A Synchronization Optimization Model Of Production And Supply Chain Network Planning And Its Application In Open Pit Mines

As competition among enterprises gradually expands to their supply chains, supply chain network design and optimization become more and more important. This paper analyzes the current research gaps of enterprise supply chain network optimization from the perspective of the system and proposes the problem on the synchronized optimization of production and network for enterprises with complex supply chains. This paper takes into account a multi-cycle and multi-product dynamic supply chain network optimization problem integrating facilities location, capacity planning and the storage and distribution of raw materials and products etc.On this basis, the writer sets up a profit-maximum model when considering the major strategic costs of enterprise production and network optimization. The model is solved by using the branch and bound algorithm. Then the paper applies the synchronized production and network optimization model to open pit mines'supply chain design and planning. Open bauxite mines are used as examples to illustrate it.Finally, this paper probes into the model-solving ideas and algorithm. With an in-depth investigation of the problem's characteristics, the author employs open bauxite mines as an example. The synchronized optimization of open bauxite mine production and supply chain network is discretized to propose a simulated annealing algorithm, which integrates the genetic algorithm crossover and mutation operator and has memory features. Case studies have proved the validity of this algorithm. Besides, a statistic inductive algorithm is designed to solve the continuous optimization models. Numerical examples demonstrate that the algorithm can generate a global optimum within the decision-making criteria.The innovation of this paper lies in distinguishing suppliers and customers of different importance and considers every strategic costs in enterprise operation. In addition, the paper applies the modified model to large-scale resourse development enterprises and considering the problem's features designs the random inductive algorithm and the simulated annealing algorithm. Therefore, the paper not only theoretically enriches the supply chain network optimization problem, but also carries out some prelimilary attempt to handle the problem through discretization. In practice, it extends the traditional supply chain network optimization to new industries and fields.