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

You, Fengqi; Pinto, José M.; Capón, Elisabet; Grossmann, Ignacio E.; Arora, Nikhil; Megan, Larry

doi:10.1021/ie1017578

Cited by 38 publications

(31 citation statements)

References 48 publications

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“…Also, You et al (2011) developed a mixed-integer linear programming model to integrate long term planning decisions of sizing storage tanks at customer locations with truck routing decisions at the operational level. They also propose two efficient computational methods in order to solve large-scale instances, one based on a two-level decomposition strategy and the other on a continuous approximation approach for the routing decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Simultaneous production and distribution of industrial gas supply-chains

Marchetti

Gupta

Grossmann

et al. 2014

Computers & Chemical Engineering

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In this paper, we propose a multi-period mixed-integer linear programming model for optimal enterpriselevel planning of industrial gas operations. The objective is to minimize the total cost of production and distribution of liquid products by coordinating production decisions at multiple plants and distribution decisions at multiple depots. Production decisions include production modes and rates that determine power consumption. Distribution decisions involve source, destination, quantity, route, and time of each truck delivery. The selection of routes is a critical factor of the distribution cost. The main goal of this contribution is to assess the benefits of optimal coordination of production and distribution. The proposed methodology has been tested on small, medium, and large size examples. The results show that significant benefits can be obtained with higher coordination among plants/depots in order to fulfill a common set of shared customer demands. The application to real industrial size test cases is also discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Simultaneous production and distribution of industrial gas supply-chains

Marchetti

Gupta

Grossmann

et al. 2014

Computers & Chemical Engineering

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“…All the trucks are assumed to have the same average traveling speed and a maximum number of working hours per day. You et al (2011) presented a simultaneous MILP model 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. To circumvent the largescale MILP model, these authors proposed a strategy that employs a continuous approximation (Langevin et al, 1996) to estimate the annual delivery cost without considering the detailed schedules of the routing problem.…”

Section: Figure 7 Optimal Production Schedulementioning

confidence: 99%

Advances in mathematical programming models for enterprise-wide optimization

Grossmann

2012

Computers & Chemical Engineering

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187

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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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“…In industrial gas distribution, liquefied gases [e.g., oxygen (O 2 ), nitrogen (N 2 ), argon (Ar), carbon dioxide (CO 2 ), and hydrogen (H 2 )] are transported from production plants to cryogenic storage tanks placed at customer sites using cryogenic trailers. This is primarily done through a VMI system (You et al, 2011). In a VMI system, it is the vendor's responsibility to prevent customer stockout of the product and avoid penalty consequences.…”

Section: Introductionmentioning

confidence: 99%