Deterministic Approximation of Stochastic Programming Problems with Probabilistic Constraints

Kan, Yuri S.; Васильева, С. Н.

doi:10.1007/978-3-030-33394-2_38

Cited by 1 publication

(1 citation statement)

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“…The main mathematical tool to deal with this problem is optimization under uncertainty. This wide argument has been approached with different solution frameworks, including deterministic approximation [8], heuristic based on the problem structure [9,10]), exact method [11], and robust methods [12]. In the broad field of applications that involve stochastic optimization, we are interested in the supply chain.…”

Section: Literature Reviewmentioning

confidence: 99%

A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry

et al. 2022

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This paper deals with the optimization problem faced by the manufacturing engineering department of an international automotive company, concerning its supply chain design (i.e., decisions regarding which plants to open, how many components to produce, and the logistic flow from production to assembly plants). The intrinsic characteristics of the problem, such as stochasticity, the high number of products and components, and exogenous factors, make it complex to formulate and solve the mathematical models. Thus, new decision support systems integrating human choices and fast solution algorithms are needed. In this paper, we present an innovative and successful use case of such an approach, encompassing the decision-maker as an integral part of the optimization process. Moreover, the proposed approach allows the managers to conduct what-if analyses in real-time, taking robust decisions with respect to future scenarios, while shortening the time needed. As a byproduct, the proposed methodology requires neither the definition of a probability distribution nor the investigation of the user’s risk aversion.

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