多期生物燃料供应链网络建模与多目标优化

建立了一个基于多目标优化以及生命周期评价（LCA）的多期生物燃料供应链模型。该模型的3个目标函数分别为总折现利润、平均单位能量生物燃料的温室气体排放和化石能源投入（economic,energy,environmental,3E）。为了将生物质生产的季节性以及库存等问题引入模型中,需要对每年进行多期划分。考虑到需要进一步引入供应链的扩张,模型的时间跨度设定为3年。此外,该模型还考虑了生物质产地、工厂,生物燃料市场的选址以及各节点间的物流流量等问题。通过将非线性的后两个目标函数利用ε-constraint法转化为线性约束条件,该模型最终被转化为混合整数线性规划（MILP）问题并得以求解。对解得的非劣解在三维坐标系上线性插值可得非劣解所在曲面,它揭示了3E目标之间的权衡取舍关系。还使用了一个基于中国国情的数据的案例对该模型进行检验。     

Supply chain management using an optimization driven simulation approach

In this work, we propose a hybrid simulation‐based optimization framework to solve the supply chain management problem. The hybrid approach combines a mathematical programming model with an agent‐based simulation model and uses them in an iterative framework. The optimization model is used to guide the decisions toward an optimal allocation of resources given the realistic supply chain representation given by the simulation. Thus, the proposed approach provides a more realistic solution compared to a stand‐alone optimization model, often a simplified representation of the actual system, by making use of the simulation model, which captures the detailed dynamic behavior of the system. A multiobjective problem has been formulated by taking into consideration the environmental impact of supply chain operations. The proposed framework has been applied to small‐scale case studies to study the effectiveness of the approach for such problems. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4612–4626, 2013     

Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty

In this paper, we address the strategic planning of integrated bioethanol–sugar supply chains (SC) under uncertainty in the demand. The design task is formulated as a multi-scenario mixed-integer linear programming (MILP) problem that decides on the capacity expansions of the production and storage facilities of the network over time along with the associated planning decisions (i.e., production rates, sales, etc.). The MILP model seeks to optimize the expected performance of the SC under several financial risk mitigation options. This consideration gives a rise to a multi-objective formulation, whose solution is given by a set of network designs that respond in different ways to the actual realization of the demand (the uncertain parameter). The capabilities of our approach are demonstrated through a case study based on the Argentinean sugarcane industry. Results include the investment strategy for the optimal SC configuration along with an analysis of the effect of demand uncertainty on the economic performance of several biofuels SC structures.     

Optimization for Intelligent Operation of Supply Chains

The schedule for manufacturing and its delivery should be strategically determined to maintain economic and sustainable management of a supply chain. However, most of existing design models for the supply chain assumes that it is known or pre-specified when products are manufactured and delivered, and therefore, only the amount of products and delivery are of interest to optimize within design frameworks. In order to provide high flexibility and cost-effectiveness in manufacturing and supply chain activities, both timing information (i.e., when to produce and deliver) and capacity (i.e., how much to produce and deliver) need to be considered simultaneously. New MILP (mixed integer linear programming) model for the design and optimization of the supply chain has been proposed, in which these two key decision variables are simultaneously optimized. For dealing with computational difficulties resulted from the large-size problem, a sequentially-updating procedure is also proposed. In this sequentially-updating procedure, the whole distribution network is divided into subsystems and optimized interactively within iterative procedure, where each subsystem is sequentially optimized until no profit improvement is observed. The enhanced flexibility of the supply chain can be obtained from this improved design philosophy. This also ensures reliable and robust responsiveness of the supply chain to customers’ demand without sacrificing efficiency and/or cost-effectiveness of manufacturing and delivery activities. Illustrated case studies show that the proposed method is able to deal with large and complex supply chain problems with significant cost savings.     

A LCA Based Biofuel Supply Chain Analysis Framework

This paper presents a life cycle assessment （LCA） based biofuel supply chain （SC） analysis framework which enables the study of economic, energy and environmental （3E） performances by using multi-objective opti-mization. The economic objective is measured by the total annual profit, the energy objective is measured by the average fossil energy （FE） inputs per MJ biofuel and the environmental objective is measured by greenhouse gas （GHG） emissions per MJ biofuel. A multi-objective linear fractional programming （MOLFP） model with multi-conversion pathways is formulated based on the framework and is solved by using theε-constraint method. The MOLFP prob-lem is turned into a mixed integer linear programming （MILP） problem by setting up the total annual profit as the optimization objective and the average FE inputs per MJ biofuel and GHG emissions per MJ biofuel as constraints. In the case study, this model is used to design an experimental biofuel supply chain in China. A set of the weekly Pareto optimal solutions is obtained. Each non-inferior solution indicates the optimal locations and the amount of biomass produced, locations and capacities of conversion factories, locations and amount of biofuel being supplied in final markets and the flow of mass through the supply chain network （SCN）. As the model reveals trade-offs among 3E criteria, we think the framework can be a good support tool of decision for the design of biofuel SC.     

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.     

Nationwide energy supply chain analysis for hybrid feedstock processes with significant CO2 emissions reduction

Integrating diverse energy sources to produce cost‐competitive fuels requires efficient resource management. An optimization framework is proposed for a nationwide energy supply chain network using hybrid coal, biomass, and natural gas to liquids (CBGTL) facilities, which are individually optimized with simultaneous heat, power, and water integration using 162 distinct combinations of feedstock types, capacities, and carbon conversion levels. The model integrates the upstream and downstream operations of the facilities, incorporating the delivery of feedstocks, fuel products, electricity supply, water, and CO₂ sequestration, with their geographical distributions. Quantitative economic trade‐offs are established between supply chain configurations that (a) replace petroleum‐based fuels by 100%, 75%, and 50% and (b) utilize the current energy infrastructures. Results suggest that cost‐competitive fuels for the US transportation sector can be produced using domestically available coal, natural gas, and sustainably harvested biomass via an optimal network of CBGTL plants with significant GHG emissions reduction from petroleum‐based processes. © 2012 American Institute of Chemical Engineers AIChE J, 2012     

Chemical supply chain modeling for analysis of homeland security events

The potential impacts of man-made and natural disasters on chemical plants, complexes, and supply chains are of great importance to homeland security. To be able to estimate these impacts, we developed an agent-based chemical supply chain model that includes: chemical plants with enterprise operations such as purchasing, production scheduling, and inventories; merchant chemical markets, and multi-modal chemical shipments. Large-scale simulations of chemical-plant activities and supply chain interactions, running on desktop computers, are used to estimate the scope and duration of disruptive-event impacts, and overall system resilience, based on the extent to which individual chemical plants can adjust their internal operations (e.g., production mixes and levels) versus their external interactions (market sales and purchases, and transportation routes and modes). To illustrate how the model estimates the impacts of a hurricane disruption, a simple example model centered on 1,4-butanediol is presented.     

A bi‐criterion optimization approach for the design and planning of hydrogen supply chains for vehicle use

In this article, we address the design of hydrogen supply chains for vehicle use with economic and environmental concerns. Given a set of available technologies to produce, store, and deliver hydrogen, the problem consists of determining the optimal design of the production‐distribution network capable of satisfying a predefined hydrogen demand. The design task is formulated as a bi‐criterion mixed‐integer linear programming (MILP) problem, which simultaneously accounts for the minimization of cost and environmental impact. The environmental impact is measured through the contribution to climate change made by the hydrogen network operation. The emissions considered in the analysis are those associated with the entire life cycle of the process, and are quantified according to the principles of Life Cycle Assessment (LCA). To expedite the search of the Pareto solutions of the problem, we introduce a bi‐level algorithm that exploits its specific structure. A case study that addresses the optimal design of the hydrogen infrastructure needed to fulfill the expected hydrogen demand in Great Britain is introduced to illustrate the capabilities of the proposed approach. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

A framework for ammonia supply chain optimization incorporating conventional and renewable generation

Ammonia is an essential nutrient for global food production brought to farmers by a well‐established supply chain. This article introduces a supply chain optimization framework which incorporates new renewable ammonia plants into the conventional ammonia supply chain. Both economic and environmental objectives are considered. The framework is then applied to two separate case studies analyzing the supply chains of Minnesota and Iowa, respectively. The base case results present an expected trade‐off between cost, which favors purchasing ammonia from conventional plants, and emissions, which favor building distributed renewable ammonia plants. Further analysis of this trade‐off shows that a carbon tax above $25/t will reduce emissions in the optimal supply chain through building large renewable plants. The importance of scale is emphasized through a Monte Carlo sensitivity analysis, as the largest scale renewable plants are selected most often in the optimal supply chain. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4390–4402, 2017     

Multienterprise supply chain: Simulation and optimization

The advancements in connectivity among the entities belonging to industrial supply chain have given rise to more complex, global supply chain networks. These networks are often constituted of entities that belong to multiple such networks. Interactions among the entities in such networks are also influenced by whether they belong to the same enterprise or different ones. This work takes into consideration the effect of such interactions. The entities belonging to different enterprises are assumed to interact through auctions. An agent based simulation model that incorporates such auctions is used to represent multienterprise supply chain networks. The dynamics of the supply chain affected by the auction mechanism are investigated. Also a derivative free optimization methodology is proposed to find the optimal warehouse capacities for the minimization of total cost. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3392–3403, 2016     

Optimal design and planning of sustainable chemical supply chains under uncertainty

This article addresses the design of sustainable chemical supply chains in the presence of uncertainty in the life cycle inventory associated with the network operation. The design task is mathematically formulated as a bi‐criterion stochastic mixed‐integer nonlinear program (MINLP) that simultaneously accounts for the maximization of the net present value and the minimization of the environmental impact for a given probability level. The environmental performance is measured through the Eco‐indicator 99, which incorporates the recent advances made in Life Cycle Assessment. The stochastic model is converted into its deterministic equivalent by reformulating the probabilistic constraint required to calculate the environmental impact in the space of uncertain parameters. The resulting deterministic bi‐criterion MINLP problem is further reformulated as a parametric MINLP, which is solved by decomposing it into two sub‐problems and iterating between them. The capabilities of the proposed model and solution procedure are illustrated through two case studies for which the set of Pareto optimal, or efficient solutions that trade‐off environmental impact and profit, are calculated. These solutions provide valuable insights into the design problem and are intended to guide the decision maker towards the adoption of more sustainable design alternatives. © 2008 American Institute of Chemical Engineers AIChE J, 2009     

Flexibility assessment and risk management in supply chains

Increased uncertainty in recent years has led the supply chains to incorporate measures to be more flexible in order to perform well in the face of the uncertain events. It has been shown that these measures improve the performance of supply chains by mitigating the risks associated with uncertainties. However, it is also important to assess the uncertainty under which a supply chain network can perform well and manage risk. Flexibility is defined in terms of the bounds of uncertain parameters within which supply chain operation is feasible. A hybrid simulation‐based optimization framework that uses two‐stage stochastic programming in a rolling horizon framework is proposed. The framework enables taking optimum planning decisions considering demand uncertainty while managing risk. The framework is used to study the trade‐offs between flexibility, economic performance, and risk associated with supply chain operation. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4166–4178, 2015     

石化企业供应链管理研究

介绍了国内外石化行业的供应链管理现状,对中石化电子化供应链方案进行了分析,并就中石化供应链管理存在的问题提出解决方案。