process of the algorithm, and then by competitive analysis, we show that online algorithm DSBPT is a best possible online algorithm with a competitive ratio of 2.</p>

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Applying “Peeling Onion” approach for competitive analysis in online scheduling with rejection

Cited by 15 publications

References 32 publications

Order acceptance and scheduling with delivery under generalized parameters

Order acceptance and scheduling with delivery under generalized parameters

Competitive Analysis of the Online Leasing Problem for Scarce Resources

A best possible algorithm for an online scheduling problem with position-based learning effect

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