A multiscale decomposition method for the optimal planning and scheduling of multi-site continuous multiproduct plants

Terrazas-Moreno, Sebastian; Grossmann, Ignacio E.

doi:10.1016/j.ces.2011.03.017

Cited by 57 publications

(32 citation statements)

References 21 publications

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“…Note that the domain of the constraints is limited to the current SKU (constraints (24), (27)-(33)), to the product/mixing/packing family to which the current SKU belongs (constraints (20)- (23)), or to the ingredients which are consumed in the production of the current SKU ( (17) and (19) …”

Section: Appendix Amentioning

confidence: 99%

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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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Section: Appendix Amentioning

confidence: 99%

“…Whether the problem contains 10 or 100 SKUs, each submodel contains approximately 6917 constraints, 20,593 continuous variables and 208 binary variables. As a result, the only difference between cases with 10 and 100 SKUs is that the number of submodels increases by a factor 10.…”

Section: Required Cpu Timementioning

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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“…(Erdirik-Dogan, Grossmann, & Wassick, 2007;Kopanos, Puigjaner, & Georgiadis, 2009;Terrazas-Moreno & Grossmann, 2011). This solution strategy is based on the decomposition of the problem into upper and lower level problems.…”

Section: Bi-level Decompositionmentioning

confidence: 99%

“…for an existing facility or network (i.e. within the flexibility allowed by a given design) is defined as planning problem (Erdirik-Dogan & Grossmann, 2008;Ierapetritou, Pistikopoulos, & Floudas, 1996;Terrazas-Moreno & Grossmann, 2011).…”

Section: Model Selection Development and Validationmentioning

confidence: 99%

A systematic framework for enterprise-wide optimization: Synthesis and design of processing networks under uncertainty

Quaglia

Sarup²,

Sin

et al. 2013

Computers & Chemical Engineering

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“…Integrated optimization of production planning and scheduling has become a focus in industrial production process operations management research [6][7][8], and this paper focuses on integrated production planning and scheduling optimization of discrete manufacturing. The simultaneous problem SLSSP and scheduling of lot-sizing is similar to the integration of production planning and scheduling.…”

Section: Introductionmentioning

confidence: 99%

Integrated Optimization Model for Production Planning and Scheduling with Logistics Constraints

Chen¹

2016

Int. j. simul. model.

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Production planning and the development of manufacturing production scheduling are two important operational management tasks. The results of these tasks will strongly influence the development of corporate profits, as well as the efficiency of utilization of resources. Considering the production scheduling problems in production plans can effectively avoid conflicts between the specific implementation production plan and the actual situations, and ultimately maximize resource utilization efficiency. This paper attempts to establish an integrated optimization model of production planning and scheduling with consideration of logistics constraints. A modified heuristic algorithm is proposed to solve this problem. In the final portion of this paper, a simulation analysis is given to demonstrate the outstanding advantage of the model and the algorithm, which gives further thought for the production planning.

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A multiscale decomposition method for the optimal planning and scheduling of multi-site continuous multiproduct plants

Cited by 57 publications

References 21 publications

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

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

A systematic framework for enterprise-wide optimization: Synthesis and design of processing networks under uncertainty

Integrated Optimization Model for Production Planning and Scheduling with Logistics Constraints

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