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Solving the manufacturing cell design problem using the modified binary firefly algorithm and the egyptian vulture optimisation algorithm

机译：使用改进的二进制萤火虫算法和埃及秃v优化算法解决制造单元设计问题

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摘要

The manufacturing cell design problem (MCDP) aims to minimise the movements of parts between the production cells. The MCDP is an NP-Hard optimisation problem with a binary domain. For the resolution of the MCDP, the authors employ the firefly algorithm (FA) metaheuristic. FA is a metaheuristic with a real domain; therefore, an efficient method for transfer and discretisation from the real domain to the binary domain has been used. The second metaheuristic used is Egyptian vulture optimisation algorithm (EVOA). EVOA is a recent metaheuristic inspired by the behaviour of the Egyptian vulture bird. EVOA uses a set of operators which must be adapted to the MCDP optimisation problem. Two types of experiments have been performed. The first experiment consists of solving the MCDP with a set of 90 homogeneous incidence matrices. In the tests, FA and EVOA have been used obtaining good results. Subsequently, the obtained results have been compared versus other eight metaheuristics. The second experiment consists in a set of 35 inhomogeneous incidence matrices. The global optimum value for 13 problems has been obtained using constraint programming. Finally, for the other 22 problems, the authors have reported the best values found using FA and EVOA.
机译：制造单元设计问题（MCDP）旨在最大程度地减少零件在生产单元之间的移动。 MCDP是具有二进制域的NP-Hard优化问题。为了解决MCDP，作者采用了萤火虫算法（FA）元启发式。 FA是具有实际领域的元启发式方法；因此，已经使用了从实域到二进制域的转移和离散化的有效方法。第二种启发式算法是埃及秃鹰优化算法（EVOA）。 EVOA是最近受埃及秃鹰行为启发的元启发法。 EVOA使用一组必须适应MCDP优化问题的运算符。已经进行了两种类型的实验。第一个实验包括用一组90个齐次入射矩阵求解MCDP。在测试中，使用FA和EVOA获得了良好的结果。随后，将获得的结果与其他八种启发式方法进行了比较。第二个实验包含一组35个非均匀入射矩阵。使用约束编程已获得13个问题的全局最优值。最后，对于其他22个问题，作者报告了使用FA和EVOA发现的最佳值。

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