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# Mixed Model Production LinesC.S.Kanagaraj Kana Garage - PowerPoint PPT Presentation

Mixed Model Production Lines C.S.Kanagaraj ( Kana + Garage ) IEM 5303. Overview. Mixed Model Production Lines (MMPL) Production Planning Techniques Evolution of Production Planning Techniques Emergence of a new technique Comparisons - Based on the dynamic environment. Overview (2).

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C.S.Kanagaraj

( Kana + Garage )

IEM 5303

2000 C.S.Kanagaraj

• Mixed Model Production Lines (MMPL)

• Production Planning Techniques

• Evolution of Production Planning Techniques

• Emergence of a new technique

• Comparisons - Based on the dynamic environment

2000 C.S.Kanagaraj

• Problem Statement

• Automated Sequencing System (ASS)

• Steps followed in sequencing

• Conclusion

2000 C.S.Kanagaraj

• Mixed Model Production Line (MMPL)

• Production lines capable of making several different parts for a given period of time are called as the mixed model production lines.

• Example: An automobile assembly line

• Mixed Model Production Lines use various production planning techniques to enhance their production capabilities

2000 C.S.Kanagaraj

• What are production planning techniques

• These are a set techniques involving mathematical equations and algorithms, which are to used to help us effectively in scheduling, sequencing, batching, etc. in the manufacturing industry.

• Example: Genetic Algorithms.

2000 C.S.Kanagaraj

• 70’s - 80’s Optimization Era

• 80’s Heuristic Era

• late 80’s Artificial Intelligence Era

• - Expert Systems

• - Neural Networks

• - Genetic Algorithms

• - Autonomous Agent Architectures

• 90’s Interactive Schedulers Era [1]

_____________________________________

[1] Maria Caridi and Andrea Sianesi “Multi-agent systems in production planning and control: An application to the scheduling of mixed-model assembly lines”, International Journal of Production Economics, Volume 68, Issue 1, 30 October 2000, Pages 29-42

2000 C.S.Kanagaraj

2000 C.S.Kanagaraj

• Optimization Era

• Differences in mono and multi objective approaches

• long time to reach full automation

• less product differentiation

• Heuristic Era

• More static than being dynamic

• It seems quite impossible to codify the system reactions to possible failures and events

[1]

2000 C.S.Kanagaraj

• Artificial Intelligence Era

• Seems to be natural but complex

• Expert System

• Neural Networks

• Genetic Algorithms

• complexity of the software

• difficulty of knowledge codifying

• Autonomous Agent Architectures

• very few are in use at present

• its efficiency and effectiveness are still hard to evaluate

[1]

2000 C.S.Kanagaraj

• Interactive Schedulers Era

• They are the simplest kind of scheduling system because the plan is not made by a machine but the planner himself, while the system checks for the feasibility of the decision makers choices.

• Schedulers are the ones most commonly used instruments in real world applications.

[1]

2000 C.S.Kanagaraj

Comparisons - High and Low Dynamics [1]

2000 C.S.Kanagaraj

• A production line of a sheet metal manufacturing facility is taken into consideration.

• The problem concerning the production line is studied. And the area is identified in a transfer area between the press floor and the surface treatment zone.

• The parts are sequenced manually before moving into the next process.

2000 C.S.Kanagaraj

• To accomplish this task, a basic design and an algorithm were borrowed from Choi Wonjoon and Shin Hyunoh [2] and the model was modified to provide a solution to the above manufacturing facility.

• The algorithm developed by Choi Wonjoon and Shin Hyunoh [2] suported a dynamic environment.

_____________________________________

[2] Choi Wonjoon and Shin Hyunoh “A Real-Time Sequence Control System for the Level Production of the Automobile Assembly Line”, Computers & Industrial Engineering, Volume 33, Issues 3-4, December 1997, Pages 769-772

2000 C.S.Kanagaraj

• An automated sequencing system is developed similar to a PBS system used in by Choi Wonjoon and Shin Hyunoh [2].

• The automated sequencing system, sequences the parts according some constraints, like demand and overall time taken by the part for getting processed.

• The main aim of the system is to make use of the production line intelligently and efficiently.

2000 C.S.Kanagaraj

1) Selecting the production/feeder line

2) Assigning priorities to the parts according to the demand

3) Selecting the part with the least processing time to process next

4) Inserting the selected part to the priority queue

A set of algorithms use by Choi Wonjoon and Shin Hyunoh [2] are used to perform the sequencing task.

2000 C.S.Kanagaraj

• Mixed model production lines have become a universal standard in many manufacturing facilities. And therefore when a dynamic sequencing system is used, the production line becomes highly productive and efficient.

~ * ~ Best of luck! Guys ~ * ~

2000 C.S.Kanagaraj