基础油供应链生产-分销计划模型及其优化

随着市场全球化的飞速发展,供应链思想已渗透到各行各业,是一种新型的企业管理思想。然而在一些 产业结构不合理、急需产业升级的领域,几乎没有相关的供应链研究,譬如石油化工领域的量化研究少之又少。 本文分析了在独立决策下基础油供应链生产、分销系统中,信息不共享,生产商、分销商以及客户只追求各自 利益最大化,忽视甚至是损害了供应链整体利益的现象。针对上述现象,构建了生产-分销集成计划模型,以实 现供应链总成本和总反应时间最小,提出了一种基于粒子群算法寻找多目标供应链网络Pareto 最优解的方法。 仿真实例的结果表明了集成模型的可行性和优越性。     

基于粒子群优化算法的板翅式换热器优化设计

以板翅式换热器的质量作为目标函数,以换热器芯体外形尺寸和翅片参数作为优化变量,采用粒子群优化算法对其结构尺寸进行优化设计,获得了换热器质量减轻、体积减小的效果。     

基于GA-PSO算法的烧结料场原料库存量优化

原料采购库存成本的约束是钢铁企业流动资金的制约瓶颈,针对钢铁企业烧结料场铁矿粉原料采购与消耗特点,以企业原料库存费用最小为目标建立了烧结料场铁矿粉原料库存量优化模型,提出一种基于遗传-粒子群算法的烧结料场铁矿粉库存量优化方法。同时,应用某钢铁企业360 m²烧结生产线的综合原料场实际生产数据进行仿真验证,结果表明,该模型可以反映该钢铁企业综合料场铁矿粉库存量的实际情况,采用的优化方法可以得到模型的最优解,为钢铁企业采购计划的制定提供决策支持。     

基于粒子群算法的喷水减温器建模研究

根据质量守恒和能量守恒定律建立喷水减温器的机理模型。为了提高模型精度,利用粒子群优化算法对喷水减温器的机理模型参数进行优化,并利用Matlab进行仿真,仿真结果很好地证明了模型的精确性。     

基于混合粒子群算法的复杂相平衡计算方法

对于含有两个部分互溶液相的相平衡问题,采用经典方法收敛困难或易陷于平凡解。为此根据最小Gibbs自由能原理,提出采用混合粒子群算法搜索全局最优解,计算得到系统的最小Gibbs自由能状态,实现复杂相平衡计算。通过改建目标函数,减少计算量,并引入组分相分率,将物料平衡约束转换为规范型立方空间的优化问题,适于粒子群算法搜索。在常规粒子群算法中引入Nelder-Mead单纯形操作,可显著提高搜优的速率和精度。将其应用于甲苯-水-苯胺液液平衡和苯-乙腈-水汽液液平衡计算,取得了良好的效果。     

基于粒子群算法的动态多目标优化

针对碳二氢生产中的反应器动态优化问题,目前虽然有多种算法对生产过程进行优化,但大部分只是对单一目标进行求解,提出一种更为灵活的反应器动态求解方法。在该方法中,首先构建碳二氢目标函数,然后采用多目标粒子群算法和分段线性函数参数法结合的方式对目标函数的进行求解,以提高整体搜索能力,得到碳二氢反应器动态优化的最优解。最后,以实际乙烯碳二加氢化工反应过程为例进行实验验证,结果证明,通过该方法进行求解的目标函数无论是在收敛性,还是在优化的平均值等方面,都比SADE-eCD和NSGA-II算法具有优势,说明该算法在反应器动态优化中是切实可行的。     

基于Matlab的吹膜机薄膜张力控制系统的建模与仿真

提出一种包含压缩因子及模拟退火算子的改进型粒子群算法,弥补了常规PSO算法的缺点。同时,提出一种改进型的二自由度PID控制器,并用改进的二自由度PID控制器替换薄膜张力控制系统中的常规PID控制器。仿真结果表明:改进方法具有优良的控制效果及鲁棒性,可以更好地满足吹膜机塑料薄膜张力控制系统参数复杂多变的生产环境。     

基于遗传算法和粒子群优化算法的电力系统无功优化

从数学的角度分析,电力系统无功优化是一个多变量、多约束、非连续性的混合非线性规划问题,因此,优化过程十分复杂.以减少有功网损为目标函数建立电力系统无功优化计算的数学模型,基于遗传算法和粒子群优化算法,提出一种新颖的混合策略来求解无功优化问题.IEEE 6和IEEE 14节点系统的仿真计算结果表明:与单一的遗传算法或粒子群优化算法相比,该混合策略在优化效果方面具有明显的优势.     

基于粒子群算法的高炉热风炉煤气消耗优化方法研究

高炉煤气管网压力与流量具有不稳定性,导致煤气存在严重放散损失。该研究基于粒子群算法进行高炉热风炉煤气消耗优化。首先,基于高炉热风炉煤气消耗量的预测结果,建立一个以煤气产消平衡和煤气放散量最小为目标的高炉热风炉煤气消耗优化模型;其次,引入粒子群算法求解模型,获得最佳优化方案。实验结果表明,经设计方法优化后的高炉热风炉煤气放散量基本为0,证实了该方法可以有效降低煤气消耗。     

粒子群优化算法的发展及应用

讨论了粒子群优化算法的发展和应用。介绍了粒子群优化算法的基本原理和算法流程,并且与其他演化算法进行了比较,给出了一些经常用到的测试函数。针对粒子群优化算法在搜索后期存在的不足,介绍了改进的粒子群优化算法,重点介绍了在实际应用领域中用到的改进粒子群优化算法。     

Fair profit allocation in supply chain optimization with transfer price and revenue sharing: MINLP model and algorithm for cellulosic biofuel supply chains

A mixed‐integer nonlinear programming (MINLP) formulation to simultaneously optimize operational decisions as well as profit allocation mechanisms in supply chain optimization, namely material transfer prices and revenue share policies among the supply chain participants is proposed. The case of cellulosic bioethanol supply chains is specifically considered and the game‐theory Nash bargaining solution approach is employed to achieve fair allocation of profit among the collection facilities, biorefineries, and distribution centers. The structural advantages of certain supply chain participants can be taken into account by specifying different values of the negotiation‐power indicators in the generalized Nash‐type objective function. A solution strategy based on a logarithm transformation and a branch‐and‐refine algorithm for efficient global optimization of the resulting nonconvex MINLP problem is proposed. To demonstrate the application of the proposed framework, an illustrative example and a state‐wide county‐level case study on the optimization of a potential cellulosic bioethanol supply chain in Illinois are presented. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3211–3229, 2014     

过程工业的集成供应链管理研究

分析了过程工业的特点和供应链管理在过程工业的应用现状,提出过程工业应通过采用集成供应链管理获得效益,即结合过程工业自身的特点,根据过程系统的功能层次模型和流结构模型,建立过程系统在投资决策、市场运营、管理控制3个不同运行层次上的集成供应链模型,并采用适当的优化策略求解,以求得企业在全生命周期内最大的综合效益。最后介绍了面向智能体的过程工业集成供应链建模方法以及求解策略。     

Downstream oil supply chain management: A critical review and future directions

The oil industry has been playing a particular role in the modern economy, acting globally in different countries within competitive business environments. Due to the complexity of the oil supply chain, the associated decision making process is a difficult task, which involves numerous elements from oil supply, going through oil refining, up to oil product distribution. Thus, decision-support tools are often required to assist the decision making in the context of the oil supply chain. The improvement of such decision-support tools is a continuous goal for corporations. From this background, this work aims to review the scientific production about the application of mathematical programming techniques to the distribution problems, faced by diverse entities in the downstream oil supply chain. The main objectives are to point out main contributions, besides identifying the major voids and new trends in order to establish an agenda for future research directions.     

Global supply chain planning for pharmaceuticals

The shortening of patent life periods, generic competition and public health policies, among other factors, have changed the operating context of the pharmaceutical industry. In this work we address a dynamic allocation/planning problem that optimises the global supply chain planning of a pharmaceutical company, from production stages at primary and secondary sites to product distribution to markets. The model explores different production and distribution costs and tax rates at different locations in order to maximise the company's net profit value (NPV).Large instances of the model are not solvable in realistic time scales, so two decomposition algorithms were developed. In the first method, the supply chain is decomposed into independent primary and secondary subproblems, and each of them is optimised separately. The second algorithm is a temporal decomposition, where the main problem is separated into several independent subproblems, one per each time period. These algorithms enable the solution of large instances of the problem in reasonable time with good quality results.     

基于Jini的供应链合作伙伴关系管理的研究与探索

市场逐步全球化,这使得企业之间需要通过合作才能共同增强竞争实力,供应链合作伙伴关系由此应运而生。Jini技术作为一种新的分布计算框架,它的简便灵活的分布式计算和强大的网络服务能够很好地解决企业之间数据的分布性、平台的异构性等问题,所以在供应链管理中具有非常广阔的应用前景。     

A mathematical modeling approach for supply chain management under disruption and operational uncertainty

In this work, we proposed a two-stage stochastic programming model for a four-echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.     

基于K/3供应链系统下制造业成本管理的思考

本文基于网络经济时代的新一代信息系统,K/3供应链系统下的制造业成本管理优势分析,并指出在实施K/3供应链系统中还需要企业优化的问题,希望能改善企业业务流程以提高企业核心竞争力提供一些参考。     

生物质原料供应链技术经济研究进展

生物质原料供应链是生物质资源转化的基础保障，是未来实现大规模生物质能源开发利用的关键所在。本文对生物质供应链相关技术问题进行了分析，分别介绍了生物质原料收获与收集、储存与预处理、运输等技术现状与存在的问题。同时，对供应链的技术经济性进行了综述，对比阐述了国内外采用的研究方法，通过建立定量化的数学模型来优化供应链的技术经济指标是当前研究的主要方式。国内外研究结果显示，生物质原料的收购价格、收集半径和收购模式是影响生物质供应链成本的关键因素。我国由于户均耕地占有面积小，生物质原料分散，生物质供应链是一个复杂的系统工程。在此基础上，进一步对生物质供应链的发展提出了展望，为生物质资源供应链的研究与发展提供参考和借鉴。     

Optimal supply chain design and operations under multi‐scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Although strategic and operational uncertainties differ in their significance of impact, a “one‐size‐fits‐all” approach has been typically used to tackle all types of uncertainty in the optimal design and operations of supply chains. In this work, we propose a stochastic robust optimization model that handles multi‐scale uncertainties in a holistic framework, aiming to optimize the expected economic performance while ensuring the robustness of operations. Stochastic programming and robust optimization approaches are integrated in a nested manner to reflect the decision maker's different levels of conservativeness toward strategic and operational uncertainties. The resulting multi‐level mixed‐integer linear programming model is solved by a decomposition‐based column‐and‐constraint generation algorithm. To illustrate the application, a county‐level case study on optimal design and operations of a spatially‐explicit biofuel supply chain in Illinois is presented, which demonstrates the advantages and flexibility of the proposed modeling framework and efficiency of the solution algorithm. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3041–3055, 2016     

Supply chain networks servicing upstream operations in oil and gas fields after the shale revolution

The recent irruption of shale resources in the oil and gas (O&G) industry has dramatically changed the paradigm for managing upstream operations. Unconventional production is largely driven by drilling new wells, yielding a much larger scale of material and service flows that need to be efficiently planned. This article presents an optimization framework for the design of integrated supply chain networks (SCNs) servicing upstream operations. Novel aspects addressed in this work are the integrated planning of material and service supplies; the economies of scale in operating, transportation and capital expenditures; the concept of waiting cost to quantify production losses; and the reverse flow of materials within closed-loop supply chains. The merits of the proposed approach are assessed through real-world case studies from the Argentine O&G industry. Optimal designs yield significant cost savings and confirm that the integrated planning of the SCN is critical for O&G operators after the shale revolution.