Minimizing the total tardiness and job rejection cost in a proportionate flow shop with generalized due dates

Mor, Baruch; Mosheiov, Gur; Shabtay, Dvir

doi:10.1007/s10951-021-00697-4

Cited by 15 publications

(3 citation statements)

References 27 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%

“…They proved that the latter problem is NP‐hard and then proposed pseudo‐polynomial time DP algorithms. Recently, Mor et al (2021) studied two MSR‐GDD problems in a proportionate flow shop environment, where the objective of the first problem is to minimize the sum of the total tardiness and TRC, while the objective of the second problem is to minimize TRC subject to a given upper bound on the total tardiness. They showed that these two problems are NP‐hard and designed both exact algorithms and approximation schemes for solving them.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Order acceptance and scheduling with delivery under generalized parameters

et al. 2023

Naval Research Logistics

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“…The DPFSP is the extension of the permutation flowshop problem [2]. The most common objective of studying flowshop scheduling problems with rejection cost and tardiness penalty is to minimize the total tardiness and rejection cost [3]. Since single-machine scheduling with tardiness and rejected jobs is NP-hard [4], the DPRTR is also NP-hard because DPRTR has multiple factories, and each factory has multiple machines.…”

Section: Introductionmentioning

confidence: 99%

NEH-based heuristics for the distributed permutation flowshop with rejection and tardiness

Lin

Jia²,

Jing³

2023

Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022)

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The distributed permutation flowshop scheduling problem (DPFSP) has become a research hotspot. In order to maximize total profit, two improved NEH algorithms are proposed to solve the DPFSP with rejection cost, tardiness penalty and release time. The basic NEH heuristic consists of two parts: generating initial job sequence and assigning jobs. This paper optimizes the NEH heuristic in these two parts. Two priority rules are proposed for generating initial job sequences. Two heuristics are proposed for assigning jobs according to the increment of tardiness penalty and increment of profit, respectively. In order to verify the performance of the proposed algorithms, they are compared with similar algorithms in the literature. The results validate the superiority of the proposed two prioritization rules and two heuristics.

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Single Machine Scheduling with Rejection and Generalized Parameters

Zhang

2021

Combinatorial Optimization and Applications

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Minimizing the total tardiness and job rejection cost in a proportionate flow shop with generalized due dates

Cited by 15 publications

References 27 publications

Order acceptance and scheduling with delivery under generalized parameters

Order acceptance and scheduling with delivery under generalized parameters

NEH-based heuristics for the distributed permutation flowshop with rejection and tardiness

Single Machine Scheduling with Rejection and Generalized Parameters

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