A survey of exact algorithms for the simple assembly line balancing problem
Management Science
Management Science
Principles of Dynamic Programming: Basic Analytical and Computational Methods
Principles of Dynamic Programming: Basic Analytical and Computational Methods
Improved genetic algorithm for the permutation flowshop scheduling problem
Computers and Operations Research
A Constructive Genetic Algorithm for permutation flowshop scheduling
Computers and Industrial Engineering
Assembly line balancing as generalized bin packing
Operations Research Letters
On methods for generating random partial orders
Operations Research Letters
An improved hybrid particle swarm optimization algorithm for fuzzy p-hub center problem
Computers and Industrial Engineering
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In this paper the setup assembly line balancing and scheduling problem (SUALBSP) is considered. Since this problem is NP-hard, a hybrid genetic algorithm (GA) is proposed to solve the problem. This problem involves assigning the tasks to the stations and scheduling them inside each station. A simple permutation is used to determine the sequence of tasks. To determine the assignment of tasks to stations, the algorithm is hybridized using a dynamic programming procedure. Using dynamic programming, at any time a chromosome can be converted to an optimal solution (subject to the chromosome sequence). Since population diversity is very important to prevent from being trapped in local optimum solutions some diversity maintaining schemes are used to overcome this issue. Operators and parameters of the algorithm is calibrated using design of experiments (DOEs) method. The computational results show that the proposed GA outperforms all of the algorithms presented to solve SUALBSP so far.