Fuzzy Logic

Caggiano, Alessandra

doi:10.1007/978-3-642-20617-7_6555

Cited by 1 publication

(1 citation statement)

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“…Since data in real-world problems are often afflicted with uncertainty, imprecision, and vagueness due 2 Mathematical Problems in Engineering to both machine and human factors, they can only be estimated with uncertainty. Several researchers [25] have been attempted to incorporate fuzzy information in their effort to solve SALBP through various types of algorithms. Kalender et al [26] have been developed to solve traditional ALBP with fuzzy operation times.…”

Section: Introductionmentioning

confidence: 99%

Genetic-Based Optimization of the Manufacturing Process of a Robotic Arm under Fuzziness

Zacharia

Tsirkas

Kabouridis

et al. 2018

Mathematical Problems in Engineering

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Fuzziness is a key concern in modern industry and, thus, its implementation in manufacturing process modeling is of high practical importance for a wide industrial audience. The scientific contribution of the present attempt lies on the fact that the assembly line balancing problem of type 2 (SALBP-2) is approached for a real manufacturing process by introducing fuzzy processing times. The main scope of this work is the solution of the SALBP-2, which is an NP-hard problem, for a real manufacturing process considering fuzziness in the processing times. Since the data obtained from realistic situations are imprecise and uncertain, the consideration of fuzziness for the solution of SALBP-2 is of great interest. Thus, real data values for the processing times are gathered and estimated with uncertainty. Then, fuzzy processing times are used for finding the optimum cycle time. The optimization tool for the solution of the fuzzy SALBP-2 is a Genetic Algorithm (GA). The validity of the proposed approach is tested on the construction process of a metallic robotic arm. The experimental results demonstrate the effectiveness and efficiency of the proposed GA in determining the optimum sequence of the tasks assigned to workstations which provides the optimum fuzzy cycle time.

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Section: Introductionmentioning

confidence: 99%