Enterprise‐wide optimization: A new frontier in process systems engineering

Grossmann, Ignacio E.

doi:10.1002/aic.10617

Cited by 465 publications

(274 citation statements)

References 73 publications

(47 reference statements)

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“…In order to make this integration efficient for solving real-world problems, special-purpose models and decomposition algorithms need to be investigated. Reviews by [1] and [2] provide more details on the relevance of EWO activities in academic research and industrial practice. The focus of this work is on integrated planning and scheduling for batch processes.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Bilevel-Lagrangean Decomposition Scheme for the Integration of Planning and Scheduling of a Network of Batch Plants

Calfa

Agarwal

Grossmann

et al. 2013

Ind. Eng. Chem. Res.

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Motivated by a real-world industrial problem, this work deals with the integration of planning and scheduling in the operation of a network of batch plants. The network consists of single-stage, multiproduct batch plants located in different sites, which can exchange intermediate products in order to blend them to obtain finished products. The time horizon is given and divided into multiple time periods, at the end of which the customer demands have to be exactly satisfied. The planning model is a simplified and aggregate formulation derived from the detailed precedence-based scheduling formulation. Traveling Salesman Problem (TSP) constraints are incorporated at the planning level in order to predict the sequence-dependent changeovers between groups of products, within and across time periods, without requiring the detailed timing of operations, which is performed at the scheduling level. In an effort to avoid solving the full-space, rigorous scheduling model, especially for large problem sizes, two decomposition strategies are investigated: Bilevel and Temporal Lagrangean. We demonstrate that Bilevel Decomposition is efficient for small to medium problem instances and that further decomposition of the planning problem, yielding a hybrid decomposition scheme, is advantageous for tackling a large-scale industrial test case. ysl ill t 0-1 variable to force only one plant l to ship product i to customer l at time period t Notation Planning Model Variablesyg gult 0-1 variable to denote the assignment of group g to unit u of plant l at time period t ygf gult 0-1 variable to denote the first group g assigned to unit u of plant l at time period t ygl gult 0-1 variable to denote the last group g assigned to unit u of plant l at time period t yp iult 0-1 variable to denote the assignment of product i to unit u of plant l at time period t zg gg ult 0-1 variable to denote if group g is followed by group g in unit u of plant l within time period t zzg gg ult 0-1 variable to denote if link between group g and group g in unit u of plant l within time period t is broken zzzg gg ult 0-1 variable to denote a changeover between group g and group g in unit u of plant l across time period t Unit-Specific General Precedence Model VariablesT e iult End time of product i in unit u of plant l at time period t T s iult Start time of product i in unit u of plant l at time period t 4 wp ii ult 0-1 variable to denote changeover between product i and product i in unit u of plant l across time period t xp ii ult 0-1 variable to denote local precedence between product i and product i in unit u of plant l within time period t yp iult 0-1 variable to denote the assignment of product ito unit u of plant l at time period t ypf iult 0-1 variable to denote the first product i assigned to unit u of plant l at time period t ypl iult 0-1 variable to denote the last product i assigned to unit u of plant l at time period t zp ii ult 0-1 variable to denote global precedence between product i and product i in unit u of plant l within time period t

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Section: Introductionmentioning

confidence: 99%

Hybrid Bilevel-Lagrangean Decomposition Scheme for the Integration of Planning and Scheduling of a Network of Batch Plants

Calfa

Agarwal

Grossmann

et al. 2013

Ind. Eng. Chem. Res.

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“…Existing methods to solve MINLP optimization problems (such as various forms of branch and bound, outer approximation, and generalized Benders decomposition methods) are generally suitable only for small-to-medium size problems because the computational requirement grows exponentially as the number of variables increase. [8] Thus, when it is possible, converting the MINLP problem into a problem that is easier to solve can potentially reduce computation times. For instance, Bournazou et al…”

Section: Introductionmentioning

confidence: 99%

“…One of the most important challenges in supply chain optimization is the development of an appropriate process model. [8] Often, constructing rigorous models for chemical engineering applications such as the planning and scheduling of manufacturing facilities leads to a mixed-integer nonlinear programming (MINLP) problem formulation, where process flow variables specified in the form of continuous variables and sequencing decision variables could be specified in the form of integer variables. Existing methods to solve MINLP optimization problems (such as various forms of branch and bound, outer approximation, and generalized Benders decomposition methods) are generally suitable only for small-to-medium size problems because the computational requirement grows exponentially as the number of variables increase.…”

Section: Introductionmentioning

confidence: 99%

Supply chain optimization of flare‐gas‐to‐butanol processes in alberta

Hoseinzade

Adams

2016

Can J Chem Eng

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In this work, the economic feasibility of combining a novel portable gas-to-methanol process with a novel methanol-to-butanol process is examined. The gas-to-methanol process converts waste flare gas into methanol using a series of truck-mounted devices deployed at oil production wellheads. The methanol-to-butanol process uses a new proprietary catalyst which produces butanol via a diketene intermediate at a large centralized facility. The goal of this work is to identify the best ways of commercializing this technology in Alberta. To do this, a supply chain optimization model is formulated which considers specifically how many gas-to-methanol trucks should be used and where specifically in Alberta they should be deployed, the specific suppliers of CO2 to use, where the location of the central methanol-to-butanol facility should be located, and the costs of transportation of materials between locations. The model framework also considers the possibility of getting methanol in full or in part by alternative means such as producing methanol from conventional pipeline natural gas, or purchasing methanol from petrochemical or biomass-based routes. The supply chain optimization problem is formulated as an NLP and BARON is used in a Pareto analysis considering weighted combinations of economic and environmental objective functions. The resulting analysis provides a variety of possible viable strategies which can provide both profitability and reduced environmental emissions in Alberta by using a combination of the novel portable flare gas capture devices with more conventional gas-to-liquids technologies.

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“…(Varma et al, 2007) Recently, the operation of enterprise-wide supply chains has attracted much interest. Grossmann (2005) and Varma et al (2007) review the current research on Enterprise-wide Optimization (EWO), and they identify challenges and research opportunities. One of the main challenges is the integration of decision-making across various layers.…”

Section: Introductionmentioning

confidence: 99%

An SKU decomposition algorithm for the tactical planning in the FMCG industry

Elzakker¹,

Zondervan²,

Raikar³

et al. 2014

Computers & Chemical Engineering

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In this paper we address the optimization of the tactical planning for the Fast Moving Consumer Goods (FMCG) industry, in which numerous trade-offs need to be considered over possibly thousands of Stock-Keeping Units (SKUs). An MILP model for the optimization of this tactical planning problem is proposed. This model is demonstrated for a case containing 10 SKUs, but is intractable for realistically sized problems. Therefore, a decomposition algorithm based on decomposing the model into single-SKU submodels is proposed in this paper. To account for the interaction between SKUs, slack variables are introduced into the capacity constraints. These slack variables initially allow the capacity to be violated. In an iterative procedure the cost of violating the capacity is slowly increased, and eventually a feasible solution is obtained. Even for the relatively small 10 SKU case, the required CPU time could be reduced from 4427s to 472s using the algorithm. Moreover, the algorithm was used to optimize cases of up to 1000 SKUs, whereas the full model is intractable for cases of 25 or more SKUs. The solutions obtained with the algorithm are typically within a few percent of the global optimum.Keywords: Tactical Planning, Optimization, MILP, Decomposition Algorithm, Fast Moving Consumer Goods IntroductionThe scale and complexity of enterprise-wide supply chains has increased significantly due to globalization. (Varma et al., 2007) Recently, the operation of enterprise-wide supply chains has attracted much interest. Grossmann (2005) and Varma et al. (2007) review the current research on Enterprise-wide Optimization (EWO), and they identify challenges and research opportunities. One of the main challenges is the integration of decision-making across various layers. This includes the integration of the various echelons of the supply chain and the integration of the various temporal decisions layers. The decisions on the various layers are often interconnected leading to trade-offs between these decisions. (Maravelias and Sung, 2009) Therefore, better solutions can be obtained if these decisions are optimized simultaneously.Usually, three temporal decision layers are distinguished: strategic planning, tactical planning and operational planning. Strategic planning covers the long-term decisions regarding the design of the supply chain. Tactical planning covers the medium-term decisions regarding the allocation of capacity. Operational planning covers the short-term scheduling decisions.Maravelias and Sung (2009) review the integration of short-term scheduling and tactical production planning. They identify two options for this integration. First, the detailed scheduling decisions can directly be included into the tactical planning model. While this would in theory yield optimal solutions, the resulting models are usually very large and difficult to solve.

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Enterprise‐wide optimization: A new frontier in process systems engineering

Abstract: (purchasing, manufacturing, distribution, sales), across various geographically distributed organizations (vendors, facilities and markets), and across various levels of decision-making (strategic, tactical and operational).

Cited by 465 publications

References 73 publications

Hybrid Bilevel-Lagrangean Decomposition Scheme for the Integration of Planning and Scheduling of a Network of Batch Plants

Hybrid Bilevel-Lagrangean Decomposition Scheme for the Integration of Planning and Scheduling of a Network of Batch Plants

Supply chain optimization of flare‐gas‐to‐butanol processes in alberta

An SKU decomposition algorithm for the tactical planning in the FMCG industry

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