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Frontiers of Mechanical Engineering >> 2014, Volume 9, Issue 3 doi: 10.1007/s11465-014-0294-x

Pareto lexicographic α-robust approach and its application in robust multi objective assembly line balancing problem

1. HUST–SANY Joint Laboratory of Advanced Manufacturing Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

2. Department of Industrial Engineering, University of Engineering and Technology, Taxila, Pakistan

Available online:2014-10-10

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Robustness in most of the literature is associated with min-max or min-max regret criteria. However, these criteria of robustness are conservative and therefore recently new criteria called, lexicographic α-robust method has been introduced in literature which defines the robust solution as a set of solutions whose quality or jth largest cost is not worse than the best possible jth largest cost in all scenarios. These criteria might be significant for robust optimization of single objective optimization problems. However, in real optimization problems, two or more than two conflicting objectives are desired to optimize concurrently and solution of multi objective optimization problems exists in the form of a set of solutions called Pareto solutions and from these solutions it might be difficult to decide which Pareto solution can satisfy min-max, min-max regret or lexicographic α-robust criteria by considering multiple objectives simultaneously. Therefore, lexicographic α-robust method which is a recently introduced method in literature is extended in the current research for Pareto solutions. The proposed method called Pareto lexicographic α-robust approach can define Pareto lexicographic α-robust solutions from different scenarios by considering multiple objectives simultaneously. A simple example and an application of the proposed method on a simple problem of multi objective optimization of simple assembly line balancing problem with task time uncertainty is presented to get their robust solutions. The presented method can be significant to implement on different multi objective robust optimization problems containing uncertainty.

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