Agent-based supply chain management—1: framework

In the face of highly competitive markets and constant pressure to reduce lead times, enterprises today consider supply chain management to be the key area where improvements can significantly impact the bottom line. More enterprises now consider the entire supply chain structure while taking business decisions. They try to identify and manage all critical relationships both upstream and downstream in their supply chains. Some impediments to this are that the necessary information usually resides across a multitude of resources, is ever changing, and is present in multiple formats. Most supply chain decision support systems (DSSs) are specific to an enterprise and its supply chain, and cannot be easily modified to assist other similar enterprises and industries. In this two-part paper, we propose a unified framework for modeling, monitoring and management of supply chains. The first part of the paper describes the framework while the second part illustrates its application to a refinery supply chain. The framework integrates the various elements of the supply chain such as enterprises, their production processes, the associated business data and knowledge and represents them in a unified, intelligent and object-oriented fashion. Supply chain elements are classified as entities, flows and relationships. Software agents are used to emulate the entities i.e. various enterprises and their internal departments. Flows—material and information—are modeled as objects. The framework helps to analyze the business policies with respect to different situations arising in the supply chain. We illustrate the framework by means of two case studies. A DSS for petrochemical cluster management is described together with a prototype DSS for crude procurement in a refinery.     

Decision support for integrated refinery supply chains: Part 1. Dynamic simulation

Supply chain studies are increasingly given top priority in enterprise-wide management. Present-day supply chains involve numerous, heterogeneous, geographically distributed entities with varying dynamics, uncertainties, and complexity. The performance of a supply chain relies on the quality of a multitude of design and operational decisions made by the various entities. In this two-part paper, we demonstrate that a dynamic model of an integrated supply chain can serve as a valuable quantitative tool that aids in such decision-making. In this Part 1, we present a dynamic model of an integrated refinery supply chain. The model explicitly considers the various supply chain activities such as crude oil supply and transportation, along with intra-refinery supply chain activities such as procurement planning, scheduling, and operations management. Discrete supply chain activities are integrated along with continuous production through bridging procurement, production, and demand management activities. Stochastic variations in transportation, yields, prices, and operational problems are considered in the proposed model. The economics of the refinery supply chain includes consideration of different crude slates, product prices, operation costs, transportation, etc. The proposed model has been implemented as a dynamic simulator, called Integrated Refinery In-Silico (IRIS). IRIS allows the user the flexibility to modify not only parameters, but also replace different policies and decision-making algorithms in a plug-and-play manner. It thus allows the user to simulate and analyze different policies, configurations, uncertainties, etc., through an easy-to-use graphical interface. The capabilities of IRIS for strategic and tactical decision support are illustrated using several case studies.     

原油作业过程优化的研究进展

原油作业过程是石油供应链的重要组成环节,包括炼油企业生产过程中的原油采购、原油分配、原油输送、原油储存和原油调和等多个工业流程。原油作业过程优化具有很高的学术理论价值与工业应用价值,与其相关的研究工作是目前学术界与工业界共同关注的热点。首先简要描述了原油作业过程,并对其优化问题的难点进行分析;其次,分别从优化模型、优化算法以及不确定性优化方法三个研究角度,重点阐述了原油采购优化、原油储运优化、原油调和优化以及不确定性条件下原油作业优化四个主要研究方向的学术进展,并对当前已有的研究成果进行了归纳总结;最后,对原油作业过程优化当前存在的一些问题提出了建议,并对该领域未来的发展方向与趋势进行了展望。     

Integrated crude selection and refinery optimization under uncertainty

Crude oil selection and procurement is the most important step in the refining process and impacts the profit margin of the refinery significantly. Due to uncertain quality of the crudes, conventional deterministic modeling and optimization methods are not suitable for refinery profitability enhancement. Therefore, a novel optimization scheme for crude oil procurement integrated with refinery operations in the face of uncertainties is presented. The decision process comprises two stages and is solved using a scenario‐based stochastic programming formulation. In Stage I, the optimal crude selections and purchase amounts are determined by maximizing the expected profit across all scenarios. In Stage II, the uncertainties are realized and optimal operations for the refinery are determined according to this realization. The resulting large‐scale mixed‐integer nonlinear programming formulation incorporates integer variables for crude selection and continuous variables for refinery operations, as well as bilinear terms for pooling processes. Nonconvex generalized Benders decomposition is used to solve this problem to obtain an global optimum efficiently. © 2015 American Institute of Chemical Engineers AIChE J, 62: 1038–1053, 2016     

Decision support for integrated refinery supply chains: Part 2. Design and operation

Supply chain management has continually attracted much attention as companies are constantly looking into areas where they can cut costs and improve profit margin while maintaining customer satisfaction. Optimizing design and operation of the supply chain is vital for this purpose. Simulation models that capture the dynamics and uncertainties of the supply chain can be used to effectively conduct design and operation optimization studies. In Part 1 of this two-part paper, we proposed an integrated refinery supply chain dynamic simulator called Integrated Refinery In-Silico (IRIS). Here, we demonstrate the application of IRIS to provide decision support for optimal refinery supply chain design and operation based on a simulation–optimization framework. Three case studies are presented: identifying the optimal strategy to deal with supply disruptions, optimization of design decisions regarding additional capacity investments, and optimization of policies’ parameters. These decisions are optimized for two objectives: profit margin and customer satisfaction. The framework consists of a linkage between IRIS and a non-dominated sorting genetic algorithm, implemented in a parallel computing environment for computational efficiency. Results indicate that the proposed framework works well for supporting policy and investment decisions in the integrated refinery supply chain.     

基于收益共享契约的基础油供应链建模方法

针对以炼油企业为主导的基础油供应链中所涉及到的多对一原料采购与定价问题,利用Stackelberg博弈问题求解方法,建立了分散决策模式下实际现有模型与收益共享契约模型,并引入粒子群优化算法,对模型进行参数估计,分析各参数对于供应链各成员利润的影响,同时得到一个广泛适用的采购定价模型。仿真结果表明,采用收益共享契约机制协调了供应链成员间利润分配,提高了原料供应商、炼油企业及供应链总体利润。     

A decision support system for the operational production planning and scheduling of an integrated pulp and paper mill

Production planning and scheduling in the process industry in general and in the pulp and paper (P&P) sector in particular can be very challenging. Most practitioners, however, address those activities relying only on spreadsheets, which is time-consuming and sub-optimal. The literature has reported some decision support systems (DSSs) that are far from the state-of-the-art with regard to optimization models and methods, and several research works that do not address industrial issues. We contribute to reduce that gap by developing and describing a DSS that resulted from several iterations with a P&P company and from a thorough review of the literature on process systems engineering. The DSS incorporates relevant industrial features (which motivated the development of a specific model), exhibits important technical details (such as the connection to existing systems and user-friendly interfaces) and shows how optimization can be integrated in real world applications, enhanced by key pre- and post-optimization procedures.     

灵活应用LP模型提高炼油厂设计水平

炼油厂设计水平的高低首先取决于总加工流程的选择是否优化合理。本文利用PIMS软件提供的模块化和开放性结构的建模环境,结合炼油工艺特点,根据设计需要灵活地构建了各种LP模型,用于炼油厂实际总加工流程的优化设计中。同时重点介绍了其在原油配置、重油加工路线选择和新工艺评估方面的应用成果,结果表明其对提高炼油厂的整体设计水平具有重要指导意义。     

炼油企业供应链管理中实沸点切割点的优化

20世纪90年代以来,国际石油和化工制造业面临新的挑战．企业要想提高竞争力,必须在产品成本、质量及新产品开发能力、生产灵活性以及用户的售后服务等方面取得优势．为此,应将企业整个生产过程放到供应链中去,使整个企业的原料供应→原料运输→仓储→生产制造→产品储存→产品运输→用户需求反馈等形成一个有机联系的系统（图1）．提高整个企业的生产和销售效率,就是提高供应链各环节的效率,对供应链进行动态优化,将所需数量和质量的产品,以最快的速度送到客户的手中．     

原油全产业链生产经营精益效益测算模型

基于框架方案进行粗略效益估算的传统效益测算方法,在数据口径、测算精确度、测算准确度、测算及时性等方面已无法满足服务生产、引领生产的作用。为解决这一问题,本文提出了一种在现有信息系统数据集成的基础上,构建效益测算模型,实现原油全产业链精益效益测算的方法。该方法以服务生产经营为目的,在梳理和分析原油产业链上下游各环节业务逻辑基础上,基于各业务环节之间的物质流和价值流,构建了原油全产业链精益效益测算模型,可实现原油产业链整体效益和分环节效益的测算;模型数据颗粒度与生产口径保持一致,并可基于集成的信息系统,实现数据的快速导入。经过在生产管理部门应用,该模型可在初步计划数据生成以后,实现原油产业链整体和分环节效益的快速、精细测算,降低了数据维护的工作量,提高了测算效率和测算结果的准确性,有效地提升了原油产业链生产管控水平,实现了由生产计划逐步向经营计划的转变。     

Fluor selected for clean fuels project in South Africa

Shell SA Energy and BP Southern Africa have chosen Fluor Corp to provide engineering, design, procurement and construction management services for a clean fuels project at their jointly owned SAPREF crude oil refinery in Durban, South Africa.     

炼油企业库存管理

以销售预测为依托,对市场需求不确定的炼油企业供应链的库存管理进行探索性研究.提出了炼油企业库存管理的策略、库存管理参数设计和算法,实际应用结果证明了该策略的合理性.     

不确定条件下石化企业供应链计划优化

针对石化企业供应链计划层的不确定性,提出了一种多周期、多产品的计划优化模型。基于离散时间建模方法,利用模糊可能性方法将不确定性问题转化为确定性问题,建立了混合整数线性规划模型。同时采用模型预测控制思想,利用模型的动态属性为决策者提供了不同满意度下的决策序列。以某炼油企业供应链为实例,证明了本方法的有效性。     

山东省油料业务的研究进展

本文对山东省主要终端行业对成品油、燃料油产品的市场需求现状及其"十一五"期间对油品需求的发展趋势进行深入分析,结合山东省油品市场的现状(山东省拥有许多地方小炼厂,原油一次加工能力大,同时国内几家大型石油公司纷纷强化在山东省的炼化业务),提出了山东省油品业务发展的建议——促进小炼油企业与大型油气生产加工一体化企业发展合作关系等,为有关研究提供参考。     

A model-based rescheduling framework for managing abnormal supply chain events

Enterprises today have realized the importance of supply chain management to achieve operational efficiency, cut costs, and maintain quality. Uncertainties in supply, demand, transportation, market conditions, and many other factors can interrupt supply chain operations, causing significant adverse effects. These uncertainties motivate the development of decision support systems for managing disruptions in the supply chain. In this paper, we propose a model-based framework for rescheduling operations in the face of supply chain disruptions. A causal model, called the composite-operations graph, captures the cause-and-effect among all the variables in supply chain operation. Its subgraph, called scheduled-operations graph, captures the causal relationships in a schedule and is used for identifying the consequences of a disruption. Rescheduling is done by searching a rectifications-graph, which captures all possible options to overcome the disruption effects, based on a user-specified utility function. In contrast to heuristic approaches, the main advantages of the proposed model-based rescheduling method are the completeness of solution search and flexibility of the utility function. The proposed framework is illustrated using a refinery supply chain example.     

炼油企业生产运营系统的活动模型研究

本文针对炼油企业各个环节的特点,建立了炼油企业生产运营系统活动模型。将需求预测、生产计划和原油采购等部分进行活动模型分解。建立了炼油企业生产运营系统IDEFO模型。     

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。     

Enterprise-wide modeling & optimization—An overview of emerging research challenges and opportunities

The process systems engineering (PSE) as well as the operations research and management science (ORMS) literature has hitherto focused on disparate processes and functions within the enterprise. These themes have included upstream R&D pipeline management, planning and scheduling in batch and continuous manufacturing systems and more recently supply chain optimization under uncertainty. In reality, the modern process enterprise functions as a cohesive entity involving several degrees of cross-functional co-ordination across enterprise planning and process functions. The complex organizational structures underlying horizontally and vertically integrated process enterprises challenge our understanding of cross-functional co-ordination and its business impact. This article looks at the impact of enterprise-wide cross-functional coordination on enterprise performance, sustainability and growth prospects. Cross-functional coordination is defined as the integration of strategic and tactical decision-making processes involving the control of financial and inventory flows (both internal and external) as well as resource deployments. Initially, we demonstrate the existence of cross-functional decision-making dependencies using an enterprise network model. Subsequently, we discuss interactions between enterprise planning decisions involving project financing, debt-equity balancing, R&D portfolio selection, risk hedging with real derivative instruments, supply chain asset creation and marketing contracts which influence decision-making at the activity/process level. Several case studies are included to re-enforce the point that planning and process decisions need to be integrated.     

面向石化企业间物流集成计划优化的模型及应用

在石油化工行业,上下游企业的联合生产是供应链优化的主要途径和重要手段。但由于石化企业的生产工艺过程复杂且设计相互独立,企业间的协同生产往往仅存在于大型石化集团统一设计、建造的炼化联合生产系统中。本文提出了适用于独立运营的石化企业集成的前提条件和建模方法,以独立生产的炼油厂和烯烃厂为研究对象,基于其生产物流计划模型,建立统一的联合生产计划优化模型。该集成优化模型为混合整数线性规划模型,以生产效益最大化作为优化求解目标,并分析了市场需求平稳、萎缩和旺盛3种不同的案例场景。实例计算表明,同独立生产相比较,石化企业的集成优化和联合生产能够降低物料存储成本和总成本,节省优化求解所需的计算资源,并促进经济效益的提升。     

Biomass-to-bioenergy and biofuel supply chain optimization: Overview,key issues and challenges

This article describes the key challenges and opportunities in modeling and optimization of biomass-to-bioenergy supply chains. It reviews the major energy pathways from terrestrial and aquatic biomass to bioenergy/biofuel products as well as power and heat with an emphasis on “drop-in” liquid hydrocarbon fuels. Key components of the bioenergy supply chains are then presented, along with a comprehensive overview and classification of the existing contributions on biofuel/bioenergy supply chain optimization. This paper identifies fertile avenues for future research that focuses on multi-scale modeling and optimization, which allows the integration across spatial scales from unit operations to biorefinery processes and to biofuel value chains, as well as across temporal scales from operational level to strategic level. Perspectives on future biofuel supply chains that integrate with petroleum refinery supply chains and/or carbon capture and sequestration systems are presented. Issues on modeling of sustainability and the treatment of uncertainties in bioenergy supply chain optimization are also discussed.