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Μόνιμο URI για αυτήν τη συλλογήhttps://pyxida.aueb.gr/handle/123456789/53

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Πλοήγηση Διδακτορικές διατριβές ανά Επιβλέπων "Mourtos, Yiannis"

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  • Μικρογραφία εικόνας
    Τεκμήριο
    Integrated methods and systems for optimization and decision support
    (30-09-2017) Plitsos, Stathis; Athens University of Economics and Business, Department of Management Science and Technology; Magos, Dimitrios; Tarantilis, Christos D.; Doukidis, George I.; Εμίρης, Δημήτριος; Δούμπος, Μιιχαήλ; Γιαννίκος, Ιωάννης; Mourtos, Yiannis
    First we focus on the multi-index assignment. We propose several components that can be employed across different types of assignment, i.e, a constraint propagation mechanism, a tabu-search meta-heuristic, a new variant of the Feasibility Pump heuristic that employs cutting planes, along with a new Branch & Cut method. Results show that these components when employed together reduce the time to optimality or the integrality gap for large instances compared to a commercial solver. Furthermore, this work paves the way towards the development of a DSS, which can facilitate several types of use. The second problem is the energy-aware production scheduling. Here, we present an energy-aware production scheduling DSS as designed, implemented and evaluated in a real context. In short, this work contributes to decision support for energy-efficient manufacturing by a metaheuristic algorithm that hierarchically optimizes flexible job-shop scheduling problems, a set of data requirements and the DSS evaluation in real settings. Last, we focus on the the binary multi-dimensional knapsack problem. Here, we describe a new primal-dual method. Current exact approaches and commercial solvers run into difficulties even for a small-to-medium number of constraints and variables. The proposed primal-dual method employs the linear relaxation, enhanced by global lifted cover inequalities to improve the upper bound and a new version of the Feasibility Pump heuristic that uses these cuts in the pumping procedure to obtain better and feasible lower bounds.