Zhu Yunlong

Robust optimization model for remanufacturing system in uncertain reverse logistics environment

The remanufacturing process of reusable parts in uncertain reverse logistics environment is considered. An existing framework for remanufacturing system is adopted. In this framework, the manufacturer has two alternatives for supplying parts: either ordering the required parts to external suppliers or overhauling returned products and bringing them back to dasiaas newpsila conditions. The numbers of returned products are uncertain and can be described as a scenario set with certain probability. By using the approach of robust optimization based on scenario analysis, a robust optimization model is constructed to maximize the total cost savings by optimally deciding the quantity of parts to be processed at each remanufacturing facilities, the number of purchased parts from subcontractor. The results of a numerical example show that the model we proposed is both solution-robust and model-robust.

Research on Smelting Ingredient Diluting for Refined Copper Strip by Bacteria Foraging Optimization Algorithm

During the smelting process of refined copper strip production, to reduce varied losses caused by obvious superscales¿of some individual ingredients, the multi-charge diluting optimization method for smelting ingredients of refined copper strips is studied. The multi-object optimization model for smelting ingredients diluting is established. Lagrangian relaxation idea is introduced to transform the multi-objective model, and the detail implement steps of bacteria foraging optimization (BFO) algorithm are designed to solve above model. The experiment result shows that BFO algorithm is suitable for solving complex optimization problems such as diluting smelting ingredient.

Optimization setting control method based on BFO and CBR for laminar cooling water

Aiming at the target optimization problem in the laminar cooling process, we proposed a laminar cooling water optimization setting control method, integrating improved bacterial foraging optimization (BFO) algorithm and case-based reasoning (CBR) technology. We construct two layers structure of dynamic optimization to realize offline optimization and online reasoning. The simulation results with industrial operating data showed the effectiveness in searching optimized cooling water consumption in varying working condition. The proposed method has the ability to adjust the water consumption setting value in time and the strip coiling temperature is controlled in the target range.

Selective Purchasing Optimization of Raw Materials for Refined Copper Strip by BFO Algorithm

Aiming at improving the efficiency and scientificity of raw materials purchasing for refined copper strip producing, a multi-objective optimization model is established, the model is transformed by introducing some constraints into the objective function. For the raw material purchasing problem of refined copper strip, the encoding system of BFO algorithm is designed, and the tumble direction in conventional BFO algorithm is adapted so as to ensure bacteria swim feasible location each time. Thereby, the detail implement steps of BFO algorithm are designed. By the practical instance application of raw material purchasing problem of refined copper strip, the validity of above optimization model and BFO algorithm for the complex optimization model such as raw material purchasing problem of refined copper strip is validates.

Artificial Immune Principle Based Charging Optimization Algorithm for Refined Copper Strip Producing

With the purpose of optimize charging for refined copper strip producing, a multi-object real time charging model is established, in which some charging practical demand such as reusing copper resource, cutting down charging cost, reducing metal burn-up, substitutive degree among different brand old materials and so on are considered in detail. To resolve the model easily, it is converted and disposed. The immune principle is analyzed, the artificial immune principle based charging optimization algorithm (AIPCOA) is designed. Some key cycles including the representation method of antibody, the affinity between antibodies and the antigen as well as the affinity among the antibodies, the generating of initial population and so on are especially studied, and the detail implementing steps are given. To verify the validity of the algorithm, the algorithm is compared with the genetic algorithm (GA). The simulation result is verified that more diversity of the solution can be obtained by AIPCOA, more cross-sectional satisfaction solutions can be obtained by AIPCOA, thus, it is easy to selection the most adaptive scheme during practical charging. The artificial immune algorithm (AIA) is fit for solving the optimization problems in which several typical satisfaction solutions is demanded simultaneously as the optional schemes, and obvious practical operation flexibility and outstanding practical application outlook can be obtained by AIA .With the purpose of optimize charging for refined copper strip producing, a multi-object real time charging model is established, in which some charging practical demand such as reusing copper resource, cutting down charging cost, reducing metal burn-up, substitutive degree among different brand old materials and so on are considered in detail. To resolve the model easily, it is converted and disposed. The immune principle is analyzed, the artificial immune principle based charging optimization algorithm (AIPCOA) is designed. Some key cycles including the representation method of antibody, the affinity between antibodies and the antigen as well as the affinity among the antibodies, the generating of initial population and so on are especially studied, and the detail implementing steps are given. To verify the validity of the algorithm, the algorithm is compared with the genetic algorithm (GA). The simulation result is verified that more diversity of the solution can be obtained by AIPCOA, more cross-sectional satisfaction solutions can be obtained by AIPCOA, thus, it is easy to selection the most adaptive scheme during practical charging. The artificial immune algorithm (AIA) is fit for solving the optimization problems in which several typical satisfaction solutions is demanded simultaneously as the optional schemes, and obvious practical operation flexibility and outstanding practical application outlook can be obtained by AIA .

An Integrated Contract Strategy in a Three-Echelon Supply Chain with Capacity Limitation under the Forecast Update

In this paper, a model with one supplier, one manufacturer and one retailer is constructed to analyze the situation that a retailer makes orders twice before and after the forecast update from the supply chain coordination perspective. The manufacturerpsilas capacity is insufficient according to the second order of the retailer, so the manufacturer may recover the deficit by capacity outsourcing from the supplier at a certain option price at first and then supplying the very product back to the retailer by exercising the options. There are two decision points in this model. At the beginning of the planning horizon, the retailer decide how much to order for the first time, and the manufacturer knows her limited capacity and decides to purchase options from the supplier to assure the retailerpsilas demand to be completely satisfied. At the second decision point, with forecast update, the retailer makes his second order, and the manufacturer exercises the options to meet the retailerpsilas order. A numerical example is presented to illustrate the efficacy of the developed model in the end.