scholarly journals Comparison and evaluation of job selection dispatching rules for integrated scheduling of multi-load automatic guided vehicles serving in variable sized flexible manufacturing system layouts: A simulation study

2018 ◽  
pp. 187-200 ◽  
Author(s):  
S. Angra ◽  
A. K. Chanda ◽  
V. K. Chawla
Keyword(s):  
Simulation Study ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Dispatching Rules ◽  
Automatic Guided Vehicles ◽  
Integrated Scheduling ◽  
Job Selection

A Simulation Study of Flexible Manufacturing System Using Dynamic Scheduling Approach

2014 ◽  
Author(s):  
Vijay Kumar Mahalingam
Keyword(s):  
Simulation Study ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Dynamic Scheduling ◽  
Control Strategies ◽  
Dispatching Rules ◽  
Average Delay ◽  
Alternative Routings ◽  
Machine Utilization

This paper presents a simulation study aimed at evaluating the performances of a typical Flexible manufacturing system (FMS) problems in terms of make span, average flow time, average delay time at local buffers and average machine utilization, subject to different control strategies which include routing flexibilities and dispatching rules. The routing strategies under evaluation are ‘No Alternative Routings (NAR)’; ‘Alternative Routings Dynamic (ARD)’; and ‘Alternative Routings Planned (ARP)’. The ARP rule was introduced into the FMS and coded with C++ program. The above routing strategies are combined with five dispatching rules and studied in different production volumes. Since an FMS usually deals with a variety of products, effects of changing the part mix ratio are also discussed. Simulation study was performed by using ARENA software. Finally results indicate that the ‘alternative routings planned’ strategy outperforms other routing strategies in general.

Intelligent AGV Control of Autonomous Decentralized FMS by Oblivion and Memory

2010 ◽  
Vol 447-448 ◽  
pp. 326-330 ◽  
Author(s):  
Hidehiko Yamamoto ◽  
Takayoshi Yamada
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Automatic Guided Vehicles ◽  
Reasoning System ◽  
Priority Ranking ◽  
The Future ◽  
Competing Hypotheses ◽  
A Priority ◽  
Production Conditions

This paper describes a method to control Autonomous decentralized Flexible Manufacturing System (AD-FMS) by using a memory to determine a priority ranking for competing hypotheses. The aim is to increase the reasoning efficiency of a system the author calls reasoning to anticipate the future (RAF) which controls automatic guided vehicles (AGVs) in AD-FMSs. The system includes memory data of past production conditions and AGV actions. The system was applied to an AD-FMS that was constructed on a computer. The results showed that, compared with conventional reasoning, this reasoning system reduced the number of hypothesis replacements until a true hypothesis was reached.　

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