The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Stochastic Optimization of Manufacture Systems by Using Markov Decision Processes
Abstract
In real-world most of manufacturing systems are large, complex, and subject to uncertainty. This is mainly due to events as random demands, breakdowns, repairs of production machines, setup and cycle times, inventory fluctuations and more. If items move too quickly, workers may work too hard. If items move too slowly, workers may have great leisure times. However, must make decisions here and now regarding the operation of the system optimally and quickly. In practice, these decisions are based on recent statistics of the system behavior, in the experience of the analyst and the urgency of the solution. In this chapter, we present a real problem associated with the production of individual parts in metalworking industry for the refrigerators production. We develop a model based on the Markov Decision Process to study the dynamics of the trajectory of end products in a manufacturing line that works by process. Then, we propose a measure of the average production rate of the line by using the Monte Carlo method. We illustrate our proposal using a numerical example with real data obtained in situ.
Related Content
|
Pawan Kumar, Mukul Bhatnagar, Sanjay Taneja.
© 2024.
26 pages.
|
|
Kapil Kumar Aggarwal, Atul Sharma, Rumit Kaur, Girish Lakhera.
© 2024.
19 pages.
|
|
Mohammad Kashif, Puneet Kumar, Sachin Ghai, Satish Kumar.
© 2024.
15 pages.
|
|
Manjit Kour.
© 2024.
13 pages.
|
|
Sanjay Taneja, Reepu.
© 2024.
19 pages.
|
|
Jaspreet Kaur, Ercan Ozen.
© 2024.
28 pages.
|
|
Hayet Kaddachi, Naceur Benzina.
© 2024.
25 pages.
|
|
|