A survey of exact algorithms for the simple assembly line balancing problem
Management Science
Identifying multiple solutions for assembly line balancing having stochastic task times
Computers and Industrial Engineering
Fast, effective algorithms for simple assembly line balancing problems
Operations Research
Genetic algorithms for assembly line balancing with various objectives
Computers and Industrial Engineering - Special issue: IE in Korea
Multi-criteria assembly sequencing
Computers and Industrial Engineering - Special issue: new advances in analysis of manufacturing systems
Computers and Industrial Engineering
2-ANTBAL: An ant colony optimisation algorithm for balancing two-sided assembly lines
Computers and Industrial Engineering
An efficient approach for type II robotic assembly line balancing problems
Computers and Industrial Engineering
Balancing of mixed-model two-sided assembly lines
Computers and Industrial Engineering
A new bidirectional heuristic for the assembly line balancing problem
Computers and Industrial Engineering
Assembly line balancing with station paralleling
Computers and Industrial Engineering
A MIP approach for balancing transfer line with complex industrial constraints
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
Assembly line balancing under uncertainty: Robust optimization models and exact solution method
Computers and Industrial Engineering
A meta-heuristic algorithm for the fuzzy assembly line balancing type-E problem
Computers and Operations Research
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This paper presents a type E simple assembly line balancing problem (SALBP-E) that combines models SALBP-1 and SALBP-2. Furthermore, this study develops a solution procedure for the proposed model. The proposed model provides a better understanding of management practice that optimizes assembly line efficiency while simultaneously minimizing total idle time. Computational results indicated that, under the given upper bound of cycle time (ct"m"a"x), the proposed model can solve problems optimally with minimal variables, constraints, and computing time.