Distributed preset reserve for multiple points with stochastic demands

YANG Ting-hong; JIANG Da-li; FANG Hai-yang; FANG Ling; LI Bin

doi:10.19818/j.cnki.1671-1637.2018.04.016

Volume 18 Issue 4

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YANG Ting-hong, JIANG Da-li, FANG Hai-yang, FANG Ling, LI Bin. Distributed preset reserve for multiple points with stochastic demands[J]. Journal of Traffic and Transportation Engineering, 2018, 18(4): 151-159. doi: 10.19818/j.cnki.1671-1637.2018.04.016

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YANG Ting-hong, JIANG Da-li, FANG Hai-yang, FANG Ling, LI Bin. Distributed preset reserve for multiple points with stochastic demands[J]. Journal of Traffic and Transportation Engineering, 2018, 18(4): 151-159. doi: 10.19818/j.cnki.1671-1637.2018.04.016

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Distributed preset reserve for multiple points with stochastic demands

doi: 10.19818/j.cnki.1671-1637.2018.04.016

1.
Department of Military Logistics, Army Logistics University, Chongqing 401311, China
2.
Department of Fundamental Studies, Army Logistics University, Chongqing 401311, China
3.
Department of Logistics Command, Army Logistics University, Chongqing 401311, China

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Author Bio:
YANG Ting-hong(1978-), male, associate professor, doctoral student, yth_ah@163.com

JIANG Da-li(1967-), male, professor, PhD, jiangdali99@163.com
Received Date: 2018-03-11
Publish Date: 2018-08-25

Abstract

Abstract

Considering the correlations of emergencies (possibility of chain reaction) among the important pass, passageway, and sensitive zone, and the randomness of demands in the key directions or areas, the optimization problems of preset reserve scale and reserve distribution about the reserve network supporting the demand network were studied. The reserve network consisted of multiple preset reserve warehouses, and the service demand network consisted of multiple stochastic demand points. The distribution characteristics of the total demand in the demand network were analyzed under the conditions that the emergency at each point and the demand quantity were random. Ensuring the effectiveness and economy of the preset reserve scale and the rationality of the reserve distribution, the reserve scale model under the given securityprobability and the distributed reserve model under the process response criterion (collectively called reserve model) were established. According to the characteristics of the model, the distributed reserve model was decomposed into a bi-level model. The sample processing method was used to resolve the random demand problem. On this basis, agenetic-simulated annealing algorithm was constructed to solve this model. Based on the concept of variation coefficient, a robustness index for reserve scheme with random emergency and random demand was proposed to analyze the robustness of the reserve model and its algorithm. The validities of the reserve model and its algorithm were verified by a case application. Research result shows that in comparison with the nearby partition principle, the reserve scale model and its algorithm can reduce the reserve scale by approximately 1/3 under the premise of ensuring the security probability. In comparison with the maximin criterion, the distributed reserve scheme under the process response criterion can reduce the response time of the first batch of materials by 11%, and the response time of 90% of materials reduces by 21%. When facing the stochastic fluctuation of emergency and demand in the demand network, the amplitude of fluctuation for the distributed reserve scheme is no more than 80% of the demand fluctuation, which suggests better robustness.
- logistics engineering,
- stochastic demand,
- distributed reserve,
- genetic-simulated annealing algorithm,
- robustness

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References

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Distributed preset reserve for multiple points with stochastic demands

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