Stochastic modeling of fuel procurement for electricity generation with contractual terms and logistics constraints

Zimberg, Bernardo; Testuri, Carlos E.; Ferrari, Germán

doi:10.1016/j.compchemeng.2018.12.021

Cited by 7 publications

(1 citation statement)

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“…The motivation of the present work is a real problem arising in an oil company that deals with fuel acquisition under contractual and logistic conditions for electric power generation. The demand that the company faces is uncertain, given that thermal electricity generation, as a complement in an electrical system, is highly dependent on renewable sources [23,29]. While the particular situation of the company focuses on fuel procurement for thermal generation, this paper discusses the formulation and solution of a more general variant of the problem core, which can represent situations in which a product procurement is carried out by selecting distinguishable discrete supply options in the context of uncertain demand.…”

Section: Introductionmentioning

confidence: 99%

A multistage stochastic lot-sizing problem with cancellation and postponement under uncertain demands

2021

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A multistage stochastic capacitated discrete procurement problem with lead times, cancellation and postponement is addressed. The problem determines the procurement of a product under uncertain demand at minimal expected cost during a time horizon. The supply of the product is made through the purchase of optional distinguishable orders of fixed size with delivery time. Due to the unveiling of uncertainty over time it is possible to make cancellation and postponement corrective decisions on order procurement. These decisions involve costs and times of implementation. A model of the problem is formulated as an extension of a discrete capacitated lot-sizing problem under uncertain demand and lead times through a multi-stage stochastic mixed-integer linear programming approach. Valid inequalities are generated, based on a conventional inequalities approach, to tighten the model formulation. Experiments are performed for several problem instances with different uncertainty information structure. Their results allow to conclude that the incorporation of a subset of the generated inequalities favor the model solution.

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Section: Introductionmentioning

confidence: 99%