钢铁企业合同匹配多目标优化模型与算法

针对钢铁企业中存在的合同对库存余材的优化匹配问题,建立了实现余材利用量最大化和匹配损失费用最小化的多目标0-1规划模型·采用模糊决策方法处理两个目标函数,尝试基于群体的增量学习(Population BasedIncreasedLearning,简称PBIL)算法进行求解·结合模型的特点,利用自然数编码表示合同的匹配结果,按照学习概率大小修复不可行个体·通过对应用实例的计算,以及与遗传算法结果的比较,证明该模型和算法是解决合同优化匹配问题较为理想的方式·

Optimal Multi-Objective Model and Algorithm for Order Matching Problems in Iron & Steel Plants

Aiming at solving the problem to match orders with inventory surplus in an iron and steel plant, an optimal multi-objective 0-1 programming model is established to maximize the utilization of material surplus on inventory and minimize the matching cost orders. Two objective functions are incorporated by fuzzy decision-making approach and the model is solved by improved PBIL (Population-based increased learning). Natural number encoding is used to represent the result of orders matching based on the model's characteristic with the impractical chromosomes repaired in terms of learning probability. Then, the computation of a practical instance and a comparison of the computational result with the result by genetic algorithm further demonstrate that the model and the algorithm are the ideal way to solve the optimal problem of order matching.