Mixed Model Production Lines

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• Mixed Model Production Lines
• C.S.Kanagaraj
• ( Kana Garage )
• IEM 5303

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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

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Overview (2)

• Problem Statement
• Automated Sequencing System (ASS)
• Steps followed in sequencing
• Conclusion

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Introduction

• 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

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Production Planning Techniques

• 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.

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Evolution of Production Planning Techniques

• 70s - 80s Optimization Era
• 80s Heuristic Era
• late 80s Artificial Intelligence Era
• - Expert Systems
• - Neural Networks
• - Genetic Algorithms
• - Autonomous Agent
  Architectures
• 90s 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

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Synoptic Table of Scheduling Techniques
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Limitations of these techniques

• 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

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Limitations of these techniques (2)

• 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

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Emerging Technique

• 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

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Graph - Production Planning Techniques

• Comparisons - High and Low Dynamics 1

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Problem Statement

• 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.

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Problem Statement (2)

• 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

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Automated Sequencing System (ASS)

• 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.

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Steps followed for sequencing

• 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.

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Conclusion

• 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