Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: The concept of the satisfaction functions is exploited to explicitly integrate the decision-maker's preferences in the SGP model to deal with probabilistic decision-making situations.
106 citations
TL;DR: This work addresses the strategic planning of integrated bioethanol–sugar supply chains under uncertainty in the demand through a multi-scenario mixed-integer linear programming problem and demonstrates the capabilities of this approach through a case study based on the Argentinean sugarcane industry.
Abstract: In this paper, we address the strategic planning of integrated bioethanol–sugar supply chains (SC) under uncertainty in the demand. The design task is formulated as a multi-scenario mixed-integer linear programming (MILP) problem that decides on the capacity expansions of the production and storage facilities of the network over time along with the associated planning decisions (i.e., production rates, sales, etc.). The MILP model seeks to optimize the expected performance of the SC under several financial risk mitigation options. This consideration gives a rise to a multi-objective formulation, whose solution is given by a set of network designs that respond in different ways to the actual realization of the demand (the uncertain parameter). The capabilities of our approach are demonstrated through a case study based on the Argentinean sugarcane industry. Results include the investment strategy for the optimal SC configuration along with an analysis of the effect of demand uncertainty on the economic performance of several biofuels SC structures.
106 citations
TL;DR: A previously developed appointment scheduling model is extended to formulate a model based on a two-stage stochastic mixed integer program for optimizing booking and appointment times in the presence of uncertainty to maximize expected profit.
106 citations
TL;DR: The multi-period mean–variance optimization framework is extended to worst-case design with multiple rival return and risk scenarios and the ex-ante performance of min–max models is tested using historical data and backtesting results are presented.
106 citations
TL;DR: In this paper, the authors assess the potential for flexible network technologies, such as phase-shifting transformers and non-network solutions, to constitute valuable interim measures within a long-term planning strategy.
Abstract: Significant uncertainty surrounds the future development of electricity systems, primarily in terms of size, location and type of new renewable generation to be connected. In this paper we assess the potential for flexible network technologies, such as phase-shifting transformers, and non-network solutions, such as energy storage and demand-side management, to constitute valuable interim measures within a long-term planning strategy. The benefit of such flexible assets lies not only in the transmission services provided but also in the way they can facilitate and de-risk subsequent decisions by deferring commitment to capital-intensive projects until more information on generation development becomes available. A novel stochastic formulation for transmission expansion planning is presented that includes consideration of investment in these flexible solutions. The proposed framework is demonstrated with a case study on the IEEE-RTS where flexible technologies are shown to constitute valuable investment options when facing uncertainties in future renewable generation development.
106 citations