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Author Dang, C.; Wei, P.F.; Faes, M.G.R.; Valdebenito, M.A.; Beer, M. doi  openurl
  Title (up) Interval uncertainty propagation by a parallel Bayesian global optimization method Type
  Year 2022 Publication Applied Mathematical Modelling Abbreviated Journal Appl. Math. Model.  
  Volume 108 Issue Pages 220-235  
  Keywords Interval uncertainty propagation; Bayesian global optimization; Gaussian process; Infill sampling criterion; Parallel computing  
  Abstract This paper is concerned with approximating the scalar response of a complex computational model subjected to multiple input interval variables. Such task is formulated as finding both the global minimum and maximum of a computationally expensive black-box function over a prescribed hyper-rectangle. On this basis, a novel non-intrusive method, called `triple-engine parallel Bayesian global optimization', is proposed. The method begins by assuming a Gaussian process prior (which can also be interpreted as a surrogate model) over the response function. The main contribution lies in developing a novel infill sampling criterion, i.e., triple-engine pseudo expected improvement strategy, to identify multiple promising points for minimization and/or maximization based on the past observations at each iteration. By doing so, these identified points can be evaluated on the real response function in parallel. Besides, another potential benefit is that both the lower and upper bounds of the model response can be obtained with a single run of the developed method. Four numerical examples with varying complexity are investigated to demonstrate the proposed method against some existing techniques, and results indicate that significant computational savings can be achieved by making full use of prior knowledge and parallel computing.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0307-904X ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000830573400001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1625  
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Author Osorio-Valenzuela, L.; Pereira, J.; Quezada, F.; Vasquez, O.C. doi  openurl
  Title (up) Minimizing the number of machines with limited workload capacity for scheduling jobs with interval constraints Type
  Year 2019 Publication Applied Mathematical Modelling Abbreviated Journal Appl. Math. Model.  
  Volume 74 Issue Pages 512-527  
  Keywords Scheduling; Parallel machines; Interval and workload constraints; Branch-and-price  
  Abstract In this paper, we consider a parallel machine scheduling problem in which machines have a limited workload capacity and jobs have deadlines and release dates. The problem is motivated by the operation of energy storage management systems for microgrids under emergency conditions and generalizes some problems that have already been studied in the literature for their theoretical value. In this work, we propose heuristic and exact algorithms to solve the problem. The heuristics are adaptations of classical bin packing heuristics in which additional conditions on the feasibility of a solution are imposed, whereas the exact method is a branch-and-price approach. The results show that the branch-andprice approach is able to optimally solve random instances with up to 250 jobs within a time limit of one hour, while the heuristic procedures provide near optimal solution within reduced running times. Finally, we also provide additional complexity results for a special case of the problem. (C) 2019 Elsevier Inc. All rights reserved.  
  Address [Osorio-Valenzuela, Luis] Univ Santiago Chile, Elect Engn Dept, Santiago, Chile, Email: luis.osoriov@usach.cl;  
  Corporate Author Thesis  
  Publisher Elsevier Science Inc Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0307-904x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000474317800031 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1013  
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