five

Instances for the Exam Location Problem

收藏
NIAID Data Ecosystem2026-05-01 收录
下载链接:
https://data.mendeley.com/datasets/3n92sptjnv
下载链接
链接失效反馈
官方服务:
资源简介:
The instances are designed to solve the Exam Location Problem (ELP) with room selection decisions and were originally generated by Mihaylov et al. (2013). The ELP is formally described in the scientific paper titled "The exam location problem: mathematical formulations and variants". The data set can be utilized for solving several ELP variants and extension with or without capacity restrictions. In addition to room, site, exam and participant information, the data files contain parameters which are designated for ELP variants concerned with exam supervisor allocations. More specifically, each file includes the following information: - The maximum number of sites an exam can be allocated. - The minimum number of participants assigned to an exam. - The number of sites. - For each site: Site ID, coordinates, number of rooms, list of rooms. - For each room: Room ID, capacity, fixed cost, variable cost, equipment availability indicator (Boolean), total units of time the room is available. - For each exam: Exam ID, duration, equipment need indicator (Boolean), list of participants. - For each participant: Participant ID, coordinates. - For each supervisor: Supervisor ID, fixed cost, variable cost, coordinates. The file IDs contain information about the number of exams and sites. For each combination, fives instances are generated randomly. We refer to the original paper "The exam location problem: mathematical formulations and variants" for further details regarding the instances. References Çalık, H., Wauters, T., and Vanden Berge, G.. The exam location problem: mathematical formulations and variants, Technical Report, KU Leuven, 2023. Mihaylov, M., Wauters, T., and Vanden Berghe, G. (2013). Geographically distributed exam timetabling. In Proceedings of the Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA).
创建时间:
2023-07-10
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作