12. Population-based heuristic algorithms for buffer allocation in unreliable production lines
Invited abstract in session TD-2: Heuristic scheduling, stream Heuristic scheduling.
Thursday, 14:30 - 16:00Room: M228
Authors (first author is the speaker)
| 1. | Leyla Demir
|
| Department of Industrial Engineering, Izmir Bakırçay University | |
| 2. | Mehmet Ulaş KOYUNCUOĞLU
|
| Department of Management Information Systems, Pamukkale University |
Abstract
The Buffer Allocation Problem (BAP) is an NP-hard combinatorial optimization problem encountered in designing production lines. The problem is mainly classified as in three categories based on the objective function: total buffer size minimization known as primal problem, production rate maximization known as dual problem, and profit maximization problem. In this study, the BAP is solved for profit maximization in unreliable production lines. The problem is formulated under the total buffer size constraint, and two population-based heuristic algorithms, i.e., Combat Genetic Algorithm (CGA) and Big Bang Big Crunch (BBBC) algorithm, are employed to solve this problem. First, the preliminary tests are carried out to determine the optimal parameters for each algorithm, and then performances of the proposed algorithms are tested on existing benchmark problems, involving small, medium, and large-sized instances. The experimental study shows that the BBBC algorithm produces better results than the CGA for all the problems considered, and it also reaches the best solution known in the literature with less computational effort in comparison to the state-of-the-art algorithms.
Keywords
- Manufacturing
- Heuristics and meta-heuristics
- Combinatorial Optimization
Status: accepted
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