The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling
The goal of this paper is to determine if the results for dynamic job-shop scheduling problems are affected by the assumptions made with regard to the processing time distributions and the scheduler's knowledge of the processing times. Three dynamic jobshop scheduling problems (including a two...
| Main Authors: | , |
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| Format: | Working Paper |
| Language: | en_US |
| Published: |
Massachusetts Institute of Technology, Operations Research Center
2004
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| Online Access: | http://hdl.handle.net/1721.1/5388 |
| _version_ | 1826216173818085376 |
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| author | Wein, Lawrence M. Ou, Jihong |
| author_facet | Wein, Lawrence M. Ou, Jihong |
| author_sort | Wein, Lawrence M. |
| collection | MIT |
| description | The goal of this paper is to determine if the results for dynamic job-shop scheduling problems are affected by the assumptions made with regard to the processing time distributions and the scheduler's knowledge of the processing times. Three dynamic jobshop scheduling problems (including a two station version of Conway et al.'s [2] nine station symmetric shop) are tested under seven different scenarios, one deterministic and six stochastic, using computer simulation. The deterministic scenario, where the processing times are exponential and observed by the scheduler, has been considered in many simulation studies, including Conway et al's. The six stochastic scenarios include the case where the processing times are exponential and only the mean is known to the scheduler, and five different cases where the machines are subject to unpredictable failures. Two policies were tested, the shortest expected processing time (SEPT) rule, and a rule derived from a Brownian analysis of the corresponding queueing network scheduling problem. Although the SEPT rule performed well in the deterministic scenario, it was easily outperformed by the Brownian policies in the six stochastic scenarios for all three problems. Thus, the results from simulation studies of dynamic, deterministic job-shop scheduling problems do not necessarily carry over to the more realistic setting where there is unpredictable variability present. |
| first_indexed | 2024-09-23T16:43:27Z |
| format | Working Paper |
| id | mit-1721.1/5388 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:43:27Z |
| publishDate | 2004 |
| publisher | Massachusetts Institute of Technology, Operations Research Center |
| record_format | dspace |
| spelling | mit-1721.1/53882019-04-10T10:36:19Z The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling Wein, Lawrence M. Ou, Jihong The goal of this paper is to determine if the results for dynamic job-shop scheduling problems are affected by the assumptions made with regard to the processing time distributions and the scheduler's knowledge of the processing times. Three dynamic jobshop scheduling problems (including a two station version of Conway et al.'s [2] nine station symmetric shop) are tested under seven different scenarios, one deterministic and six stochastic, using computer simulation. The deterministic scenario, where the processing times are exponential and observed by the scheduler, has been considered in many simulation studies, including Conway et al's. The six stochastic scenarios include the case where the processing times are exponential and only the mean is known to the scheduler, and five different cases where the machines are subject to unpredictable failures. Two policies were tested, the shortest expected processing time (SEPT) rule, and a rule derived from a Brownian analysis of the corresponding queueing network scheduling problem. Although the SEPT rule performed well in the deterministic scenario, it was easily outperformed by the Brownian policies in the six stochastic scenarios for all three problems. Thus, the results from simulation studies of dynamic, deterministic job-shop scheduling problems do not necessarily carry over to the more realistic setting where there is unpredictable variability present. 2004-05-28T19:37:07Z 2004-05-28T19:37:07Z 1989-11 Working Paper http://hdl.handle.net/1721.1/5388 en_US Operations Research Center Working Paper;OR 205-89 1744 bytes 1645914 bytes application/pdf application/pdf Massachusetts Institute of Technology, Operations Research Center |
| spellingShingle | Wein, Lawrence M. Ou, Jihong The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling |
| title | The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling |
| title_full | The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling |
| title_fullStr | The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling |
| title_full_unstemmed | The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling |
| title_short | The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling |
| title_sort | impact of processing time knowledge on dynamic job shop scheduling |
| url | http://hdl.handle.net/1721.1/5388 |
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