Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm
Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm
复制标题
使用 NSGA-II 算法进行基于预测的石油采购和分配多目标优化
DOI：
10.1142/s0219622016500097
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发表时间：
2016-03
期刊：
影响因子：
--
通讯作者：
Tang Ling
中科院分区：
文献类型：
--
作者：
Yu Lean;Yang Zebin;Tang Ling
针对石油市场的不确定性，提出了一种新的基于价格和需求预测的石油采购和配送优化方法，基于预测的石油购销优化模型。特别地，所提出的方法通过引入最近提出的信息技术（即，极端学习机（ELM））到石油采购和分销优化模型。该模型包括两个主要步骤：市场预测和计划优化。在市场预测中，ELM技术用于提供快速的训练时间和对石油价格和需求的准确预测结果。在计划优化中，考虑总利润最大化和库存风险最小化两个目标，并采用最流行的多目标进化算法（MOEA）--非支配排序遗传算法II（NSGA-II）搜索近似Pareto最优解。为了说明和验证，在美国的车用汽油市场的重点作为研究样本，实验结果表明，所提出的基于预测的优化方法优于其基准模型（没有市场预测和/或规划优化），在最高的利润和最低的风险。
Due to the uncertainty in oil markets, this paper proposes a novel approach for oil purchasing and distribution optimization by incorporating price and demand prediction, i.e., the prediction-based oil purchasing-and-distribution optimization model. In particular, the proposed method bridges the latest information technology and decision-making technique by introducing the recently proposed information technology (i.e., extreme learning machine (ELM)) into the oil purchasing-and-distribution optimization model. Two main steps are involved: market prediction and planning optimization in the proposed model. In market prediction, the ELM technique is employed to provide fast training time and accurate forecasting results for oil prices and demands. In planning optimization, two objectives of general profit maximization and inventory risk minimization are considered; and the most popular multi-objective evolutionary algorithm (MOEA), nondominated sorting genetic algorithm II (NSGA-II), is implemented to search approximate Pareto optimal solutions. For illustration and verification, the motor gasoline market in the US is focused on as the study sample, and the experimental results demonstrate the superiority of the proposed prediction-based optimization approach over its benchmark models (without market prediction and/or planning optimization), in terms of the highest profit and the lowest risk.
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