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.     

Designing biomass supply chains within planetary boundaries

The design of sustainable supply chains, which recently emerged as an active area of research in process systems engineering, is vital to ensure sustainable development. Despite past and ongoing efforts, the available methods often overlook impacts beyond climate change or incorporate them via standard life cycle assessment metrics that are hard to interpret from an absolute sustainability viewpoint. We here address the design of biomass supply chains considering critical ecological limits of the Earth—planetary boundaries—which should never be surpassed by anthropogenic activities. Our method relies on a mixed-integer linear program that incorporates a planetary boundaries-based damage model to quantify absolute sustainability precisely. We apply this approach to the sugarcane-to-ethanol industry in Argentina, identifying the optimal combination of technologies and network layout that minimize the impact on these ecological boundaries. Our framework can find applications in a wide range of supply chain problems related to chemicals and fuels production, energy systems, and agriculture planning.     

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.     

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.     

石化企业的有效竞争模式——战略供应链

资源的可靠获得，供应链的有效构建，对于大型石化企业的成功以至于生存，至关重要。阐述供应链的三种模式的区别。战略供应链模式，基于企业竞争与合作的理念，即“竞合”理念，与传统的市场交易型的供应链，和内部交易型的一体化供应链不同，其专注于构建企业的核心竞争力。介绍BP、壳牌等规模超大的跨国公司业务重组，放弃内部一体化的供应链模式，以提高其核心竞争力的企业发展动向，对中石化等国内石化企业的供应链现状进行了分析。     

Model-based systems engineering approaches to chemicals and materials manufacturing

Model-based systems engineering (MBSE) is part of a long-term trend toward model-centric approaches adopted by many engineering disciplines. We establish the need of an MBSE approach by reviewing the importance, complexity, and vulnerability of the U.S. chemical supply chains. We discuss the origins, work processes, modeling approaches, and supporting tools of the systems engineering discipline (SE) and discuss limitations of the Process Systems Engineering (PSE) framework. We make the case for MBSE as a more generalizable approach. We introduce systems modeling strategies for MBSE, and a novel MBSE method that supports the automation tailored and extended to support the analysis of chemical supply chains. We demonstrate a specific use case of this method by creating a systems model for the manufacturing of an active pharmaceutical ingredient, Atropine. We conclude with a prospectus on developmental opportunities for extracting greater benefit from MBSE in the design and management of chemical supply chains.     

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     

Supply chain risk identification using a HAZOP‐based approach

Risk management has become imperative for today's complex supply chains. Most approaches reported in the literature have been ad‐hoc and specific to certain risks; a general and comprehensive approach is lacking. To address this, we present a structured methodology for risk identification. Supply chain networks are in many ways similar to chemical plants, therefore well‐established methods and concepts from chemical process risk management can be adapted to supply chains. Drawing from this analogy, we propose to represent supply chain structure and operations using flow and work‐flow diagrams, equivalent to process flow diagrams (PFDs) and operating procedures. Following the HAZard and OPerability (HAZOP) analysis method common in process safety, risk identification can be performed by systematically generating deviations in different supply chain parameters and identifying their possible causes, consequences, safeguards, and mitigating actions. The application and benefits of the proposed approach are demonstrated using a refinery supply chain case study. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Resilient supply chain design and operations with decision-dependent uncertainty using a data-driven robust optimization approach

To addresses the design and operations of resilient supply chains under uncertain disruptions, a general framework is proposed for resilient supply chain optimization, including a quantitative measure of resilience and a holistic biobjective two-stage adaptive robust fractional programming model with decision-dependent uncertainty set for simultaneously optimizing both the economic objective and the resilience objective of supply chains. The decision-dependent uncertainty set ensures that the uncertain parameters (e.g., the remaining production capacities of facilities after disruptions) are dependent on first-stage decisions, including facility location decisions and production capacity decisions. A data-driven method is used to construct the uncertainty set to fully extract information from historical data. Moreover, the proposed model takes the time delay between disruptions and recovery into consideration. To tackle the computational challenge of solving the resulting multilevel optimization problem, two solution strategies are proposed. The applicability of the proposed approach is illustrated through applications on a location-transportation problem and on a spatially-explicit biofuel supply chain optimization problem. © 2018 American Institute of Chemical Engineers AIChE J, 65: 1006–1021, 2019     

Blocked crystallization in capped ultrathin polymer films studied by molecular simulations

The crystallization of capped ultrathin polymer films is closely dependent on film thickness and interfacial interaction. Using dynamic Monte Carlo simulations, the crystallization behaviors of polymer films confined between two substrates were investigated. The crystallization rate of confined polymers is reduced with high interfacial interactions. Above a critical strength of interfacial interaction, polymer crystallization in the thin film is inhibited within the simulation time scales. An increase in film thickness leads to a rise in critical interfacial interaction. In thicker films, the chains have more space to change conformation to form crystal stems. In addition, there are fewer absorbed segments in confined chains for the thicker films, and thus the chains have stronger ability to adjust their conformation. Therefore an increase in film thickness can cause a reduction in the entropic barrier required for the formation of crystals and thus an increase in the critical interfacial interaction. © 2018 Society of Chemical Industry     

Flexibility analysis of process supply chain networks

One of the key fundamentals for organizations to remain competitive in the present economic climate is to effectively manage their supply chains under uncertainty. The notion of supply chain flexibility attempts to characterize the ability of a supply chain to perform satisfactorily in the face of uncertainty. However, limited quantitative analysis is available. In this work, we utilize a flexibility analysis framework developed within the context of process operations and design to characterize supply chain flexibility. This framework also provides a quantitative mapping to various types of flexibility discussed in the operations research and management science literature. Two case studies are included to illustrate the application of this framework for analyzing the flexibility of existing supply chain processes, as well as utilizing it in supply chain design.     

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     

Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances

Traditionally the design of supply chains has been based on economic objectives. However, as societal environment concerns grows, environmental aspects are also emerging at academic and industry levels as decisive factors within the supply chain management context. The investment towards logistics structures that considers both economic and environmental performances is nowadays an important and current research topic.This paper addresses the planning and design of supply chain structures for annual profit maximization, while considering environmental aspects. The latter are accounted for through the Eco-indicator methodology. Profit and environmental impacts are balanced using an optimization approach adapted from symmetric fuzzy linear programming (SFLP), while the supply chain is modelled as a mixed integer linear programming (MILP) optimization problem using the Resource-Task-Network (RTN) methodology. The obtained model applicability is validated through the solution of a set of supply chain problems.     

Integration of control theory and scheduling methods for supply chain management

In this paper, we propose to use distributed model predictive control for supply chain optimization. In particular, we focus on inventory management in supply chains. We use cooperative model predictive control, in which each agent makes their local decisions by optimizing the overall supply chain objective. Motivated by recent results in Stewart, Wright, and Rawlings (2011), we develop a new cooperative MPC algorithm that is applicable to any stabilizable system, and in particular to supply chain models. We illustrate cooperative MPC for a two node supply chain example and compare its performance and properties with other classical distributed operating policies.     

Robust decision making for hybrid process supply chain systems via model predictive control

Model predictive control (MPC) is a promising solution for the effective control of process supply chains. This paper presents an optimization-based decision support tool for supply chain management, by means of a robust MPC strategy. The proposed formulation: (i) captures uncertainty in model parameters and demand by stochastic programming, (ii) accommodates hybrid process systems with decisions governed by logical conditions/rulesets, and (iii) addresses multiple supply chain performance metrics including customer service and economics, within an integrated optimization framework. Two mechanisms for uncertainty propagation are presented – an open-loop approach, and an approximate closed-loop strategy. The performance of the robust MPC framework is analyzed through its application to two process supply chain case studies. The proposed approach is shown to provide a substantial reduction in the occurrence of back orders when compared to a nominal MPC implementation.     

Linear viscoelastic relaxation modulus of polydisperse poly(dimethylsiloxane) melts containing unentangled chains

This work analyzes the relationship between the shear relaxation modulus of entangled, linear and flexible homopolymer blends and its molecular weight distribution (MWD) when a fraction of the sample contains chains with molecular weight M lower than the effective critical molecular weight between entanglements Mc_eff. This effective critical parameter is defined in terms of the critical molecular weight between entanglements M_c of the bulk polymer that forms the physical network and the effective mass fraction Wc_eff of the unentangled chains. In the terminal zone of the linear viscoelastic response, the double reptation mixing rule for blended entangled chains and a modified law for the relaxation time of chains in a polydisperse matrix are considered, where the effect of chains with M<Mc_eff is included. Although chain reptation with contour length fluctuations and tube constraint release are still the relevant mechanisms of chain relaxation in the terminal zone when the polydispersity is high, it is found that the presence of a fraction of molecules with M<Mc_eff modifies substantially the tube constrain release mode of chain relaxation. In this sense, a modified relaxation law for polymer chains in a polydisperse entangled melt that includes the effect of the MWD of unentangled chains is proposed. This law is validated with rheometric data of linear viscoelasticity for well-characterized polydimethylsiloxane (PDMS) blends and their MWD obtained from size exclusion chromatography. The short time response of PDMS, which involves the glassy modes of relaxation, is modeled by considering Rouse diffusion between entanglement points of chains with M>Mc_eff. This mechanism is independent from the MWD. The unentangled chains with M<Mc_eff occluded in the polymer network also follow Rouse modes of relaxation although they exhibit dependence on the MWD.     

Assessment of molecular connectedness in semi-dilute polymer solutions by elongational flow

The study of the response of polymer solutions to purely elongational flow-fields, as assessed by birefringence, has been extended to the semi-dilute region. As the concentration was increased the optical effects seen gave direct indication of the onset of network behaviour above a critical strain-rate. The concentration at which such chain interactions first occur was found to be significantly lower than identified by the conventional c1 criterion. At any given concentration a time scale could be identified below which the system responds as a network and above which, as an assembly of isolated chains. This critical disentanglement time decreased with concentration, consistent with the time needed for overlapping chains to diffuse apart. On a time-scale longer than this disentanglement time, the chains display the same coil-stretch transition with increasing strain-rate as in dilute solutions, with allowance for the increased solution viscosity. On this longer time-scale the chains can slip out of each other's environment, in spite of their geometric overlap. Atactic polystyrene and poly(ethylene oxide) were compared, polystyrene showing the greater entanglement effects by the present criterion. Some reference is made to more strongly interacting systems (H bonds, ionic forces) where chains can only extend in a mutually interacting fashion.     

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     

Process Design in World 3.0 – Challenges and Strategies to Master the Raw Material Change

The evolution of global energy supply chains leads to a raw material change in the chemical industry. Despite this change, the value‐added chains of the chemical industry have to keep up their output of diverse high‐quality products desired by the customers. C1 chemistry in combination with suitable conversion technologies yielding olefins and aromatics will play a key role in mastering this challenge. New chemical value‐added chains have to be developed and assessed, resulting in an increasing importance of conceptual process design. All this will take place in what Ghemawat has called the World 3.0, a globally linked but regionally diverse world. This diversity creates further challenges for process design in the chemical industries. A systematic concept to address these challenges is given here, including strategies for optimization and decision support.     

The essential role of network topology in rubber elasticity

This paper discusses various new aspects in the elasticity of rubbers and the statistics of elastomers. It is shown that the role of network topology is crucial in the statistics of rubbers. This is seen mostly on the influence of heterogeneities of crosslink density, which is also of practical interest. Strong heterogeneities lower the modulus, but do not change the stress-strain behavior. It is also shown that if in a given network the flexible chains are replaced by rigid rods which are flexibly hinged the entropy is still of order of that of the flexible network and that the modulus is of the same order as the Gaussian one. This holds for lower functionalities, i.e. less than six. The phase behavior of crosslinked blends and interpenetrating networks is shown to be similar of the microphase transition of block copolymers. Semi IPNs demix at a critical crosslink density of the crosslinked part, or alternatively at a critical length of the free chains, even though if the chains are of the same chemical nature as the crosslink component.