Some matheuristic algorithms for multistage stochastic optimization models with endogenous uncertainty and risk management

Escudero, Laureano F.; Garín, María Araceli; Monge, Juan F.; Unzueta, Aitziber

doi:10.1016/j.ejor.2020.02.046

Cited by 12 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since any organization is an open system, it is influenced by the external business environment. Such impacts can be unpredictable (in particular, force majeure) and lead to uncontrolled changes in company's activities [7]. Stochastic factor analysis is a method for solving a wide range of statistical estimation problems.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Implementation of Stochastic Analysis in Corporate Decision-Making Models

Liu

Tulenty

et al. 2021

Mathematics

View full text Add to dashboard Cite

The stochastic approach as a method for modeling factor systems of interrelationships of economic activity aspects allows minimizing managerial errors against the background of company growth and expansion of operating activities. The purpose of this study is to form a decision-making model to ensure the financial competitiveness of enterprises in the context of stochastic analysis. This study demonstrates stochastic analysis implementation in companies of the 2nd and 3rd degrees of internationalization based on multiple regression and factorial analysis of variance. The practical basis of the study was Chinese and Russian mining enterprises that enter highly competitive markets and therefore should avoid mistakes in decision-making as much as possible. The model of financial competitiveness proposed in the article demonstrates the best ways to introduce stochastics in companies to optimize their overall productivity, regardless of the country of origin. In a practical sense, research on reducing managerial mistakes allows enterprises to have financial success even in the turbulent conditions of today’s global market, regardless of the company’s jurisdiction.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED: Implementation of Stochastic Analysis in Corporate Decision-Making Models

Liu

Tulenty

et al. 2021

Mathematics

View full text Add to dashboard Cite

show abstract

“…Of particular concern can be the case of "black swan" scenarios (i.e., scenarios with a small probability of occurring and a very high total cost), which can motivate employing risk averse measures. We consider this as an important line of research for future work; in particular, it would be interesting to experiment with time-consistent risk measures as Expected Conditional Value-at-Risk [8] and Expected Conditional Stochastic Dominance [9].…”

Section: Discussionmentioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

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.

show abstract

“…The risk measures available in a two‐stage risk‐averse model include the variance, value at risk , and conditional value‐at‐risk (CVaR). Considering time consistency, the expected CVaR (ECVaR) (Homem‐de‐Mello and Pagnoncelli, 2016) and expected conditional stochastic dominance (ECSD) (Escudero et al., 2020) may be better choices as risk measures. Various quantitative and qualitative analyses of risk‐averse models have been presented, where quantitative analyses mainly consider the risk in the objective function and qualitative analyses involve the introduction of chance constraints to reflect risk‐averse decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Two‐stage distributionally robust optimization model for a pharmaceutical cold supply chain network design problem

Liu

Yang

2023

Int Trans Operational Res

View full text Add to dashboard Cite

Pharmaceutical safety has received increasing attention from governments and corporations, and building a safe and effective pharmaceutical cold supply chain network has become an important issue. This paper proposes a two‐stage pharmaceutical cold supply chain network design problem considering drug safety, in which the drug demand, transportation costs, and drug safety risk costs are assumed to be random variables. Due to the presence of uncertainty and the fact that information about the distribution of uncertain parameters is often only partially known, a distributionally robust optimization method is used to handle the uncertainty. A two‐stage distributionally robust optimization model is constructed, in which the reliability of the estimation of the demand necessary to satisfy the entire cold chain network is ensured to be greater than a certain predetermined level by introducing an ambiguous joint chance constraint. The decision process for the problem can be divided into strategic and operational decisions with the goal of minimizing the total costs related to facility construction, the purchase of raw materials, drug production, transportation, and safety risks. By introducing an ambiguity set with mean and covariance information to describe the uncertain parameters, the two‐stage model is eventually reformulated as a standard second‐order cone program, thus making it computationally tractable. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed models and optimization methods.

show abstract

Some matheuristic algorithms for multistage stochastic optimization models with endogenous uncertainty and risk management

Cited by 12 publications

References 42 publications

RETRACTED: Implementation of Stochastic Analysis in Corporate Decision-Making Models

RETRACTED: Implementation of Stochastic Analysis in Corporate Decision-Making Models

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

Two‐stage distributionally robust optimization model for a pharmaceutical cold supply chain network design problem

Contact Info

Product

Resources

About