Particle Swarm Optimization for Raw Material Purchasing Plan in Large Scale Ore Dressing Plant
粒子群优化算法在大型选矿企业原料采购计划中的应用

The raw ores purchasing cost is the main part of concentrate cost in an ore dressing plant. To optimize the raw ores purchasing plan is to minimize the purchasing cost. While assuring conditions of concentrate grade and quantity of raw ores which are required by production technology, the purchasing plan for varieties of raw ores to minimize the purchasing cost is very crucial to minimize the production cost of an ore dressing plant. Meeting the needs of concentrate inventory and concentrate grade, two models are proposed in this paper: the raw ores demand model for minimizing concentrate inventory and the raw ores purchasing model for minimizing purchasing cost. A particle swarm optimization (PSO) algorithm, based on fuzzy rule to adjust inertia weight, is used to dynamically optimize the models and to make sure the purchasing quantity of varieties of raw ores. Simulation experiments have been conducted by using the real data of an ore dressing plant. The result shows the effectiveness of the proposed algorithm in this paper.