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Lower-Level Decision Task Solution While Optimising a Construction Project Schedule

机译:较低级别决策任务解决方案,同时优化建设项目时间表

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A bi-level approach for optimisation of a construction project is discussed in the paper. Considered optimisation problem deals with identification of optimal project and a corresponding optimal schedule. Project structure is defined by applied order of technological operations. Application of decomposition-coordination principle facilitates problem solution. We therefore obtain tasks which belong to 2 distinct optimisation levels. The lower optimisation task level is devoted to optimal allocation of execution modes to operations while assuming considered project structures. The global optimisation task level pertains to choice of the best structure for a construction project. Solution of lower level tasks are applied in this regard. The main difficulty in global project schedule optimisation results from multiplicity of feasible construction project structures and a need for solution of lower level tasks. We generally consider application of Monte Carlo simulation (MC) for generating feasible project structures. Mixed linear programming (MILP) and MC is the applied to solve lower level tasks. We also apply metaheurstics combined with MILP to solve lower level tasks while generating feasible project structures. Effects of application of presented approaches for solving lower level task solution approaches are finally compared.
机译:本文讨论了用于优化建筑项目的双级方法。考虑了优化问题处理最佳项目和相应的最佳时间表。项目结构由技术运营的应用顺序定义。分解协调原理的应用有助于解决问题。因此,我们获得属于2个不同优化水平的任务。在假设考虑项目结构的同时,较低的优化任务级别用于最佳地将执行模式分配到操作。全球优化任务水平涉及建筑项目最佳结构的选择。在这方面应用较低级别任务的解决方案。全球项目安排优化的主要困难是来自多种可行的建设项目结构的优化以及对较低级别任务的解决方案的需求。我们通常考虑应用Monte Carlo仿真(MC)来产生可行的项目结构。混合线性编程(MILP)和MC适用于解决较低级别的任务。我们还将Metaheurstics与MILP相结合,以解决可行的项目结构的同时解决较低级别的任务。对求解借助较低级任务解决方案方法的应用方法的影响最终进行了比较。

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