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Multipurpose linear programming optimization model for repetitive activities scheduling in construction projects

机译:建筑项目中重复活动的多用途线性规划优化模型

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摘要

Repetitiveness in project's activities has gained an important role in the construction industry. Multiple linear scheduling methods have been proposed in order to fully take advantage of the spatial and temporal information these type of project can provide to practitioners. Besides the advances in the optimization models in these fields, to the extent of the authors knowledge, there is still pending a complete and flexible mathematical linear programming formulation that allow practitioners to easily and jointly solve the Resource allocation, Resource-Constrained Project Scheduling and Time-Cost Tradeoff problem, taking into account as many scheduling properties, benefits and challenges that linear scheduling of repetitive activities imply. This paper shows a complete guide and computational experimentation, of a novel mathematical model that can be easily used by practitioners to optimize construction schedules considering to the largest extent the time and space conditions repetitive projects offer. Particularly, it contributes to the repetitive activities scheduling body of knowledge by successfully implementing a robust linear programing optimization model in a real construction project, while considering as much linear scheduling characteristics as possible. It proves that relationships in the sub-activity level, continuity conditions, multiple modes of execution, controlled acceleration routines and execution mode shifts, and multiple crews can be easily and jointly integrated to a linear optimization model by adding simple linear restrictions to the model.
机译:项目活动中的重复性在建筑业中取得了重要作用。已经提出了多种线性调度方法,以便充分利用这些类型的项目可以向从业者提供的空间和时间信息。除了这些字段中的优化模型的进步外,还有尚未等待完整且灵活的数学线性编程配方,使从业者可以轻松地共同解决资源分配,资源受限的项目调度和时间 - 将权衡问题,考虑到重复活动线性调度的许多调度属性,福利和挑战意味着。本文显示了一个完整的指南和计算实验,该模型可以通过从业者轻松使用的新数学模型,以优化施工时间表,考虑到最大程度的时间和空间条件重复项目提供。特别地,它通过成功在真实施工项目中成功实现鲁棒线性编程优化模型来贡献知识的重复活动,同时考虑尽可能多的线性调度特性。它证明了子活动级别,连续性条件,多种执行模式,受控加速例行模式以及执行模式偏移的关系,可以通过为模型添加简单的线性限制来容易地且共同集成到线性优化模型。

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