An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type-II

In this study, we consider the assembly line worker assignment and balancing problem of type-II (ALWABP-2). ALWABP-2 arises when task times differ depending on operator skills and concerns with the assignment of tasks and operators to stations in order to minimize the cycle time. We developed an ite...

Full description

Saved in:
Bibliographic Details
Published in:Computers & operations research 2013-01, Vol.40 (1), p.418-426
Main Authors: Mutlu, Özcan, Polat, Olcay, Supciller, Aliye Ayca
Format: Article
Language:English
Subjects:
Applied sciences
Assembly lines
Benchmarking
Exact sciences and technology
Genetic algorithm
Genetic algorithms
Heuristic
Inventory control, production control. Distribution
Iterated local search
Iterative methods
Line balancing
Modified bisection search
Operational research and scientific management
Operational research. Management science
Operators
Optimization algorithms
Searching
Studies
Tasks
Worker assignment
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, we consider the assembly line worker assignment and balancing problem of type-II (ALWABP-2). ALWABP-2 arises when task times differ depending on operator skills and concerns with the assignment of tasks and operators to stations in order to minimize the cycle time. We developed an iterative genetic algorithm (IGA) to solve this problem. In the IGA, three search approaches are adopted in order to obtain search diversity and efficiency: modified bisection search, genetic algorithm and iterated local search. When designing the IGA, all the parameters such as construction heuristics, genetic operators and local search operators are adapted specifically to the ALWABP-2. The performance of the proposed IGA is compared with heuristic and metaheuristic approaches on benchmark problem instances. Experimental results show that the proposed IGA is very effective and robust for a large set of benchmark problems.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2012.07.010