A comparative evaluation of heuristic line balancing techniques
Management Science
A survey of exact algorithms for the simple assembly line balancing problem
Management Science
Computers and Industrial Engineering
The OPL optimization programming language
The OPL optimization programming language
Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems
INFORMS Journal on Computing
Rule-Based Modeling of Assembly Constraints for Line Balancing
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
A mathematical model and a genetic algorithm for two-sided assembly line balancing
Computers and Operations Research
An efficient approach for type II robotic assembly line balancing problems
Computers and Industrial Engineering
A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The assembly line balancing problem employs traditional precedence graphs to model precedence relations among assembly tasks. Yet they cannot address alternative ways of assembling a product. That is, they only model conjunctions, not disjunctions. Moreover, some additional constraints need also to be considered, but these constraints cannot be modeled effectively through precedence graphs, e.g., constraints indicating certain tasks cannot be assigned into the same station. To address these issues, this paper proposes to model assembly constraints through the well known If-then rules, and to solve the rule-based model through constraint programming (CP), as CP naturally models logical assertions. The paper also shows how to map a rule-based model to a CP or an integer programming (IP) model. Finally, a computational experiment is carried out to analyze the performances of CP and IP models with respect to modeling capability, solution quality and time. The results reveal that CP is more effective and efficient than IP.