Routing distributions and their impact on dispatch rules
Motivated by the job-shop production process of our industry partner, we examine dispatching rules effects on two key performance indicators (KPIs) -- job lateness and the percentage of late jobs. In the literature, authors use the uniform distribution to generate random job shop data. In addition t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Andrew Brown [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © COPYRIGHT 2015 Elsevier Science Publishers |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Computers & industrial engineering - Amsterdam [u.a.] : Elsevier, 1976, 88(2015), Seite 293-306 |
|---|---|
| Übergeordnetes Werk: |
volume:88 ; year:2015 ; pages:293-306 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.cie.2015.07.014 |
|---|
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OLC1963022440 |
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| 520 | |a Motivated by the job-shop production process of our industry partner, we examine dispatching rules effects on two key performance indicators (KPIs) -- job lateness and the percentage of late jobs. In the literature, authors use the uniform distribution to generate random job shop data. In addition to our discussion on dispatching rules, we propose an alternative idea for random job shop data, the routing distribution, and we compare dispatching rules performance using KPI frontiers under different routing distributions. We show that using their current dispatch rule, earliest operation due date (EODD), the industry partner is never worse off, even as their job-shop's operational environment changes. We further show that using multiple dispatch rules across several job-shop departments does improve a job-shop's performance on the KPIs, though the improvement is small and in some cases may not be statistically significant. In addition, we find that EODD is one of several dispatching rule which consistently lie on the KPI frontier for different job routing distributions. We find that dispatching rule performance is greatly affected by the routing distribution of the job-shop where the rules are employed. Lastly, we leave the readers with some insight into determining which dispatch rules and routing distributions should be considered for different job shops. | ||
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10.1016/j.cie.2015.07.014 doi PQ20160617 (DE-627)OLC1963022440 (DE-599)GBVOLC1963022440 (PRQ)c2812-2bf48757f0b3edde91211e3b89c3eaf3d878b03289776f360d0168e29e6c2bb40 (KEY)0011885020150000088000000293routingdistributionsandtheirimpactondispatchrules DE-627 ger DE-627 rakwb eng 004 DNB 85.35 bkl 54.80 bkl 83.00 bkl Andrew Brown verfasserin aut Routing distributions and their impact on dispatch rules 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Motivated by the job-shop production process of our industry partner, we examine dispatching rules effects on two key performance indicators (KPIs) -- job lateness and the percentage of late jobs. In the literature, authors use the uniform distribution to generate random job shop data. In addition to our discussion on dispatching rules, we propose an alternative idea for random job shop data, the routing distribution, and we compare dispatching rules performance using KPI frontiers under different routing distributions. We show that using their current dispatch rule, earliest operation due date (EODD), the industry partner is never worse off, even as their job-shop's operational environment changes. We further show that using multiple dispatch rules across several job-shop departments does improve a job-shop's performance on the KPIs, though the improvement is small and in some cases may not be statistically significant. In addition, we find that EODD is one of several dispatching rule which consistently lie on the KPI frontier for different job routing distributions. We find that dispatching rule performance is greatly affected by the routing distribution of the job-shop where the rules are employed. Lastly, we leave the readers with some insight into determining which dispatch rules and routing distributions should be considered for different job shops. Nutzungsrecht: © COPYRIGHT 2015 Elsevier Science Publishers Route optimization Comparative studies Probability distribution Performance evaluation Job shops Production management Business metrics Stanko Dimitrov oth Ada Y Barlatt oth Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier, 1976 88(2015), Seite 293-306 (DE-627)129448982 (DE-600)196993-6 (DE-576)014814994 0360-8352 nnns volume:88 year:2015 pages:293-306 http://dx.doi.org/10.1016/j.cie.2015.07.014 Volltext http://search.proquest.com/docview/1713974913 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_70 85.35 AVZ 54.80 AVZ 83.00 AVZ AR 88 2015 293-306 |
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10.1016/j.cie.2015.07.014 doi PQ20160617 (DE-627)OLC1963022440 (DE-599)GBVOLC1963022440 (PRQ)c2812-2bf48757f0b3edde91211e3b89c3eaf3d878b03289776f360d0168e29e6c2bb40 (KEY)0011885020150000088000000293routingdistributionsandtheirimpactondispatchrules DE-627 ger DE-627 rakwb eng 004 DNB 85.35 bkl 54.80 bkl 83.00 bkl Andrew Brown verfasserin aut Routing distributions and their impact on dispatch rules 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Motivated by the job-shop production process of our industry partner, we examine dispatching rules effects on two key performance indicators (KPIs) -- job lateness and the percentage of late jobs. In the literature, authors use the uniform distribution to generate random job shop data. In addition to our discussion on dispatching rules, we propose an alternative idea for random job shop data, the routing distribution, and we compare dispatching rules performance using KPI frontiers under different routing distributions. We show that using their current dispatch rule, earliest operation due date (EODD), the industry partner is never worse off, even as their job-shop's operational environment changes. We further show that using multiple dispatch rules across several job-shop departments does improve a job-shop's performance on the KPIs, though the improvement is small and in some cases may not be statistically significant. In addition, we find that EODD is one of several dispatching rule which consistently lie on the KPI frontier for different job routing distributions. We find that dispatching rule performance is greatly affected by the routing distribution of the job-shop where the rules are employed. Lastly, we leave the readers with some insight into determining which dispatch rules and routing distributions should be considered for different job shops. Nutzungsrecht: © COPYRIGHT 2015 Elsevier Science Publishers Route optimization Comparative studies Probability distribution Performance evaluation Job shops Production management Business metrics Stanko Dimitrov oth Ada Y Barlatt oth Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier, 1976 88(2015), Seite 293-306 (DE-627)129448982 (DE-600)196993-6 (DE-576)014814994 0360-8352 nnns volume:88 year:2015 pages:293-306 http://dx.doi.org/10.1016/j.cie.2015.07.014 Volltext http://search.proquest.com/docview/1713974913 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_70 85.35 AVZ 54.80 AVZ 83.00 AVZ AR 88 2015 293-306 |
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Routing distributions and their impact on dispatch rules |
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Routing distributions and their impact on dispatch rules |
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Routing distributions and their impact on dispatch rules |
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Motivated by the job-shop production process of our industry partner, we examine dispatching rules effects on two key performance indicators (KPIs) -- job lateness and the percentage of late jobs. In the literature, authors use the uniform distribution to generate random job shop data. In addition to our discussion on dispatching rules, we propose an alternative idea for random job shop data, the routing distribution, and we compare dispatching rules performance using KPI frontiers under different routing distributions. We show that using their current dispatch rule, earliest operation due date (EODD), the industry partner is never worse off, even as their job-shop's operational environment changes. We further show that using multiple dispatch rules across several job-shop departments does improve a job-shop's performance on the KPIs, though the improvement is small and in some cases may not be statistically significant. In addition, we find that EODD is one of several dispatching rule which consistently lie on the KPI frontier for different job routing distributions. We find that dispatching rule performance is greatly affected by the routing distribution of the job-shop where the rules are employed. Lastly, we leave the readers with some insight into determining which dispatch rules and routing distributions should be considered for different job shops. |
| abstractGer |
Motivated by the job-shop production process of our industry partner, we examine dispatching rules effects on two key performance indicators (KPIs) -- job lateness and the percentage of late jobs. In the literature, authors use the uniform distribution to generate random job shop data. In addition to our discussion on dispatching rules, we propose an alternative idea for random job shop data, the routing distribution, and we compare dispatching rules performance using KPI frontiers under different routing distributions. We show that using their current dispatch rule, earliest operation due date (EODD), the industry partner is never worse off, even as their job-shop's operational environment changes. We further show that using multiple dispatch rules across several job-shop departments does improve a job-shop's performance on the KPIs, though the improvement is small and in some cases may not be statistically significant. In addition, we find that EODD is one of several dispatching rule which consistently lie on the KPI frontier for different job routing distributions. We find that dispatching rule performance is greatly affected by the routing distribution of the job-shop where the rules are employed. Lastly, we leave the readers with some insight into determining which dispatch rules and routing distributions should be considered for different job shops. |
| abstract_unstemmed |
Motivated by the job-shop production process of our industry partner, we examine dispatching rules effects on two key performance indicators (KPIs) -- job lateness and the percentage of late jobs. In the literature, authors use the uniform distribution to generate random job shop data. In addition to our discussion on dispatching rules, we propose an alternative idea for random job shop data, the routing distribution, and we compare dispatching rules performance using KPI frontiers under different routing distributions. We show that using their current dispatch rule, earliest operation due date (EODD), the industry partner is never worse off, even as their job-shop's operational environment changes. We further show that using multiple dispatch rules across several job-shop departments does improve a job-shop's performance on the KPIs, though the improvement is small and in some cases may not be statistically significant. In addition, we find that EODD is one of several dispatching rule which consistently lie on the KPI frontier for different job routing distributions. We find that dispatching rule performance is greatly affected by the routing distribution of the job-shop where the rules are employed. Lastly, we leave the readers with some insight into determining which dispatch rules and routing distributions should be considered for different job shops. |
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Routing distributions and their impact on dispatch rules |
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