A Case Study in Line Balancing and Simulation
Author(s) -
I. Ozan Yilmazlar,
Adarsh Jeyes,
Alexis Fiore,
Apurva Patel,
Chelsea Spence,
Chase Wentzky,
Nicole Zero,
Mary E. Kurz,
Joshua D. Summers,
Kevin Taaffe
Publication year - 2020
Publication title -
procedia manufacturing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.504
H-Index - 43
ISSN - 2351-9789
DOI - 10.1016/j.promfg.2020.05.076
Subject(s) - automotive industry , randomness , task (project management) , production line , computer science , assembly line , line (geometry) , metaheuristic , production (economics) , work (physics) , quality (philosophy) , integer programming , mathematical optimization , industrial engineering , operations research , simulation , engineering , algorithm , systems engineering , mathematics , geometry , mechanical engineering , philosophy , statistics , epistemology , economics , macroeconomics , aerospace engineering
Assembly line balancing (ALB) allocates individual tasks to work stations while respecting the physical, safety, and quality constraints. Two-sided assembly lines are generally used in the production of medium or large-sized products (e.g. automotive, household appliance). We considered several characteristics including zoning constraints, the task to task relationships, tooling and station dependent constraints to offer the real-world environment. The most common two objectives for the ALB are minimizing the number of workers (type-1) and minimizing the cycle time (type-2). This article presents an integer programming formulation for both type-1 and type-2 ALB problems and metaheuristics to solve this complex problem. Even if ALB gives better results than the current line balance that our industry partner applied, it cannot be guaranteed that the amount of daily production will increase due to randomness in the line. We simulate the proposed line balances to provide a testing platform for line balancing results and to help identify inefficiencies and bottlenecks in the system.
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