Impact of learning and fatigue factors on single machine scheduling with penalties for tardy jobs

Dondeti, V. Reddy; Mohanty, Bidhu

doi:10.1016/s0377-2217(97)00070-2

Cited by 39 publications

(12 citation statements)

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“…Biskup (1999) introduced into scheduling theory a dependency of job processing times on the position of the job in the sequence (i.e., on the number of already processed jobs). The concept of the learning effect was introduced to scheduling theory by Meilijson and Tamir (1984) as well as Dondeti and Mohanty (1998). However, they did not give any reasonable real-life practical example for the model combining the scheduling process with the learning one.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…The aging effect is a process opposite to the learning one, i.e., the workers get tired during the long time production process and their efficiency decreases. The concept of the learning effect was introduced to scheduling theory by Meilijson and Tamir (1984) as well as Dondeti and Mohanty (1998). Liman et al (1997) examined a problem with a common due window assignment and changeable job processing times: (34) where p j is the normal processing time, p j is minimum processing time, while x j is the amount of reduction in processing time (0 ≤ x j ≤ p j − p j ) and G j x j is the cost of shortening the job.…”

Section: Other Modelsmentioning

confidence: 99%

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Scheduling problems with a common due window assignment: A survey

Janiak¹,

Kwiatkowski²,

Lichtenstein³

2013

International Journal of Applied Mathematics and Computer Science

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In this article a survey of studies on scheduling problems with a common due window assignment and earliness/tardiness penalty functions is presented. A due window is a generalization of the classical due date and describes a time interval in which a job should be finished. If a job is completed before or after the due window, it incurs an earliness or a tardiness penalty, respectively. In this survey we separately analyse the classical models with job-independent and job-dependent earliness/tardiness penalty functions and some other more complicated models. We describe the computational complexity of the problems and the main features of the approaches developed to solve them. Particular attention is paid to practical applications of the analysed models. As turns out, some complicated models combining classical scheduling problems with, e.g., learning and aging effects have no reasonable practical justification in the literature.

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Section: Discussion and Recommendationsmentioning

confidence: 99%

Section: Other Modelsmentioning

confidence: 99%

Scheduling problems with a common due window assignment: A survey

Janiak¹,

Kwiatkowski²,

Lichtenstein³

2013

International Journal of Applied Mathematics and Computer Science

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“…Into scheduling theory it was brought by Biskup (1999) as a dependency of job processing times on the number of already processed jobs (i.e., on the position of the job in a sequence). However, the learning effect in the context of scheduling was defined already in the 80s and 90s of 20th century (see e.g., Meilijson and Tamir (1984), Dondeti and Mohanty (1998), where the processing times were dependent on the total processing time of already processed jobs rather than their number). The obtained results in this domain were summarized recently by Biskup (2008).…”

Section: Scheduling Problems With a Learning Effectmentioning

confidence: 99%

“…Finally, it is worth mentioning that the concept of learning under scheduling context was mentioned some years earlier (Meilijson and Tamir (1984), Dondeti and Mohanty (1998)), however, these authors do not consider any specified model of job processing times. They provide only some preliminary general considerations.…”

Section: Scheduling Problems With a Learning Effectmentioning

confidence: 99%

Scheduling Problems with Learning and Ageing Effects: A Survey

Janiak

Krysiak

Trela

2011

dmms

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Abstract. In recent years, many papers concerning scheduling problems with simultaneous learning and ageing effects were published. In this paper, the state of the art of research concerning these problems is presented. In order to facilitate understanding this subject, the scheduling problems where these effects occur separately, are firstly explained. Then, the papers devoted to scheduling problems combining the effects of learning and ageing are discussed. Particular attention was paid on practical applications of the considered scheduling problems. After thorough analysis it turned out that both scheduling problems with learning effect, and with ageing effect, as well as, in particular, the problems with models merging learning and ageing effects do not have any reasonable real-life applications. This is because the learning and ageing effects are in general long time horizon phenomena observed in repetitive systems and scheduling theory concerns either with repetitive short-horizon planning problems or single long-horizon projects. Therefore, there is no sense to continue research considering these scheduling problems from practical (computer engineering, automatic control, technical and economical) point of view, unless such reasonable real-life example appears.

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“…The author showed that as long as processing times are decreasing, shortest processing time (SPT) sequencing minimizes flow time. Dondeti and Mohanty (1998) presented complexity results and developed dynamic programming algorithms for due date objectives where both learning and fatigue effects are considered. Biskup (1999) introduced a framework within the single resource scheduling environment that led to a series of related papers.…”

Section: Learning Effectsmentioning

confidence: 99%