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Research on Petrol Secondary Distribution with Stochastic Demand
Science Journal of Energy Engineering
Volume 4, Issue 4, August 2016, Pages: 30-34
Received: Oct. 10, 2016; Accepted: Oct. 20, 2016; Published: Nov. 14, 2016
Authors
Pan Cen, Information Institute, Beijing Wuzi University, Beijing, China
Can Cong, Information Institute, Beijing Wuzi University, Beijing, China
Zhenping Li, Information Institute, Beijing Wuzi University, Beijing, China
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Abstract
Petrol is a kind of strategic natural resources. Providing legitimate transportation plans for the petrol secondary distribution is the key links to guarantee the petrol normal sales. The aim of this paper mainly is to obtain a distribution plan to meet certain level of service considering stochastic demand circumstances. Factors including the sales volume, the initial inventory, different type vehicles and capacity limitation constraints are considered. Firstly, a mathematical model for minimizing the total cost of petrol secondary distribution is built on the premise of considering various factors. Then a hybrid algorithm is designed by combining the greedy algorithm and the saving algorithm. The greedy algorithm is used to find a local optimal solution, and the saving algorithm is used to adjust the solution. Finally, the hybrid algorithm is used to solve a specific cases, which verifying the feasibility of the algorithm.
Keywords
Petrol Secondary Distribution, Mathematical Model, The Greedy Algorithm, The Saving Algorithm
Pan Cen, Can Cong, Zhenping Li, Research on Petrol Secondary Distribution with Stochastic Demand, Science Journal of Energy Engineering. Vol. 4, No. 4, 2016, pp. 30-34. doi: 10.11648/j.sjee.20160404.11
References
[1]
Li Z., Jiang C. (2015) Study on the Transportation Problem of Petrol Secondary Distribution with Considering Shortage Cost. Open Journal of Modelling and Simulation, 4: 34-40.
[2]
Li Q. Li S.(2008) A Novel Logistic Distribution Model of Refined Oil Based on Vendor or Managed Inventory. Journal of China University of Petroleum.
[3]
Xu H. (2008) Research on The Optimization of Oil Field Transportation Vehicle Scheduling Based on Genetic Algorithm.
[4]
Cong J. (2009) Research and Implementation of Oil Logistics Optimization. Xi'an University of Technology.
[5]
Ma Y. (2010) Dispatching Optimization Model of Second Distribution of Gasolin & Diesel Oil and Solution Based on Genetic Algorithm. Operations Research and Management Science, 19(06): 73-78.
[6]
Bard J. F., Nananukul N. (2010) A Branch and Price Algorithm for An Integrated Producation and Dinventory Routing Problem [J]. Computers & Operations Research, 37(22):02-17.
[7]
Dong W., Young H., Tae Y., Mitsuo G. (2014) An adaptive genetic algorithm for the time dependent inventoryrouting problem [J], Journal of Intelligent Manufacturing, 25:1025–1042.
[8]
Adelman Daniel Adelman. (2004) A Price-Directed Approach to Stochastic Inventory/Routing. Operation Reseach, 52:499-514.
[9]
Mohd Kamarul Irwan Abdul Rahim,. Yi Q. (2014) Modelling and Solving The Multi-period Inventory Routing Problem With Stochastic Stationary Demand Rates [J]. International Journal of Production Research, 52(43)51-63.
[10]
Zhao D. (2012) Research on Inventory Routing Problem With Stochastic Demand. Southwest Jiaotong University, 88-98.
[11]
Qureshi A. G,. Taniguchi E, Yamada T. (2009) An Exact Solution Approach for Vehicle Routing and Scheduling Problem With Soft Time Windows. Transportation Research Part E, 45:960-977.
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