Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.     

Supply chain optimisation for the process industries: Advances and opportunities

Supply chain management and optimisation is a critical aspect of modern enterprises and a flourishing research area. This paper presents a critical review of methodologies for enhancing the decision-making for process industry supply chains towards the development of optimal infrastructures (assets and network) and planning. The presence of uncertainty within supply chains is discussed as an important issue for efficient capacity utilisation and robust infrastructure decisions. The incorporation of business/financial and sustainability aspects is also considered and future challenges are identified.     

Mathematical modeling for simultaneous design of plants and supply chain in the batch process industry

Most supply chain design models have focused on the integration problem, where links among nodes must be settled in order to allow an efficient operation of the whole system. At this level, all the problem elements are modeled like black boxes, and the optimal solution determines the nodes allocation and their capacity, and links among nodes. In this work, a new approach is proposed where decisions about plant design are simultaneously made with operational and planning decisions on the supply chain. Thus, tradeoffs between the plant structure and the network design are assessed. The model considers unit duplications and the allocation of storage tanks for plant design. Using different sets of discrete sizes for batch units and tanks, a mixed integer linear programming model (MILP) is attained. The proposed formulation is compared with other non-integrated approaches in order to illustrate the advantages of the presented simultaneous approach.     

The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure

In a global economy, the key to success is providing products around the world at the right time in the right quantity and quality, at a low cost. Efficient supply chains have an important role in guaranteeing this success. Optimized planning of such structures is required and uncertainties regarding product demands and prices, amongst other supply chain conditions, should also be considered. In this paper, we look into supply chain planning decisions that account for uncertainty on product portfolios demand and prices. A multi-period planning model is developed where the supply chain operational decisions on supply, production, transportation, and distribution at the actual period consider the uncertainty on products’ demand and prices. Different decision scenarios, involving the evaluation of the supply chain economical performance, are analyzed (e.g. global operating costs/profit realized) for different criteria on the importance of the partners within the global chain (i.e. partners’ structure). A Mixed Integer Linear Programming (MILP) formulation is formulated for each planning scenario and the optimal solution is reached using a standard Branch and Bound (B&B) procedure. The final results provide details on the supply chain partners production, transportation and inventory, at each planning period, while accounting for the importance of each partner in the global chain as well as demand/price uncertainties. The applicability of the developed formulation is illustrated through the solution of a real case-study involving an industrial chain in the pharmaceutical sector.     

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     

Optimal design of closed-loop supply chain networks with multifunctional nodes

This paper introduces a general mathematical programming framework that employs an innovative generalized supply chain network (SCN) composition coupled with forward and reverse logistics activities. Generalized echelon will have the ability to produce/distribute all forward materials/products and recover/redistribute simultaneously all the returned which are categorized with respect to their quality zone. The work addresses a multi-product, multi-echelon and multi-period Mixed-Integer Linear Programming (MILP) problem in a closed-loop supply chain network design solved to global optimality using standard branch-and-bound techniques. Further, the model aims to find the optimal structure of the network in order to satisfy market demand with the minimum overall capital and operational cost. Applicability and robustness of the proposed model are illustrated by using a medium real case study from a European consumer goods company whereas its benefits are valued through a comparison with a counterpart model that utilizes the mainstream fixed echelon network structure.     

The multi-echelon vehicle routing problem with cross docking in supply chain management

Multi-echelon distribution networks are quite common in supply chain and logistics. Deliveries of multiple items from factories to customers are managed by routing and consolidating shipments in warehouses carrying on long-term inventories. On the other hand, cross-docking is a logistics technique that differs from warehousing because products are no longer stored at intermediate depots. Instead, cross-dock facilities consolidate incoming shipments based on customer demands and immediately deliver them to their destinations. Hybrid strategies combining direct shipping, warehousing and cross-docking are usually applied in real-world distribution systems. This work deals with the operational management of hybrid multi-echelon multi-item distribution networks. The goal of the N-echelon vehicle routing problem with cross-docking in supply chain management (the VRPCD-SCM problem) consists of satisfying customer demands at minimum total transportation cost. A monolithic optimization framework for the VRPCD-SCM based on a mixed-integer linear mathematical formulation is presented. Computational results for several problem instances are reported.     

A control-relevant approach to demand modeling for supply chain management

The development of control-oriented decision policies for inventory management in supply chains has drawn considerable interest in recent years. Modeling demand to supply forecasts is an important component of an effective solution to this problem. Drawing from the problem of control-relevant parameter estimation, this paper presents an approach for demand modeling in a production-inventory system that relies on a specialized weight to tailor the emphasis of the fit to the intended purpose of the model, which is to provide forecasts to inventory management policies based on internal model control or model predictive control. A systematic approach to generate this weight function (implemented using data prefilters in the time domain) is presented and the benefits demonstrated on a series of representative case studies. The multi-objective formulation developed in this work allows the user to emphasize minimizing inventory variance, minimizing starts variance, or their combination, as dictated by operational and enterprise goals.     

An optimization framework for the integration of water management and shale gas supply chain design

This study presents the mathematical formulation and implementation of a comprehensive optimization framework for the assessment of shale gas resources. The framework simultaneously integrates water management and the design and planning of the shale gas supply chain, from the shale formation to final product demand centers and from fresh water supply for hydraulic fracturing to water injection and/or disposal. The framework also addresses some issues regarding wastewater quality, i.e., total dissolved solids (TDS) concentration, as well as spatial and temporal variations in gas composition, features that typically arise in exploiting shale formations. In addition, the proposed framework also considers the integration of different modeling, simulation and optimization tools that are commonly used in the energy sector to evaluate the technical and economic viability of new energy sources. Finally, the capabilities of the proposed framework are illustrated through two case studies (A and B) involving 5 well-pads operating with constant and variable gas composition, respectively. The effects of the modeling of variable TDS concentration in the produced wastewater is also addressed in case study B.     

Generalized capital investment planning of oil-refineries using MILP and sequence-dependent setups

Due to quantity times quality nonlinear terms inherent in the oil-refining industry, performing industrial-sized capital investment planning (CIP) in this field is traditionally done using linear (LP) or nonlinear (NLP) models whereby a gamut of scenarios are generated and manually searched to make expand and/or install decisions. Though mixed-integer nonlinear (MINLP) solvers have made significant advancements, they are often slow for large industrial applications in optimization; hence, we propose a more tractable approach to solve the CIP problem using a mixed-integer linear programming (MILP) model and input–output (Leontief) models, where the nonlinearities are approximated to linearized operations, activities, or modes in large-scaled flowsheet problems. To model the different types of CIP's known as revamping, retrofitting, and repairing, we unify the modeling by combining planning balances with scheduling concepts of sequence-dependent changeovers to represent the construction, commission, and correction stages explicitly in similar applications such as process design synthesis, asset allocation and utilization, and turnaround and inspection scheduling. Two motivating examples illustrate the modeling, and a retrofit example and an oil-refinery investment planning problem are also highlighted.     

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.     

Financial and financial engineering considerations in supply chain and product development pipeline management

Enterprise-wide decision problems are receiving increasing attention in the process systems engineering literature. In particular, the supply chain and product development pipeline management components of this general class of problems have been subjects of intensive research in both their deterministic and their stochastic forms. The supply chain management (SCM) problem has seen work largely focused on the process operations and logistics components while for the product development pipeline management (PDPM) problem much of the attention has been on MILP formulations addressing the consequences of product failure during its development. In their full realization, both are recognized as challenging stochastic multi-stage decision problems. In this paper we discuss three important aspects of these problems that require further research: the realistic representation of the financial components and appropriate criteria for this class of problems, strategic management of supplier and customer relationships through inventory management and option contracts, and innovative approaches to suitably value and integrate a broader range of decisions available to management. We highlight and extend relevant contributions and case examples drawn from the recent literature that are emerging on these topics and use this work to point out further challenges.     

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.     

Mathematical programming and game theory optimization-based tool for supply chain planning in cooperative/competitive environments

This work proposes to improve the tactical decision-making of a supply chain (SC) under an uncertain competition scenario through the use of different optimization criteria, which allows to manage not only the specific objectives of the SC of interest, but also the way how its clients address their selection between different potential suppliers, identifying best market share for the SC of interest and the strategy to attain it. The resulting multi-objective optimization problem has been solved using the ?-constraint method in order to approximate the Pareto space of non-dominated solutions while a framework based on game theory is used as a reactive decision making support tool to deal with the uncertainty of the competitive scenario. The use of the proposed system is illustrated through its application to a multi-product, multi-echelon supply chain case study, which is intended to cooperate or to compete with another SC of similar characteristics.     

Effect of FA chain length on normal- and reversed-phase HPLC of phospholipids

Forty-seven saturated synthetic diacyl PA, PC, PE, PG, and PS and five unsaturated diacyl phospholipids (PL) underwent normal- and reversed-phase (RP) HPLC with isocratic isopropanol/hexane/water (5∶4∶1) and methanol/chloroform/acetonitrile/water (79.5∶9∶8∶3.5) mobile phases, respectively. For normal-phase HPLC, capacity factors (k′ _i ) decrease with chain length (n) of the two identical PL FA residues, whereas the opposite occurs with RP (C₁₈)-HPLC. Plots of In k′ _i vs. n for individual PL classes are in general curved, violating the linear free-energy relationship. For PL of the same n but with different head groups, k′ _i with normal-phase HPLC varies as PE<PG<PA<PS<PC, except when n≥16, when the order is PE<PS≈PA≈PG<PC. For RP-HPLC, the order of k′ _i values is PG<_A≈PS≤PC≈PE until n≥16, when it is PA≈PG<PS≪PC≈PE. With normal-phase HPLC, k′ _i values of PL with unsaturated FA of n=18 are ordered as PE<PA<PC. Increasing degrees of unsaturation lead to increasing k′ _i .     

我国聚酯产业链供需现状及市场分析

介绍了我国聚酯产业链中的对二甲苯(PX)、精对苯二甲酸(PTA)、聚对苯二甲酸乙二醇酯(PET)的生产现状和消费情况;指出上游PX项目建设增速缓慢,PX供需缺口加大,2012年我国PX自给率约54%,消费量占全球的30%,生产能力占全球约30%,PX价格受制于国际市场,居高不下;PTA生产能力过剩,2012年我国PTA自给率已达78%,PTA价格无法向下游转移,2012年PTA装置平均开工率约73%;PET发展过快,2012年我国PET自给率为103.8%,生产能力约占全球的50%,应加大PET的差别化产品开发;采用一元线性回归法预测2017年我国PX,PTA,PET的需求量分别为17 800,27 000,31 300 kt。     

给排水工程课程设计教学改革的研究与实践

我们针对当前给排水工程课程设计中存在的不足,本文分析了课程设计改革的必要性,设计了课程设计的教学内容、教学体系、教学方法,指出了教师在指导过程中应实施“三抓三落实”,重点攻克“三难点”,加强“三互动”,践行“四能一严”。通过教学改革,学生设计积极性明显增加,工程实践能力有效增强,设计质量和学生满意度显著提升。     

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。     

Disinfecting the Coquitlam Water Supply: Ozone and UV Disinfection

The Greater Vancouver Water District (GVWD) is upgrading its unfiltered Coquitlam water treatment system to meet the updated Health Canada guidelines for providing 3-log reduction or inactivation of Cryptosporidium and Giardia. The existing Coquitlam system, which is designed for peak flows of 1200 ML/d (317 mgd), includes an ozonation facility providing 3 log Giardia inactivation, and a chlorination/corrosion control facility. To meet Health Canada guidelines, a UV disinfection facility will be constructed, which will provide 3-log inactivation of Cryptosporidium and Giardia, and the ozone dosage will be increased to reduce THM and HAA precursors as well as improve UV transmittance. This paper will focus on the conceptual design of the Coquitlam system and the preliminary bench-scale studies that were completed for GVWD as part of the predesign phase of the project.     

Analysis and comparison of single period single level and bilevel programming representations of a pre-existing timberlands supply chain with a new biorefinery facility

This research investigates the economic impact of a new biorefinery on an established timberlands system. This paper proposes that a single level model does not adequately represent the interactions in the supply chain. The timberlands system has two major decision makers: harvesters and manufacturers. Bilevel models are two-level turn-based models with each level representing a decision maker. The two levels are interconnected, but neither has control over the decisions of the other. The leader makes an initial decision, and the follower reacts; the leader can anticipate the follower's reaction. To demonstrate the value of this more complex representation, a single period bilevel and a single period single level model were formulated for a representative case study. The separation of objective functions in the bilevel problem yielded decisions different from those of the single level formulation. It was concluded that the bilevel model may more accurately represent the real system.