自适应虚拟团队规划与协调:数学编程方法

IF 1.8 Q3 MANAGEMENT
Christopher Garcia
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引用次数: 0

摘要

目的远程工作的兴起越来越多地要求企业根据需求将单一的大型综合人才库协调成临时的短期项目团队。这个问题同时涉及许多考虑因素,包括项目收入和拒绝成本、相互冲突的项目和角色、员工分配成本、员工使用偏好和限制、员工重新分配成本以及任意的角色开始和结束时间。此外,还必须根据不断变化的情况持续更新计划。本文探讨了动态虚拟团队规划与协调问题。我们证明了这一问题的 NP 难度,并提供了一种动态混合整数线性规划(MILP)公式,用于优化初始计划的生成以及计划的持续调整和重新优化。我们利用因子实验设计生成了具有多种特征的基准问题,并使用通用 MILP 求解器进行了广泛的计算实验。所有观察到的求解时间都足以支持现实世界中虚拟团队协调的运营决策要求,这证明了这种方法的可行性。本研究中开发的方法可以使组织以自动化的方式优化大规模虚拟团队的协调,并根据不断变化的情况持续调整计划。 原创性/价值 本文探讨了一个复杂的新问题,由于远程工作的增加,这个问题对组织的重要性与日俱增。我们为优化解决该问题的规划和重新规划方面提供了问题表述和精确方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive virtual team planning and coordination: a mathematical programming approach

Purpose

The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination.

Design/methodology/approach

We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver.

Findings

Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach.

Practical implications

The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner.

Originality/value

This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem.

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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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