Abstract
To solve the logistics terminal distribution problem, we construct the multi-objective location allocation model of pick-up point. The model considers the characteristics of express logistics system, enterprise demand and customer demand for delivery distance, and two objectives of minimizing location cost and maximizing customer distance satisfaction are included. We set the segmentation distance function according to the distance of the user from the pick-up point, and calculate the satisfaction function. To solve the model, we modify the non-dominated sorting genetic algorithm with elite strategy algorithm (NSGA-II). Consider the needs of the enterprise or user, we modify the crossover operator, and use the tournament method to evaluate the offspring population generated by genetic operations to reduce the loss of elite individuals. We also verify the effectiveness of the model and the modified algorithm through experiments. Experimental results show that the modified algorithm can obtain a better Pareto optimal solution set for the multi-objective location-allocation problem.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant No. 61976242, in part by the Opening Project of Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University under Grant No. 2018002, in part by the Open Fund of Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education under Grant No. IPIU2019003, and in part by the State Key Program of National Natural Science of China under Grant No. 61836009.
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Chen, S., Cao, B., Li, R. (2020). Multi-objective Pick-up Point Location Optimization Based on a Modified Genetic Algorithm. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_60
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DOI: https://doi.org/10.1007/978-981-15-3425-6_60
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