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Manuel ChiSe
Manuel ChiSe

The document presents a multi-objective ant colony algorithm called MACS to solve the 1/3 variant of the Time and Space Assembly Line Balancing Problem (TSALBP). MACS minimizes the number of stations and total station area given a fixed cycle time. It was tested on four problem instances and compared to random search and single-objective ACS algorithms. MACS with a 0.2 parameter performed best in converging to optimal Pareto fronts with more diversity. Current work involves improving MACS with multi-colony techniques and incorporating preferences. Future work includes local search and multi-objective genetic algorithms.

Technology
1 of 23
A Multi-objective Ant Colony Algorithm for the 1/3 Variant of the Time and Space Assembly Line Balancing Problem Manuel Chica, Óscar Cordón, Sergio Damas Joaquín Bautista
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object]
SALBP and TSALBP (1) ,[object Object],[object Object]
SALBP and TSALBP (2) ,[object Object],[object Object]
SALBP and TSALBP (3) ,[object Object],There are 4 TSALBP multi-objective variants . We have selected the 1/3 variant , which minimises the number of stations, m , and their area, A , given a fixed cycle time limit. We made this decision because it is the most realistic variant in the automotive industry.
The Multiple Ant Colony System (MACS) (1) ,[object Object],[object Object],λ weights the importance of the heuristic information of each ant, and β weights the heuristic information with respect to the pheromone trail
The Multiple Ant Colony System (MACS) (2) ,[object Object],[object Object],[object Object],Average of the non-dominated solutions
Our MACS-based approach (1) ,[object Object],[object Object],task a task c task e task d task b task f task g task h station 1 station 2 station 3 A = 20 C = 12 A = 16 C = 11 A = 24 C = 12
Our MACS-based approach (2) ,[object Object],[object Object],The first one is related with the required time of the task and the number of successors: The second one related with the required area of the task and the number of successors: Tasks having a large value of time and area and a high number of successors, are preferred to be allocated first.
Our MACS-based approach (3) ,[object Object],[object Object]
Experiments (1) ,[object Object],[object Object]
Experiments (2) ,[object Object],[object Object],[object Object],[object Object]
Experiments (3) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experiments (4) ,[object Object],[object Object]
Experiments (5) ,[object Object],[object Object]
Current work (1) ,[object Object],[object Object],station 3 A m station 1 m A station 2 m A 0.2 0.5 0.9
Current work (2) ,[object Object],[object Object],[object Object]
Current work (3) ,[object Object],[object Object]
Concluding remarks ,[object Object],[object Object]
Future work (1) ,[object Object],[object Object]
Future work (2) ,[object Object],[object Object]
THANKS!

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Slides TSALBP ACO 2008

  • 1. A Multi-objective Ant Colony Algorithm for the 1/3 Variant of the Time and Space Assembly Line Balancing Problem Manuel Chica, Óscar Cordón, Sergio Damas Joaquín Bautista
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