Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty

Bio-fuels represent promising candidates for renewable liquid fuels. One of the challenges for the emerging industry is the high level of uncertainty in supply amounts, market demands, market prices, and processing technologies. These uncertainties complicate the assessment of investment decisions. This paper presents a model for the optimal design of biomass supply chain networks under uncertainty. The uncertainties manifest themselves as a large number of stochastic model parameters that could impact the overall profitability and design. The supply chain network we study covers the Southeastern region of the United States and includes biomass supply locations and amounts, candidate sites and capacities for two kinds of fuel conversion processing, and the logistics of transportation from the locations of forestry resources to the conversion sites and then to the final markets.To reduce the design problem to a manageable size the impact of each uncertain parameter on the objective function is computed for each end of the parameter's range. The parameters that cause the most change in the profit over their range are then combined into scenarios that are used to find a design through a two stage mixed integer stochastic program. The first stage decisions are the capital investment decisions including the size and location of the processing plants. The second stage recourse decisions are the biomass and product flows in each scenario. The objective is the maximization of the expected profit over the different scenarios. The robustness and global sensitivity analysis of the nominal design (for a single nominal scenario) vs. the robust design (for multiple scenarios) are analyzed using Monte Carlo simulation over the hypercube formed from the parameter ranges.     

Framework for margins-based planning: Forest biorefinery case study

The biorefinery concept offers a promising solution to transform the struggling forestry industry. Not only will the implementation of new products and processes help to diversify revenues, it will also offer an opportunity to change the manufacturing culture by better managing the flexibility of assets to react to volatile market conditions. In this paper, an integrated supply-chain planning framework is presented. It is based on optimizing a superstructure to help decision makers identify different supply-chain policies to adapt to different market conditions. It integrates revenue management concepts, activity-based cost accounting principles, manufacturing flexibility and supply-chain flexibility in a tactical model to maximize profit in a price-volatile environment. A case study of a newsprint mill implementing a parallel biomass fractionation line producing several biochemicals is used to illustrate this approach. Results and benefits are presented for the traditional pulp and paper business and for the transformed biorefinery in different market scenarios.     

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.     

Multiobjective supply chain design under uncertainty

In this article, the design and retrofit problem of a supply chain (SC) consisting of several production plants, warehouses and markets, and the associated distribution systems, is considered. The first problem formulation modifies and extends other previously presented models, in order to include several essential characteristics for realistically representing the consequences of design decisions on the SC performance. Then, in order to take into account the effects of the uncertainty in the production scenario, a two-stage stochastic model is constructed. The problem objective, i.e., SC performance, is assessed by taking into account not only the profit over the time horizon, but also the resulting demand satisfaction. This approach can be used to obtain different kinds of solutions, that may be valuable at different levels. On one hand, the SC configurations obtained by means of deterministic mathematical programming can be compared with those determined by different stochastic scenarios representing different approaches to face uncertainty. Additionally, this approach enables to consider and manage the financial risk associated to the different design options, resulting in a set of Pareto optimal solutions that can be used for decision-making.     

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.     

Robust supply chain performance via Model Predictive Control

This paper presents a novel robust Model Predictive Control (MPC) method for real-time supply chain optimization under uncertainties. This method optimizes the closed-loop economic performance of supply chain systems and addresses different sources of uncertainties located external to and within the feedback loop. The future system behavior is predicted by a closed-loop model, which includes both the open-loop system model and a controller model described by an optimization problem. The robust MPC formulation involves the solution of a constrained, bi-level stochastic optimization problem, which is transformed into a tractable problem involving a limited number of deterministic conic optimization problems solved reliably using an interior point method. The robust controller is applied to a real industrial multi-echelon supply chain optimization problem, and its performance is shown to reduce stock-outs without excessive inventories.     

A multi-level simulation approach for the crude oil loading/unloading scheduling problem

An integrated approach for refinery production scheduling and unit operation optimization problems is presented. Each problem is at a different decision making layer and has an independent objective function and model. The objective function at the operational level is an on-line maximization of the difference between the product revenue and the energy and environmental costs of the main refinery units. It is modeled as an NLP and is constrained by ranges on the unit's operating condition as well as product quality constraints. The production scheduling layer is modeled as an MILP with the objective of minimizing the logistical costs of unloading the crude oil over a day-to-week time horizon. The objective function is a linear sum of the unloading, sea waiting, inventory, and setup costs. The nonlinear simulation model for the process units is used to find optimized refining costs and revenue for a blend of two crudes. Multiple linear regression of the individual crude oil flow rates within the crude oil percentage range allowed by the facility is then used to derive linear refining cost and revenue functions. Along with logistics costs, the refining costs or revenue are considered in the MILP scheduling objective function. Results show that this integrated approach can lead to a decrease of production and logistics costs or increased profit, provide a more intelligent crude schedule, and identify production level scheduling decisions which have a tradeoff benefit with the operational mode of the refinery.     

From PSE to PSE—Decision support for resilient enterprises

In recent years, the process systems engineering (PSE) community has recognized the need to address chemical enterprises comprising globally distributed, but strongly interacting, facilities. We examine this extension of PSE, which we call the PSE of enterprise (PSE²), as it relates to the five traditional PSE areas of system representation, modeling and simulation, synthesis and design, planning and scheduling, and control and supervision. We illustrate the strong structural, operational, and methodological parallels between PSE and PSE² in this study.     

Integration of production planning and scheduling: Overview, challenges and opportunities

We review the integration of medium-term production planning and short-term scheduling. We begin with an overview of supply chain management and the associated planning problems. Next, we formally define the production planning problem and explain why integration with scheduling leads to better solutions. We present the major modeling approaches for the integration of scheduling and planning decisions, and discuss the major solution strategies. We close with an account of the challenges and opportunities in this area.     

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 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.     

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.     

Integration of enterprise levels based on an ontological framework

Enterprises are highly complex systems in which one or more organizations share a definite mission, goals and objectives to offer a product or service. In this study, an ontological framework is built as a mechanism for exchanging information and knowledge models for multiple applications and effective integration between hierarchical levels. The potential of the general semantic framework that is developed is demonstrated using a case study concerning the enterprise supply chain network design-planning problem.     

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.     

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.