{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T06:47:08Z","timestamp":1663310828527},"reference-count":0,"publisher":"Politechnika Wroclawska Oficyna Wydawnicza","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"abstract":"<jats:p>This paper addresses a class of problems under interval data uncertainty, composed of min-max regret generalisations of classical 0-1 optimisation problems with interval costs. These problems are called robust-hard when their classical counterparts are already NP-hard. The state-of-the-art exact algorithms for interval 0-1 min-max regret problems in general work by solving a corresponding mixed integer linear programming formulation in a Benders\u2019 decomposition fashion. Each of the possibly exponentially many Benders\u2019 cuts is separated on the fly through the resolution of an instance of the classical 0-1 optimisation problem counterpart. Since these separation subproblems may be NP-hard, not all of them can be easily modelled by means of Linear Programming (LP), unless P = NP. In this work, we formally describe these algorithms through a logic-based Benders\u2019 decomposition framework and assess the impact of three warm-start procedures. These procedures work by providing promising initial cuts and primal bounds through the resolution of a linearly relaxed model and an LP-based heuristic. Extensive computational experiments in solving two challenging robust-hard problems indicate that these procedures can highly improve the quality of the bounds obtained by the Benders\u2019 framework within a limited execution time. Moreover, the simplicity and effectiveness of these speed-up procedures makes them an easily reproducible option when dealing with interval 0-1 min-max regret problems in general, especially the more challenging subclass of robust-hard problems<\/jats:p>","DOI":"10.37190\/ord210202","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T06:21:36Z","timestamp":1625725296000},"source":"Crossref","is-referenced-by-count":0,"title":["Improving logic-based Benders\u2019 algorithms for solving min-max regret problems"],"prefix":"10.37190","volume":"31","author":[{"given":"Lucas","family":"Assun\u00e7\u00e3o","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9a Cynthia","family":"Santos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thiago F.","family":"Noronha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael","family":"Andrade","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"23140","container-title":["Operations Research and Decisions"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.orduser.pwr.wroc.pl\/DownloadFile.aspx?aid=1583","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T13:25:15Z","timestamp":1663248315000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.orduser.pwr.wroc.pl\/DownloadFile.aspx?aid=1583"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":0,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.37190\/ord210202","relation":{},"ISSN":["2081-8858","2391-6060"],"issn-type":[{"value":"2081-8858","type":"print"},{"value":"2391-6060","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}