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February 11, 2011

"Team Develops Computational Strategies for Optimal Planning of Inventory and Distribution of Industrial Gases"

Developing effective strategies for optimizing industrial gas distribution systems raises numerous challenges; short-term distribution planning decisions must be balanced with long-term inventory decisions. In addition, uncertainties arising from demand fluctuations and the losses or gains of customers in the distribution network may significantly affect the decision-making across the industrial gas supply chains.

Researchers at Argonne National Laboratory, Carnegie Mellon University, and Praxair Inc. (aworldwide provider of industrial gases) have developed a stochastic mixed-integer nonlinear programming (MINLP) mixed-integer linear programming (MILP) model to address these challenges. To handle the significant computational complexity that arises, the team developed a continuous approximation approach, which estimates the operational cost at the strategic level and determines the tradeoff with the capital cost from tank sizing. A tailored branch-and-refine algorithm based on successive piece-wise linear approximation was also developed to globally optimize the stochastic MINLP problems.

Case studies with up to 200 customers—and involving as many as 42,230 binary variables, 12,262 continuous variables, and 17,394 constraints—show the effectiveness of the approach in solving the distribution-inventory planning problem of large-scale industrial gas supply chains.

 You, Fengqi; Pinto, Jose M.; Capón, Elisabet; Grossmann, Ignacio E.; Arora, Nikhil; Megan, Larry, “Optimal Distribution-Inventory Planning of Industrial Gases: I. Fast Computational Strategies for Large-Scale Problems,” Industrial & Engineering Chemistry Research, in press. DOI: 10.1021/ie1017578

You, Fengqi; Pinto, Jose M.; Grossmann, Ignacio E.; Megan, Larry. “Optimal Distribution-Inventory Planning of Industrial Gases: II. MINLP Models and Algorithms for Stochastic Cases,” Industrial & Engineering Chemistry Research, in press. DOI: 10.1021/ie101758u

 

 


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