Solving fuzzy assembly-line balancing problem with genetic algorithms
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Stability aspects of the traveling salesman problem based on k-best solutions
Discrete Applied Mathematics
Mathematical Programming: Series A and B
Multicriteria Optimization
Sensitivity analysis of the knapsack sharing problem: Perturbation of the weight of an item
Computers and Operations Research
Reduction approaches for a generalized line balancing problem
Computers and Operations Research
Stability of a schedule minimizing mean flow time
Mathematical and Computer Modelling: An International Journal
Assembly line balancing under uncertainty: Robust optimization models and exact solution method
Computers and Industrial Engineering
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A generalized formulation for assembly line balancing problem (GALBP) is considered, where several workplaces are associated with each workstation. Thus, all tasks assigned to the same workstation have to be partitioned into blocks: each block regroups all tasks to be performed at the same workplace. The product items visit all workplaces sequentially, therefore, all blocks are proceeded in a sequential way. However, the tasks grouped into the same block are executed simultaneously. As a consequence, the execution of a block takes only the time of its longest task. This parallel execution modifies the manner to take into account the cycle time constraint. Precedence and exclusion constraints also exist for workstations and their workplaces. The objective is to assign all given tasks to workstations and workplaces while minimizing the line cost estimated as a weighted sum of the number of workstations and workplaces. The goal of this article is to propose a stability measure for feasible and optimal solutions of this problem with regard to possible variations of the processing time of certain tasks. A heuristic procedure providing a compromise between the objective function and the suggested stability measure is developed and evaluated on benchmark data sets.