Normative design of organizations. I. Mission planning

Levchuk, Georgiy; Levchuk, Yuri N.; Luo, Jie; Pattipati, Krishna R.; Kleinman, David L.

doi:10.1109/tsmca.2002.802819

Cited by 94 publications

(29 citation statements)

References 32 publications

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“…While this interdisciplinary interaction between engineering, particularly computer science and robotics, and biology is now commonplace (Di Caro and Dorigo 1998;Foukia et al 2003;Di Caro et al 2004, January;Gerkey and Matarić 2004;Mullen et al 2009;Rubenstein et al 2014), it is to our knowledge still rare for the science of organizations or management and biology, and in many of the existing cases, production processes, not the human workers themselves, are modeled as the equivalent to the 'workers' discussed here (Fulkerson and Staffend 1997;Cicirello and Smith 2004;Frayret et al 2004). Perhaps this is in part due to the difficulty of translating research findings from biology to the social sciences and vice versa (though see: Pugh et al 1969;Levchuk et al 2002;Helbing and Balietti 2011), or perhaps due to the fact that in designing optimal strategies for human organizations, the selfish motives of individuals play an overshadowing role (Itoh 1992; note that "selfish" is used here not in a moral sense, but purely in the sense that individual payoffs, as well as group-level performance, have to be optimized in human organizations, unlike in social insect organizational strategies). However, such questions as what is the ideal ratio of specialists to generalists, how does fluctuation in demand for different jobs affect the optimal level of specialization, and may there be benefits in terms of efficiency, flexibility, or robustness to having 'surplus' workers, are universal across many social and engineered complex systems.…”

Section: Resultsmentioning

confidence: 99%

When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents

Charbonneau

Dornhaus

2015

J Bioecon

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We expect that human organizations and cooperative animal groups should be optimized for collective performance. This often involves the allocation of different individuals to different tasks. Social insect colonies are a prime example of cooperative animal groups that display sophisticated mechanisms of task allocation. Here we discuss which task allocation strategies may be adapted to which environmental and social conditions. Effective and robust task allocation is a hard problem, and in many biological and engineered complex systems is solved in a decentralized manner: human organizations may benefit from insights into what makes decentralized strategies of group organization effective. In addition, we often find considerable variation among individuals in how much work they appear to contribute, despite the fact that individual selfishness in social insects is low and optimization occurs largely at the group level. We review possible explanations for uneven workloads among workers, including limitations on individual information collection or constraints of task allocation efficiency, such as when there is a mismatch between the frequency of fluctuations in demand for work and the speed at which workers can be reallocated. These processes are likely to apply to any system in which worker agents are allocated to tasks with fluctuating demand, and should therefore be instructive to understanding optimal task allocation and inactive workers in any distributed system. Some of these processes imply that a certain proportion of inactive workers can be an adaptive strategy for collective organization.

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Section: Resultsmentioning

confidence: 99%

When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents

Charbonneau

Dornhaus

2015

J Bioecon

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“…The landing campaign of a joint operation is setting for simulation analysis, the timing relation graph of the mission and detailed parameters of the platform and task are described in [10].…”

Section: A Mission Scenariomentioning

confidence: 99%

Adaptive Optimization Method of Resource Structure in Uncertain Environment Based on Cloud Genetic Algorithm

Meng-chen¹,

Sun²,

Zhang³

et al. 2016

Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)

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Abstract-Aiming at the influence of uncertain factors that impact on tasks and platforms in battlefield environment ， adaptive optimization method based on uncertain event is proposed. Taking the uncertainty of platform loss and task implementing time into consideration, established a resource structure optimizing model which takes the minimum mission execution time as object function. And a Resource Structure Based on Cloud Genetic Algorithm is designed to adjust or redesign the platform resource structure according to the influence that uncertain events impact on organization resource capability while triggering optimization. The availability and validity of the algorithm is proved by demonstrating a simulation case of joint campaign.

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“…US Forces developed Computer-Aided Mission Planning System (CAMPS), which have assistant calculation function. The typical system of CAMPS are Mission Support System I(MSS I) and Mission Support System II (MSS II) [7]. US Forces equipped on F-15 fighter and F-16 fighter with MSS I and MSS II.…”

Section: ) Tactic Mpsmentioning

confidence: 99%

Overview on Mission Planning System

Zhao¹,

Zhang²,

Lv³

et al. 2016

IJKE

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Abstract-As a new type of information network system, Mission Planning System has become the new focal point, especially in the sphere of military technology. In order to guide further research of Mission Planning System, this paper has done several works as below. Firstly, referred to levels of mission, types of missions and the arms and services, mission planning systems are classified in this paper. Subsequently, this paper analyzes the development of mission planning system in US Forces and in other countries' forces. Finally, based on analyzing the experiences, the advices are given for the construction of mission planning system. Index Terms-Mission planning, mission planning system, joint operation, command and control.

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Normative design of organizations. I. Mission planning

Cited by 94 publications

References 32 publications

When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents

When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents

Adaptive Optimization Method of Resource Structure in Uncertain Environment Based on Cloud Genetic Algorithm

Overview on Mission Planning System

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