In copper smelting, the ore blending scheme is crucial for product quality and cost. Traditional methods, relying on manual experience, have limitations and can't reach the optimal. This study thus presents an int...
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In copper smelting, the ore blending scheme is crucial for product quality and cost. Traditional methods, relying on manual experience, have limitations and can't reach the optimal. This study thus presents an intelligent ore blending method. It starts by constructing a mathematical model. The objective function covers three aspects: minimising the cost of raw materials entering the furnace, minimising deviation of elemental content of raw materials entering the furnace from set values, and minimising deviation of the total weight of the ore blend from the set value. Constraint conditions consider production processes to ensure model feasibility. An improved PSO algorithm, with a linearly decreasing inertia weight and constriction factor method, is designed to solve the model. Tests using two sets of data from a large copper smelting enterprise show that for the same Cu and S contents, the cost of raw materials entering the furnace decreased by 40.044 yuan and 35.186 yuan per ton, respectively. Also, the intelligent method converges quickly, getting an optimised scheme in about 20s. This reduces ore blending workload, improves efficiency, cuts costs, and brings economic benefits to the enterprise. Dans le proc & eacute;d & eacute;de fusion du cuivre, le sch & eacute;ma de m & eacute;lange du minerai joue un r & ocirc;le d & eacute;cisif dans la qualit & eacute;et le co & ucirc;t du produit. Cependant, les m & eacute;thodes traditionnelles de m & eacute;lange du minerai se fondent principalement sur l'exp & eacute;rience manuelle et pr & eacute;sentent des limites importantes, ce qui rend difficile l'obtention d'un sch & eacute;ma de m & eacute;lange du minerai proche de l'optimal. Par cons & eacute;quent, cette & eacute;tude propose une m & eacute;thode intelligente de m & eacute;lange du minerai. En premier, cette m & eacute;thode construit un mod & egrave;le math & eacute;matique dont la fonction objective comprend trois aspects: premi & egrave;rement, le co & uci
As a basic part of organizations' logistics management, purchasing function has supplier selection as one of its main responsibilities. One of the main objectives a buyer follows in supplier selection is to determ...
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As a basic part of organizations' logistics management, purchasing function has supplier selection as one of its main responsibilities. One of the main objectives a buyer follows in supplier selection is to determine optimal quota to be allocated to each supplier. How to allocate orders to different suppliers is as important task as it may affect efficiency of the whole chain. Also, due to variations in real-world business environment, order allocation process is always associated with uncertainties that make it complicated. Therefore, a three-stage integrated framework with environmental uncertainties considered is proposed to allocate orders;this framework can determine qualified suppliers to whom it assigns optimal quota. Considering multi-period purchases and uncertainties, this framework presents a multi-objective nonlinear programming model to determine optimal quota to be allocated to each qualified supplier within each specified period. In order to have the order allocation process closer to real-world cases while increasing the reliability of the obtained solutions, time value of money, inflation, transportation modes, supplier's profit, and pricing strategy are considered in this model. According to uncertain structure of the proposed model, a solution strategy is proposed to convert this model into a single-objective deterministic model. Then, the resulted single-objective deterministic model is solved by proposing three evolutionary metaheuristic algorithms based on cuckoo optimization algorithm and imperialist competitive algorithm. Finally, a sample problem is presented together with some statistical tests and sensitivity analyses to assess and examine the proposed framework.
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