Supply chain management and metaheuristic algorithms: Analysing a new hybrid genetic crossover operator

Teodoro, Felipe Gustavo Silva; Costa, Daniel; Peres, Sarajane Marques; Lima, Clodoaldo A. M.

doi:10.1109/la-cci.2015.7435981

Cited by 1 publication

(1 citation statement)

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“…This modified hybrid genetic algorithm (mhGA) provides an improved search-ability of best solution and the convergence speed. Teodoro et al [46] proposed a new hybrid operator for genetic algorithm used to optimize the purchase of products in stores geographically separated in order to obtain solutions that combine the purchase of products with the lowest possible total cost, considering the route between each store. They described the development of a strategy based on GA, emphasizing the proposition of a new hybrid genetic crossover operator, to provide a set of optimized solutions for a SCM specific problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

Aravendan

Panneerselvam

2015

IIM

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This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor "algorithm", the best algorithm is identified using Duncan's multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

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Section: Literature Reviewmentioning

confidence: 99%