首页> 外文期刊>Applied Soft Computing >Project scheduling for minimizing temporary availability cost of rental resources and tardiness penalty of activities
【24h】

Project scheduling for minimizing temporary availability cost of rental resources and tardiness penalty of activities

机译:以最大限度地减少租赁资源的临时可用性和活动迟到罚款的项目调度

获取原文
获取原文并翻译 | 示例
           

摘要

This paper addresses the resource availability cost problem with rental resources where each activity has a given due date to be completed. In this problem setting, the required resources are temporarily rented to accomplish the corresponding activities where the paid fee for the rental resources depends on duration of their availability. In addition, each activity would be subjected to a tardiness penalty if its finish time surpasses its given due date. A mathematical model is presented for the problem and some features of its solution space are established. Also, a best-performed version of ant colony optimization (ACO) algorithm based on Ant Colony System is developed to tackle this strongly NP-Hard problem. The proposed method consists a new compatible schedule generation scheme, a new resource based heuristic role and an efficient local search. In a comprehensive experimental effort, the proposed parameters-tuned approach is compared with the exact solutions obtained by GAMS on several small-scale instances, while results of a competitive metaheuristic based on Genetic Algorithm are employed to validate the developed ACO algorithm for the large-scale instances. Finally, effectiveness of the proposed ACO is analyzed using statistical tests and the impact of the crucial parameters on the resulting solutions is demonstrated. (C) 2017 Elsevier B.V. All rights reserved.
机译:本文通过租赁资源解决了资源可用性成本问题,其中每个活动都有一个特定的截止日期。在此问题设置中,暂时租用所需资源以完成租赁资源的付费费用的相应活动取决于其可用性的持续时间。此外,如果其结束时间超过其给定的截止日期,每项活动将受到迟到的惩罚。呈现了数学模型的问题,建立了解决方案空间的一些特征。此外,开发了基于蚁群系统的蚁群优化(ACO)算法的最佳类型的蚁群优化(ACO)算法,以解决这种强烈的NP难题。该提出的方法包括一个新的兼容计划生成方案,基于新的资源的启发式角色和有效的本地搜索。在全面的实验努力中,将所提出的参数调谐方法与来自几种小规模实例的Gams获得的精确解决方案进行了比较,而基于遗传算法的竞争性成群质型的结果用于验证大型的ACO算法规模实例。最后,使用统计测试分析所提出的ACO的有效性,并证明了对所得溶液的关键参数的影响。 (c)2017 Elsevier B.v.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号