### Resumé

Sprog | Engelsk |
---|---|

Tidsskrift | Computers & Operations Research |

Vol/bind | 58 |

Sider | 32-40 |

ISSN | 0305-0548 |

DOI | |

Status | Udgivet - jun. 2015 |

### Emneord

- Multiperiod stochastic mixed 0–1 linear programming
- Risk averse
- Stochastic dominance constraints
- Stochastic dynamic programming
- Cross-scenario constraints

### Citer dette

*Computers & Operations Research*,

*58*, 32-40. DOI: 10.1016/j.cor.2014.12.007

}

*Computers & Operations Research*, bind 58, s. 32-40. DOI: 10.1016/j.cor.2014.12.007

**An SDP Approach for Multiperiod Mixed 0–1 Linear Programming Models with Stochastic Dominance Constraints for Risk Management.** / Escudero, Laureano F.; Monge, Juan Francisco; Morales, Dolores Romero.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - An SDP Approach for Multiperiod Mixed 0–1 Linear Programming Models with Stochastic Dominance Constraints for Risk Management

AU - Escudero,Laureano F.

AU - Monge,Juan Francisco

AU - Morales,Dolores Romero

PY - 2015/6

Y1 - 2015/6

N2 - In this paper we consider multiperiod mixed 0–1 linear programming models under uncertainty. We propose a risk averse strategy using stochastic dominance constraints (SDC) induced by mixed-integer linear recourse as the risk measure. The SDC strategy extends the existing literature to the multistage case and includes both the first-order and second-order constraints. We propose a stochastic dynamic programming (SDP) solution approach, where one has to overcome the negative impact of the cross-scenario constraints on the decomposability of the model. In our computational experience we compare our SDP approach against a commercial optimization package, in terms of solution accuracy and elapsed time. We use supply chain planning instances, where procurement, production, inventory, and distribution decisions need to be made under demand uncertainty. We confirm the hardness of the testbed, where the benchmark cannot find a feasible solution for half of the test instances while we always find one, and show the appealing tradeoff of SDP, in terms of solution accuracy and elapsed time, when solving medium-to-large instances.

AB - In this paper we consider multiperiod mixed 0–1 linear programming models under uncertainty. We propose a risk averse strategy using stochastic dominance constraints (SDC) induced by mixed-integer linear recourse as the risk measure. The SDC strategy extends the existing literature to the multistage case and includes both the first-order and second-order constraints. We propose a stochastic dynamic programming (SDP) solution approach, where one has to overcome the negative impact of the cross-scenario constraints on the decomposability of the model. In our computational experience we compare our SDP approach against a commercial optimization package, in terms of solution accuracy and elapsed time. We use supply chain planning instances, where procurement, production, inventory, and distribution decisions need to be made under demand uncertainty. We confirm the hardness of the testbed, where the benchmark cannot find a feasible solution for half of the test instances while we always find one, and show the appealing tradeoff of SDP, in terms of solution accuracy and elapsed time, when solving medium-to-large instances.

KW - Multiperiod stochastic mixed 0–1 linear programming

KW - Risk averse

KW - Stochastic dominance constraints

KW - Stochastic dynamic programming

KW - Cross-scenario constraints

UR - http://sfx-45cbs.hosted.exlibrisgroup.com/45cbs?url_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&ctx_enc=info:ofi/enc:UTF-8&ctx_ver=Z39.88-2004&rfr_id=info:sid/sfxit.com:azlist&sfx.ignore_date_threshold=1&rft.object_id=954921388919&rft.object_portfolio_id=&svc.holdings=yes&svc.fulltext=yes

U2 - 10.1016/j.cor.2014.12.007

DO - 10.1016/j.cor.2014.12.007

M3 - Journal article

VL - 58

SP - 32

EP - 40

JO - Computers & Operations Research

T2 - Computers & Operations Research

JF - Computers & Operations Research

SN - 0305-0548

ER -