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A general ontological timetabling-model driven metaheuristics approach based on elite solutions

机译:基于精英解决方案的一般本体论时间表模型驱动型式化态化方法

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摘要

Timetabling is a managerial problem that recurringly appears in various domains such as education, transport, sports, and staff management. The combinatorial nature of this problem poses solution challenges that aggravate with an increase in the problem size. While heuristics and metaheuristics initially offered promise, the progress plateaued as attempts to solve even bigger problems showed exorbitant costs while sampling feasible solutions. This issue is criticized for the lack of exploiting the underlying problem structure and the prevailing fragmen-tation in modeling and solution approaches. To address these issues, we first propose a novel timetabling ontology that serves as a common modeling basis, resolving the existing heterogeneity across various application domains. This ontology facilitates mapping the anatomy of any real timetabling problem onto its general structure. Second, it offers a unique two-stage solution approach for solving this generalized problem. The first stage of this approach entails generating elite initial solutions by exploiting this general problem structure, while the second stage uses a metaheuristic to improve these solutions at a very low computational cost. Using a university timetabling problem, we demonstrate the applicability of this approach. The numerical results show that the proposed algorithm converges within a fraction of computational costs incurred by other techniques for comparable problem sizes. This research paves the way for consolidating efforts for the development of gener-alizable cross-domain timetabling approaches.
机译:时间表是一个管理问题,即经常出现在教育,运输,体育和员工管理等各个领域。这个问题的组合性质造成了解决方案挑战,这些挑战加剧了问题大小的增加。虽然启发式和美术机构最初提供了承诺,但仍然有助于解决更大问题的进展表现出吸收的成本,同时采样可行的解决方案。这一问题因缺乏利用潜在的问题结构和建模方法的现行Fragmen-tatives而受到批评。为了解决这些问题,我们首先提出了一种新颖的时间表本体,其用作共同建模基础,解决各种应用领域的现有异质性。该本体有助于将任何实际时间表问题的解剖结构进行映射到其一般结构上。其次,它提供了一种独特的两级解决方案方法,用于解决这一广义问题。这种方法的第一阶段需要通过利用这种一般问题结构来生成精英初始解决方案,而第二阶段使用成群化以以非常低的计算成本改进这些解决方案。使用大学时间表问题,我们展示了这种方法的适用性。数值结果表明,所提出的算法在其他技术的计算成本的一小部分内收敛于可比问题大小的其他技术。这项研究铺平了巩固努力开发且可加强的跨域时间表的努力的方式。

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