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     

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     

Incorporating environmental impacts and regulations in a holistic supply chains modeling: An LCA approach

Corporate approaches to improve environmental performance cannot be undertaken in isolation, so a concerted effort along the supply chain (SC) entities is needed which poses another important challenge to managers. This work addresses the optimization of SC planning and design considering economical and environmental issues. The strategic decisions considered in the model are facility location, processing technology selection and production–distribution planning. A life cycle assessment (LCA) approach is envisaged to incorporate the environmental aspects of the model. IMPACT 2002+ methodology is selected to perform the impact assessment within the SC thus providing a feasible implementation of a combined midpoint–endpoint evaluation. The proposed approach reduces the value-subjectivity inherent to the assignment of weights in the calculation of an overall environmental impact by considering endpoint damage categories as objective function. Additionally, the model performs an impact mapping along the comprising SC nodes and activities. Such mapping allows to focus financial efforts to reduce environmental burdens to the most promising subjects. Furthermore, consideration of CO₂ trading scheme and temporal distribution of environmental interventions are also included with the intention of providing a tool that may be utilized to evaluate current regulatory policies or pursue more effective ones. The mathematical formulation of this problem becomes a multi-objective MILP (moMILP). Criteria selected for the objective function are damage categories impacts, overall impact factor and net present value (NPV). Main advantages of this model are highlighted through a realistic case study of maleic anhydride SC production and distribution network.     

Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis

This article addresses the optimal design and planning of cellulosic ethanol supply chains under economic, environmental, and social objectives. The economic objective is measured by the total annualized cost, the environmental objective is measured by the life cycle greenhouse gas emissions, and the social objective is measured by the number of accrued local jobs. A multiobjective mixed‐integer linear programming (mo‐MILP) model is developed that accounts for major characteristics of cellulosic ethanol supply chains, including supply seasonality and geographical diversity, biomass degradation, feedstock density, diverse conversion pathways and byproducts, infrastructure compatibility, demand distribution, regional economy, and government incentives. Aspen Plus models for biorefineries with different feedstocks and conversion pathways are built to provide detailed techno‐economic and emission analysis results for the mo‐MILP model, which simultaneously predicts the optimal network design, facility location, technology selection, capital investment, production planning, inventory control, and logistics management decisions. The mo‐MILP problem is solved with an ε‐constraint method; and the resulting Pareto‐optimal curves reveal the tradeoff between the economic, environmental, and social dimensions of the sustainable biofuel supply chains. The proposed approach is illustrated through two case studies for the state of Illinois. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

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     

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.     

Game theory approach to optimal design of shale gas supply chains with consideration of economics and life cycle greenhouse gas emissions

This article addresses the optimal design of a non‐cooperative shale gas supply chain based on a game theory approach. Instead of assuming a single stakeholder as in centralized models, we consider different stakeholders, including the upstream shale gas producer and the midstream shale gas processor. Following the Stackelberg game, the shale gas producer is identified as the leader, whose objectives include maximizing its net present value (NPV) and minimizing the life cycle greenhouse gas (GHG) emissions. The shale gas processor is identified as the follower that takes actions after the leader to maximize its own NPV. The resulting problem is a multiobjective mixed‐integer bilevel linear programming problem, which cannot be solved directly using any off‐the‐shelf optimization solvers. Therefore, an efficient projection‐based reformulation and decomposition algorithm is further presented. Based on a case study of the Marcellus shale play, the non‐cooperative model not only captures the interactions between stakeholders but also provides more realistic solutions. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2671–2693, 2017     

Scenario-based dynamic negotiation for the coordination of multi-enterprise supply chains under uncertainty

A novel scenario-based dynamic negotiation approach is proposed for the coordination of decentralized supply chains under uncertainty. The relations between the involved organizations (client, provider and third parties) and their respective conflicting objectives are captured through a non-zero-sum and non-symmetric roles SBDN negotiation. The client (leader) designs coordination agreements considering the uncertain reaction of the provider (follower) resulting from the uncertain nature of the third parties, which is modeled as a probability of acceptance function. Different negotiation scenarios are studied: (i) cooperative, and (ii) non-cooperative and (iii) standalone cases. The use of the resulting models is illustrated through a case study with different vendors around a “leader” (client) in a decentralized scenario. Although the usual cooperation hypothesis will allow higher overall profit expectations, using the proposed approach it is possible to identify non-cooperative scenarios with high individual profit expectations which are more likely to be accepted by all individual partners.     

能源和化工系统的全生命周期评价和可持续性研究

在资源和能源日趋紧缺的背景下,开拓替代能源和新的化工产品技术路线势在必行。然而,目前对各种新技术方案的基础数据和系统分析比较薄弱,对产业布局和生态环境的长期影响有待深入研究。对近年来研究资源/能源化工复杂系统的建模、模拟、结构优化和系统集成问题的进展进行综述,运用过程系统投入产出分析的理论和方法,建立化工产品技术路线的全生命周期分析模型与集成的框架平台,研究资源、能源、技术、经济、环境等多因素综合优化评价问题。以煤气化合成气衍生的煤化工系统为基础案例,专门论述了在产品路线规划和过程合成中,集成优化、全生命周期评价和可持续性的研究进展。目标在于推进"技术-经济-环境-产业发展"多属性、多目标、多尺度系统集成的基础理论研究和技术创新。     

Data-driven distributionally robust optimization of shale gas supply chains under uncertainty

This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first-stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second-stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data-driven approach is applied to construct the ambiguity set based on principal component analysis and first-order deviation functions. By taking advantage of affine decision rules, a tractable mixed-integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a small-scale illustrative example and a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models, including the deterministic and stochastic programming counterparts, are investigated as well. © 2018 American Institute of Chemical Engineers AIChE J, 65: 947–963, 2019     

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     

Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models,decomposition algorithm,and a Comparison between CVaR and downside risk

A bicriterion, multiperiod, stochastic mixed‐integer linear programming model to address the optimal design of hydrocarbon biorefinery supply chains under supply and demand uncertainties is presented. The model accounts for multiple conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives, and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk. The latter criterion is measured by conditional value‐at‐risk and downside risk. The model simultaneously determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multicut L‐shaped method is implemented to circumvent the computational burden of solving large scale problems. The proposed modeling framework and algorithm are illustrated through four case studies of hydrocarbon biorefinery supply chain for the State of Illinois. Comparisons between the deterministic and stochastic solutions, the different risk metrics, and two decomposition methods are discussed. The computational results show the effectiveness of the proposed strategy for optimal design of hydrocarbon biorefinery supply chain under the presence of uncertainties. © 2012 American Institute of Chemical Engineers AIChE J, 2012     

Life cycle assessment of functional materials and devices: Opportunities,challenges, and current and future trends

Functional ceramics such as piezoelectrics, thermoelectrics, magnetic materials, ionic conductors, and semiconductors are opening new frontiers that underpin numerous aspects of modern life. This widespread usage comes with a responsibility to understand what impact their mass production has on the environment. Life-cycle assessment (LCA) is a tool employed for the identification of sustainable materials pathways through the consideration of environmental burdens of materials both during fabrication and as a final product. Although the LCA technique has been widely used for the evaluation of environmental impacts in numerous product supply chains, its application for environmental profiling of functional ceramics is now gaining attention. This paper presents a review of current developments in LCA, including existing and emerging applications with emphasis on the development and fabrication of functional materials and devices (FM&D). Selected published works on LCA of functional ceramics are discussed, highlighting the importance of adopting LCA at the design stage and/or at laboratory stage before expensive investments and resources are committed. Drawing from the extant literature, we show that the integration of environmental and sustainability principles into the overall process of FM&D manufacturing, in a way that anticipates foreseeable harmful consequences while identifying opportunities for improvement, can aid the timely communications of key findings to functional materials developers. This guides the orientation of research, development and deployment, and provides insights toward the prioritization of research activities while potentially averting unintended consequences. It is intended that the review presented will encourage the materials science community to engage with LCA to address important materials design, substitution, and optimization needs.     

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     

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。     

Industrial system prioritization using the sustainability-interval-index conceptual framework with life-cycle considerations

This study aims to develop a sustainability-interval-index (SII) conceptual framework with life-cycle considerations which can incorporate the opinions/preferences of multiple stakeholders in the complex decision-making processes and address the decision-making matrix composed by multiple types of data for multiactor life cycle ranking of industrial systems. The multiactor fuzzy best-worst method which allows multiple groups of stakeholders to use fuzzy numbers to express their opinions and preferences was developed for determining the weights of the indicators in life cycle sustainability assessment. A multicriteria decision-making method under hybrid information was developed for addressing the decision-making matrix composed by multiple types of data. Five hydrogen production pathways were studied, and the results reveal that the developed SII is feasible for sustainability ranking of industrial systems in life cycle perspective.     

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。     

Computer-aided process engineering for environmental efficiency: Industrial drying of biomass

The idea of taking into account environmental impact criteria in the process design becomes a necessity for both the industry and governments, due to increased binding regulations. Moreover, to address the issue of integrating sustainable processes, environmental impact must be weighed and balanced against other concerns, such as economic performance, product quality, and long-term sustainability. Therefore, this study introduces a methodology for environmental impact minimization and optimization of multiple conflicting criteria. A general eco-design method for biomass drying process is proposed. The main target is to develop an assessment computer-aided process engineering tool that compares environmental impacts of different operating conditions and fuel types to support decision-makers for an improved compliance to environmental criterion and sustainability.