Optimally balancing large assembly lines with `FABLE'
Management Science
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Exact and approximation algorithms for makespan minimization on unrelated parallel machines
Discrete Applied Mathematics
Computers and Operations Research
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Lagrangian Relaxation Method for Solving Integer Programming Problems
Management Science
Assignment Problems
An efficient approach for type II robotic assembly line balancing problems
Computers and Industrial Engineering
A branch, bound, and remember algorithm for the 1|ri|Σti scheduling problem
Journal of Scheduling
Scheduling Unrelated Parallel Machines- Algorithms, Complexity, and Performance
Scheduling Unrelated Parallel Machines- Algorithms, Complexity, and Performance
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
On solving the assembly line worker assignment and balancing problem via beam search
Computers and Operations Research
Simple heuristics for the assembly line worker assignment and balancing problem
Journal of Heuristics
A Branch, Bound, and Remember Algorithm for the Simple Assembly Line Balancing Problem
INFORMS Journal on Computing
Computers and Operations Research
Computers and Operations Research
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In this paper, we studied the assembly line worker assignment and balancing problem, which is an extension of the classical assembly line balancing problem in which an optimal partition of the assembly work among the stations is sought along with the assignment of the operators to the stations. The relationship between this problem and several other well-studied problems is explored, and new lower bounds are derived. Additionally, an exact enumeration algorithm, which makes use of the lower bounds, is developed to solve the problem. The algorithm is tested by using a standard benchmark set of instances. The results show that the algorithm improves upon the best-performing methods from the literature in terms of solution quality, and verifies more optimal solutions than the other available exact methods.