{"id":16726,"date":"2020-05-25T07:19:41","date_gmt":"2020-05-25T07:19:41","guid":{"rendered":"http:\/\/cio.edu.umh.es\/?p=16726"},"modified":"2021-07-22T09:50:08","modified_gmt":"2021-07-22T07:50:08","slug":"some-matheuristic-algorithms-for-multistage-stochastic-optimization-models-with-endogenous-uncertainty-and-risk-management-2020-european-journal-of-operational-research-285-988-1001","status":"publish","type":"post","link":"https:\/\/cio.umh.es\/en\/2020\/05\/25\/some-matheuristic-algorithms-for-multistage-stochastic-optimization-models-with-endogenous-uncertainty-and-risk-management-2020-european-journal-of-operational-research-285-988-1001\/","title":{"rendered":"Some matheuristic algorithms for multistage stochastic optimization models with endogenous uncertainty and risk management (2020), European Journal of Operational Research 285. 988\u20131001"},"content":{"rendered":"<p>[:es]<strong>Laureano F. Escudero\u00a0<\/strong>(<em>Rey Juan Carlos University of Madrid<\/em>), <strong>M. Araceli Gar\u00edn\u00a0<\/strong>(<em>University of Pa\u00eds Vasco<\/em>), <strong>Juan F. Monge<\/strong>\u00a0(<em>Miguel Hern\u00e1ndez University of Elche) <\/em>and<em>\u00a0<\/em><strong>Aitziber Unzueta\u00a0<\/strong><em>(University of Pa\u00eds Vasco).<\/em><br \/>\n<strong>Abstract:\u00a0<\/strong>Two matheuristic decomposition algorithms are introduced. The \ufb01rst one is a Progressive Hedging type so-named Regularized scenario Cluster Progressive Algorithm. The second one is a Frank-Wolfe PH type so-named Regularized scenario Cluster Simplicial Decomposition Progressive Algorithm. An extension of endogenous Type III uncertainty is considered for representing the decision dependent scenario probability and outlook. Its performance is tested in the time-consistent Expected Conditional Stochastic Dominance risk averse environment. As a result of the modeling, the typical risk neutral multistage mixed 0\u20131 linear stochastic problem under uncertainty is replaced with an enlarged model that is equivalent to the required mixed 0\u20131 bilinear model. Based on the special features of the problem, it is unrealistic to seek the optimal solution for large-scale instances. Feasible solutions and lower bounds on the solution value of the original model are provided. In total, 48 strategies are considered, each one consists of a combination of a regularization norm, a calibration type for the PH pseudo-gradient computation, and a set of value intervals of the in\ufb02uential variables on a representative endogenous uncertainty-based piecewise function in the scenarios. Computational results are reported for a large-scale extension of a well-known real-life pilot case for preparedness resource allocation planning aiming to natural disaster relief. The matheuristics outperform the plain use of a state-of-the-art solver.<br \/>\n<strong>Keywords:\u00a0<\/strong>Stochastic programming; Exogenous and endogenous uncertainties; Time-consistent stochastic dominance; Mixed 0\u20131 bilinear optimization; Scenario cluster-based decomposition algorithms[:]<\/p>","protected":false},"excerpt":{"rendered":"<p>[:es]Laureano F. Escudero\u00a0(Rey Juan Carlos University of Madrid), M. Araceli Gar\u00edn\u00a0(University of Pa\u00eds Vasco), Juan F. Monge\u00a0(Miguel Hern\u00e1ndez University of Elche) and\u00a0Aitziber Unzueta\u00a0(University of Pa\u00eds Vasco).<br \/>\nAbstract:\u00a0Two matheuristic decomposition algorithms are introduced. The \ufb01rst one is a Progressive Hedging type so-named Regularized scenario Cluster Progressive Algorithm. The second one is a Frank-Wolfe PH type so-named Regularized [&#8230;]<\/p>","protected":false},"author":6202,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_links_to":"","_links_to_target":""},"categories":[369888],"tags":[],"_links":{"self":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/16726"}],"collection":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/users\/6202"}],"replies":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/comments?post=16726"}],"version-history":[{"count":0,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/16726\/revisions"}],"wp:attachment":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/media?parent=16726"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/categories?post=16726"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/tags?post=16726"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}