{"title":"自适应虚拟团队规划与协调:数学编程方法","authors":"Christopher Garcia","doi":"10.1108/jm2-03-2024-0070","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>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.</p><!--/ Abstract__block -->","PeriodicalId":16349,"journal":{"name":"Journal of Modelling in Management","volume":"73 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive virtual team planning and coordination: a mathematical programming approach\",\"authors\":\"Christopher Garcia\",\"doi\":\"10.1108/jm2-03-2024-0070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>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.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>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.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>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.</p><!--/ Abstract__block -->\\n<h3>Practical implications</h3>\\n<p>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.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. 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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.
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.