2015
DOI: 10.5267/j.msl.2015.4.002
|View full text |Cite
|
Sign up to set email alerts
|

Optimization of rewards in single machine scheduling in the rewards-driven systems

Abstract: The single machine scheduling problem aims at obtaining the best sequence for a set of jobs in a manufacturing system with a single machine. In this paper, we optimize rewards in single machine scheduling in rewards-driven systems such that total reward is maximized while the constraints contains of limitation in total rewards for earliness and learning, independent of earliness and learning and etc. are satisfied. In mentioned systems as for earliness and learning the bonus is awarded to operators, we conside… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 58 publications
(4 citation statements)
references
References 35 publications
0
4
0
Order By: Relevance
“…Indeed, several years may be required for structuring, implementing, and monitoring a given ISPMS as well as gathering adequate information and performing its evaluation before shifting to a different ISPMS. In particular, it would be interesting to evaluate the effect of different organizational strategies that might be implemented towards increased sustainability (Mintzberg and Waters, 1985) and the effect of the application of reward-driven systems, which are proven to foster performance and innovation (Fellnhofer, 2018;Gharaei et al, 2015). Considering the potential trade-offs between cost and completeness and precision of the system, and environmental and economic performance (Arena et al, 2015), the application from an industrial sustainability perspective appears to be quite challenging, considering the additional variables to be considered (Frini and Benamor, 2017;Gong et al, 2018;Nicolăescu et al, 2015).…”
Section: Limitations and Further Researchmentioning
confidence: 99%
“…Indeed, several years may be required for structuring, implementing, and monitoring a given ISPMS as well as gathering adequate information and performing its evaluation before shifting to a different ISPMS. In particular, it would be interesting to evaluate the effect of different organizational strategies that might be implemented towards increased sustainability (Mintzberg and Waters, 1985) and the effect of the application of reward-driven systems, which are proven to foster performance and innovation (Fellnhofer, 2018;Gharaei et al, 2015). Considering the potential trade-offs between cost and completeness and precision of the system, and environmental and economic performance (Arena et al, 2015), the application from an industrial sustainability perspective appears to be quite challenging, considering the additional variables to be considered (Frini and Benamor, 2017;Gong et al, 2018;Nicolăescu et al, 2015).…”
Section: Limitations and Further Researchmentioning
confidence: 99%
“…Furthermore, future research could examine the transfer of additional mathematical frameworks from the finance literature to decision making problems from the supply chain and operations sector. In particular, rewards-driven systems and maintenance concepts (Duan et al, 2018;Gharaei et al, 2015) could be promising to integrate sustainability into the supplier selection process.…”
Section: Further Researchmentioning
confidence: 99%
“…Pasandideh et al (2015) compared the performance of this method in solving nonlinear problems with the one of an exact method titled sequential quadratic programming [7], which is another efficient method to solve nonlinear programming problems (Pasandideh et al, 2015). Moreover, Gharaei et al (2015) confirmed the satisfactory performance of this method to solve nonlinear optimization problems (Gharaei et al, 2015). It is noted, efficient algorithms have superlinear convergence and superlinearly convergent algorithms need only a couple of iterations (Potra, 2003).…”
Section: Solution Method: Optimization the Integrated Inventory Model In Fourechelon Scmentioning
confidence: 93%