Supply Chain Optimization in Continuous Flexible Process Networks

Bok, Jin-Kwang; Grossmann, Ignacio E.; Park, Sunwon

doi:10.1021/ie990526w

“…Special cuts are added to the master problem to accelerate the convergence. Similar bi-level decomposition schemes have been developed by Bok et al (2000) for supply chain optimization problem, and by Erdirik-Dogan and for the simultaneous planning and scheduling of single-stage continuous multiproduct plants. Finally, another major decomposition approach relies on a rolling horizon strategy in which multiperiod problems are solved by recursively applying a more detailed model in the first time period and a simpler aggregate problem in the remaining time periods (Bassett et al, 1996;Dimitriadis et al, 1997).…”

Section: Decomposition Techniquesmentioning

confidence: 99%

Advances in mathematical programming models for enterprise-wide optimization

Grossmann

¹

2012

Computers & Chemical Engineering

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Enterprise-wide optimization (EWO) is an area that lies at the interface of chemical engineering and operations research, and has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the operations of supply, manufacturing and distribution activities of a company to reduce costs, inventories and environmental impact, and to maximize profits and responsiveness. A major focus in EWO is the optimal operation of manufacturing facilities that often require the use of nonlinear process models. Major operational items include planning, scheduling, real-time optimization and control. We provide an overview of EWO in terms of a mathematical programming framework. We first provide a brief overview of mathematical programming techniques (mixed-integer linear and nonlinear optimization methods), as well as decomposition methods, stochastic programming and modeling systems. We then address some of the major issues involved in the modeling and solution of these problems. Finally, based on the EWO program at the Center of Advanced Process Decision-making at Carnegie Mellon, we describe several applications to show the potential of this area. Keywords

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“…[25][26][27][28][29] If the supply chain planning problem includes both strategic and operational decisions, bilevel decomposition 21 can be implemented to iteratively solve an upper level aggregated model and a lower level detailed model. [30][31][32] Hierarchical decomposition 22 is effective for supply chain optimizatoin problems with multiple levels of decision-making. [33][34][35][36] .…”

Section: Literature Reviewmentioning

confidence: 99%

“…In particular, the nonlinear nonconvex terms in this model appear in constraints (30), (36) - (38) and (44). In this section, we perform exact linearizations to reformulate the MINLP model into an MILP by introducing additional variables and constraints.…”

Section: Milp Reformulationmentioning

confidence: 99%

Optimal Distribution-Inventory Planning of Industrial Gases. II. MINLP Models and Algorithms for Stochastic Cases

You

¹

,

Pinto²,

Grossmann

³

et al. 2011

Ind. Eng. Chem. Res.

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In this paper, we address the optimization of industrial gas distribution systems, which consist of plants and customers, as well as storage tanks, trucks and trailers. A mixedinteger linear programming (MILP) model is presented to minimize the total capital and operating cost, and to integrate short-term distribution planning decisions for the vehicle routing with long-term inventory decisions for sizing storage tanks at customer locations. In order to optimize asset allocation in the industrial gas distribution network by incorporating operating decisions, the model also takes into account the synergies among delivery schedule, tank sizes, customer locations and inventory profiles. To effectively solve large scale instances, we propose two fast computational strategies. The first approach is a two-level solution strategy based on the decomposition of the full scale MILP model into an upper level route selectiontank sizing model and a lower level reduced routing model. The second approach is based on a continuous approximation method, which estimates the operational cost at the strategic level and determines the tradeoff with the capital cost from tank sizing.Three cases studies including instances with up to 200 customers are presented to illustrate the applications of the models and the performance of the proposed solution methods.

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“…Note that constraints (30) and (36) - (38) are nonlinear constraints with nonconvex terms, but they can all be exactly linearized as discussed in Section 5.6.…”

Section: Figure 3 Inventory Profile Of a Customer Under Cyclic Invenmentioning

confidence: 99%

Optimal Distribution-Inventory Planning of Industrial Gases. I. Fast Computational Strategies for Large-Scale Problems

You

¹

,

Pinto²,

Capón

³

et al. 2011

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

In this paper, we address the optimization of industrial gas distribution systems, which consist of plants and customers, as well as storage tanks, trucks and trailers. A mixedinteger linear programming (MILP) model is presented to minimize the total capital and operating cost, and to integrate short-term distribution planning decisions for the vehicle routing with long-term inventory decisions for sizing storage tanks at customer locations. In order to optimize asset allocation in the industrial gas distribution network by incorporating operating decisions, the model also takes into account the synergies among delivery schedule, tank sizes, customer locations and inventory profiles. To effectively solve large scale instances, we propose two fast computational strategies. The first approach is a two-level solution strategy based on the decomposition of the full scale MILP model into an upper level route selectiontank sizing model and a lower level reduced routing model. The second approach is based on a continuous approximation method, which estimates the operational cost at the strategic level and determines the tradeoff with the capital cost from tank sizing.Three cases studies including instances with up to 200 customers are presented to illustrate the applications of the models and the performance of the proposed solution methods.

show abstract

Supply Chain Optimization in Continuous Flexible Process Networks

Cited by 99 publications

References 20 publications

Advances in mathematical programming models for enterprise-wide optimization

Advances in mathematical programming models for enterprise-wide optimization

Optimal Distribution-Inventory Planning of Industrial Gases. II. MINLP Models and Algorithms for Stochastic Cases

Optimal Distribution-Inventory Planning of Industrial Gases. I. Fast Computational Strategies for Large-Scale Problems

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