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IMA Journal of Management Mathematics Advance Access originally published online on February 27, 2007
IMA Journal of Management Mathematics 2008 19(4):403-416; doi:10.1093/imaman/dpm004
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© The authors 2007. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

This article appears in the following IMA Journal of Management Mathematics issue: Special Issue Stochastic Programming [View the issue table of contents]

A stochastic optimization model for a gas sale company

E. Allevi

Department of Quantitative Methods, Brescia University, Contrada S. Chiara, 50, Brescia 25122, Italy

M. I. Bertocchi{dagger}

Department of Mathematics, Statistics, Computer Science and Applications, Bergamo University, Via dei Caniana 2, Bergamo 24127, Italy

M. Innorta and M. T. Vespucci

Department of Engineering and Management, Bergamo University, Via Marconi 5, Dalmine 24044, Italy

{dagger} Email: marida.bertocchi{at}unibg.it

Accepted on 30 January 2007.

In this paper, the authors develop a stochastic optimization model, named Optimization Modelling for Gas Seller (OMoGas), to assist companies dealing with gas retail commercialization. Stochasticity is due to the dependence of consumption on temperature uncertainty. Nonlinearities are present in both the objective function and the constraints. The model can be classified as a non-linear programming (NLP) mixed integer model, with the profit function depending on the number of contracts with the final consumers, the characteristics of such consumers and the cost supported to meet the final demand. Constraints related to a maximum daily gas consumption, yearly maximum and minimum consumption in order to avoid penalties and consumption profiles are included. The model is implemented in the General Algebraic Modeling System (GAMS) environment and the results obtained by the stochastic version, based on consumption scenarios, are compared with the deterministic solution.

Keywords: gas sale company; tariff components; stochastic programming


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