Production control of unreliable manufacturing systems with perishable inventory

2021 ◽  
Author(s):  
Vladmir Polotski ◽  
Ali Gharbi ◽  
Jean-Pierre Kenne
Keyword(s):  
Manufacturing Systems ◽  
Production Control ◽  
Perishable Inventory

scholarly journals Queue Length Forecasting in Complex Manufacturing Job Shops

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 322-338
Author(s):  
Marvin Carl May ◽  
Alexander Albers ◽  
Marc David Fischer ◽  
Florian Mayerhofer ◽  
Louis Schäfer ◽  
...  
Keyword(s):  
Operations Management ◽  
Queue Length ◽  
Manufacturing Systems ◽  
Production Control ◽  
Data Sets ◽  
Job Shops ◽  
Static Data ◽  
Queue Lengths ◽  
Operational Excellence

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

MINIMUM AVERAGE COST PRODUCTION PLANNING IN STOCHASTIC MANUFACTURING SYSTEMS

1998 ◽  
Vol 08 (07) ◽  
pp. 1251-1276 ◽  
Author(s):  
SURESH P. SETHI ◽  
HANQIN ZHANG ◽  
QING ZHANG
Keyword(s):  
Control Problem ◽  
Production Planning ◽  
Average Cost ◽  
Production Cost ◽  
Manufacturing Systems ◽  
Production Control ◽  
Production Capacity ◽  
Production Rates ◽  
Planning Problems

Recently, the production control problem in stochastic manufacturing systems has generated a great deal of interest. The goal is to obtain production rates to minimize total expected surplus and production cost. This paper reviews the research devoted to minimum average cost production planning problems in stochastic manufacturing systems. Manufacturing systems involve a single or parallel failure-prone machines producing a number of different products, random production capacity, and constant demands.

Perturbation analysis for production control and optimization of manufacturing systems

Automatica ◽  
2004 ◽  
Vol 40 (6) ◽  
pp. 945-956 ◽  
Author(s):  
Haining Yu ◽  
Christos G. Cassandras
Keyword(s):  
Perturbation Analysis ◽  
Manufacturing Systems ◽  
Production Control

A semi-heterarchical production control architecture for industry 4.0-based manufacturing systems

2020 ◽  
Vol 24 ◽  
pp. 43-46 ◽  
Author(s):  
Andrea Grassi ◽  
Guido Guizzi ◽  
Liberatina Carmela Santillo ◽  
Silvestro Vespoli
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Production Control ◽  
Control Architecture

Simulation Analysis of Production Control Methods in Manufacturing Systems

2012 ◽  
Vol 490-495 ◽  
pp. 1704-1708
Author(s):  
Shao Wei Feng ◽  
Jing Zhang ◽  
Shao Chun Ding
Keyword(s):  
Simulation Model ◽  
Manufacturing Systems ◽  
Production Control ◽  
Simulation Analysis ◽  
Production Performance ◽  
Buffer Size ◽  
Total System ◽  
Manufacturing Management ◽  
Performance Indexes ◽  
System Output

It is very important to improve shop production performance in manufacturing process. The main manufacturing management methods include Kanban and Drum Buffer Rope (DBR) systems. In this paper computer simulation is used to evaluate the performance of these manufacturing systems. A simulation model was developed to collect and analyze some key performance indexes including total system output, flow time and average WIP invention. The optimal buffer size was found out by studying the two manufacturing systems at different capacities. The systems were also compared with and without machine breakdowns. The simulation model provided a significant insight into the two systems and the benefits of both the systems were realized

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