A hybrid path‐relinking method for solving two‐stage stochastic integer problems

Amorim, Pedro; Costa, Alysson M.; Almada-Lobo, Bernardo

doi:10.1111/itor.12084

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…See, for example, Amorim et al. (2015) for a path relinking method, Karademir et al. (2014) for a greedy heuristic, and Raba et al.…”

Section: Introductionmentioning

confidence: 99%

From scenarios to conditional scenarios in two‐stage stochastic MILP problems

Beltrán-Royo

2020

Int Trans Operational Res

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The conditional scenario (CS) approach was introduced as an effective approximation to the two-stage stochastic mixed-integer linear programming problem. Although the original definition of CS is general, in practice it is basically suitable for the multivariate normal distribution. In this paper, we propose a new definition of CS that is suitable for approximating any multivariate distribution (continuous or discrete). This definition allows the approximation of a potentially large set of scenarios using a small set of CSs, unlike the previous definition. In the computational study, dedicated to solving the portfolio optimization problem with hard real-world constraints, the CS approach has clearly outperformed the sample average approximation approach in terms of solution time.

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“…See, for example, Amorim et al. (2015) for a path relinking method, Karademir et al. (2014) for a greedy heuristic, and Raba et al.…”

Section: Introductionmentioning

confidence: 99%

From scenarios to conditional scenarios in two‐stage stochastic MILP problems

Beltrán-Royo

2020

Int Trans Operational Res

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“…In fact, SP has the advantage of being an intuitive modeling approach to generate solutions that are able to hedge against multiple outcomes. Although stochastic programming is sometimes criticized for being computationally prohibitive for large-scale problems, Graves (2011) argued that with the increase in computational technology and the development of efficient algorithms, this technique has been more exploited for production planning problems in recent years.Stochastic lot-sizing formulations are mostly derived from either two-stage (Amorim et al 2015;Hu and Hu 2016;Alem et al 2018) or multistage structures (Huang and Küçükyavuz 2008;Li and Thorstenson 2014;Koca et al 2018) in a risk-neutral (RN) perspec-tive, i.e., assuming that decision makers only focus on minimizing (maximizing) the expected cost (profit).…”

Section: Introductionmentioning

confidence: 99%

A practical assessment of risk-averse approaches in production lot-sizing problems

Alem

Oliveira

Carrión

2019

International Journal of Production Research

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This paper presents an empirical assessment of four state-of-the-art risk-averse approaches to deal with the capacitated lot-sizing problem under stochastic demand. We analyze two mean-risk models based on the semideviation and on the conditional value-at-risk risk measures, and alternate first and secondorder stochastic dominance approaches. The extensive computational experiments based on different instances characteristics and on a case-study suggest that CVaR exhibits a good tradeoff between risk and performance, followed by the semideviation and first-order stochastic dominance approach. For all approaches, enforcing risk-aversion helps to reduce the cost standard deviation substantially, which is usually accomplished via increasing production rates. Overall, we can say that very risk-averse decisionmakers would be willing to pay an increased price to have a much less risky solution given by CVaR. In less risk-averse settings, though, semideviation and first-order stochastic dominance can be appealing alternatives to provide significantly more stable production planning costs with a marginal increase of the expected costs.

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“…Numerical experiments show that initialization methods using mathematical programs can significantly improve the results compared to random initialization of the evolutionary algorithm population. Amorim et al (2015) presented a hybrid method to build algorithms, that combine a mixed-integer linear solver with a path-relinking metaheuristic, to solve two-stage stochastic problems with continuous second stage decision vari-ables. In the first step of this hybrid algorithm, each scenario is solved individually by a mixed-integer linear solver.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary framework for two-stage stochastic resource allocation problems

Hokama,

Felice,

Bracht

et al. 2019

Preprint

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Resource allocation problems are a family of problems in which resources must be selected to satisfy given demands. This paper focuses on the two-stage stochastic generalization of resource allocation problems where future demands are expressed in a finite number of possible scenarios. The goal is to select cost effective resources to be acquired in the present time (first stage), and to implement a complete solution for each scenario (second stage), while minimizing the total expected cost of the choices in both stages.We propose an evolutionary framework for solving general two-stage stochastic resource allocation problems. In each iteration of our framework, a local search algorithm selects resources to be acquired in the first stage. A genetic metaheuristic then completes the solutions for each scenario and relevant information is passed onto the next iteration, thereby supporting the acquisition of promising resources in the following first stage. Experimentation on numerous instances of the two-stage stochastic Steiner tree problem suggests that our evolutionary framework is powerful enough to address large instances of a wide variety of two-stage stochastic resource allocation problems.

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A hybrid path‐relinking method for solving two‐stage stochastic integer problems

Cited by 4 publications

References 17 publications

From scenarios to conditional scenarios in two‐stage stochastic MILP problems

From scenarios to conditional scenarios in two‐stage stochastic MILP problems

A practical assessment of risk-averse approaches in production lot-sizing problems

Evolutionary framework for two-stage stochastic resource allocation problems

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