id |
ndltd-OhioLink-oai-etd.ohiolink.edu-osu1264613608
|
record_format |
oai_dc
|
spelling |
ndltd-OhioLink-oai-etd.ohiolink.edu-osu12646136082021-08-03T05:58:31Z Effective dispatching in the material requirements planning job shop McCaskey, Donald Wayne, Jr. <p>Dispatching decisions are the final step in implementing all production plans. They determine the processing sequence of jobs and thus determine when jobs will be completed. These completion times influence customer service levels, inventory levels and plant efficiency. This study evaluates the relative importance of the dispatching process and presents a new dispatch rule which incorporates capacity requirements data into the decision.</p><p>An MRP/CRP operating environment is assumed. The use of Material Requirement Planning (MRP) and Capacity Requirements Planning (CRP) is common in multiproduct, multistage manufacturing systems. Often the conversion processes are organized by function, thus creating a job shop environment. The study focuses on the effectiveness of dispatching decisions in these MRP/CRP job shops.</p><p>Two aspects of the study are unique. First, the study allows overtime to be scheduled for individual work centers based on weekly CRP load profiles. Second, it presents a new dispatch rule, DSSU. This rule advances jobs whose processing paths are more congested in an effort to improve the utilization of bottleneck work centers. Since CRP can schedule overtime and thus significantly increase the capacity of bottlenecks, their queues could dry up. The DSSU rule advances jobs whose downstream work centers are currently bottlenecks, thus temporarily increasing their input rates. The impact on system performance is compared with that of traditional dispatching techniques in order to assess the value of including the CRP data.</p><p>This study also evaluates the relative importance of improvements in dispatching, lot sizing, slack capacity, forecast error and demand variability.</p><p>The experimental results are generated by a simulation model. Results are evaluated by analysis of variance techniques. The main system performance criteria are total inventory investment, mean backorder time to customers and mean percent of overtime scheduled.</p><p>The major contribution of this research is the insight gained into the interactions of controlled factors. Better dispatching can significantly improve system performance if lot sizes are small. However, for many operating environments the importance of the dispatching technique is of minor managerial significance, and other considerations, such as lot sizing and slack capacity, have a stronger influence on system performance.</p> 1987 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1264613608 http://rave.ohiolink.edu/etdc/view?acc_num=osu1264613608 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
|
collection |
NDLTD
|
language |
English
|
sources |
NDLTD
|
author |
McCaskey, Donald Wayne, Jr.
|
spellingShingle |
McCaskey, Donald Wayne, Jr.
Effective dispatching in the material requirements planning job shop
|
author_facet |
McCaskey, Donald Wayne, Jr.
|
author_sort |
McCaskey, Donald Wayne, Jr.
|
title |
Effective dispatching in the material requirements planning job shop
|
title_short |
Effective dispatching in the material requirements planning job shop
|
title_full |
Effective dispatching in the material requirements planning job shop
|
title_fullStr |
Effective dispatching in the material requirements planning job shop
|
title_full_unstemmed |
Effective dispatching in the material requirements planning job shop
|
title_sort |
effective dispatching in the material requirements planning job shop
|
publisher |
The Ohio State University / OhioLINK
|
publishDate |
1987
|
url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1264613608
|
work_keys_str_mv |
AT mccaskeydonaldwaynejr effectivedispatchinginthematerialrequirementsplanningjobshop
|
_version_ |
1719428747469258752
|