A roommate problem and room allocation in dormitories using mathematical modeling and multi-attribute decision-making techniques

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-02-19 DOI:10.1108/jm2-09-2023-0214
Alireza Khalili-Fard, Reza Tavakkoli-Moghaddam, Nasser Abdali, Mohammad Alipour-Vaezi, Ali Bozorgi-Amiri
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Abstract

Purpose

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

Design/methodology/approach

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

Findings

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

Originality/value

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

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利用数学建模和多属性决策技术解决室友问题和宿舍房间分配问题
目的 近几十年来,学生宿舍中的学生人数显著增加,这主要归因于留学生人数的不断增长。宿舍是学生发展的重要环境。学生之间的协调性和兼容性会极大地影响他们的整体成功。本研究旨在引入一种创新方法,用于宿舍内的室友选择和房间分配。在本研究中,首先使用多属性决策方法,包括贝叶斯最佳-最差法和加权总和乘积评估法,计算出成对学生之间的不相容率。然后,利用线性数学模型,选择室友并分配到寝室,以实现总不相容率和成本最小化的双重目标。结果结果表明,与随机分配和基于偏好的分配这两种常见方法相比,所提出的方法非常有效。此外,所提方法的适用性超出了当前的范围,使其适用于解决各种匹配问题,包括船员配对和同学配对。原创性/价值这种新颖的室友选择和房间分配方法增强了优化宿舍安排的决策能力。受作者面临的一个实际问题的启发,本研究致力于为这一问题提供一个稳健的解决方案。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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