Training and recruitment in logistics node design by using web-based simulation

Bruzzone, Agostino G.; Bocca, Enrico; Longo, Francesco; Massei, Marina

doi:10.1504/ijims.2007.014482

Cited by 20 publications

(9 citation statements)

References 8 publications

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“…Simulation models has been successfully used in a wide area of domains to design, evaluate and improve system indicators, as can be seen, for example, in the logistic arena in Bruzzone et al 32,33 and Longo and Mirabelli. 34 Causal simulation models can contribute as a challenging assessment method for sustainability as they can provide valuable information for guiding the decision and policy makers in adopting the right measures for infrastructure development.…”

Section: Simulation Models For the Design Of Sustainability Indicatorsmentioning

confidence: 99%

Causal simulation models for facing third millennium air transport sustainability

et al. 2013

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Aeronautics and air transport is a vital sector of our society and economy. Air transport logistics is one of the key players to support efficient globalization; however, sustainable mobility is at stake, due to facts such as the interdependencies with the financial system, climate change and an increasing scarcity of resources. This paper highlights the consequences of a lack of a proper understanding of air-side and land-side, leading to an unsustainable air transport system. A system approach for knowledge sharing between air traffic controllers, handling operators, airlines and airport managers is justified by means of causal models to design a mitigation mechanism to tackle perturbations instead of increasing the latent capacity. The simulation benefits to design indicators of sustainability are also mentioned.

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Section: Simulation Models For the Design Of Sustainability Indicatorsmentioning

confidence: 99%

Causal simulation models for facing third millennium air transport sustainability

et al. 2013

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“…The aim of this research is to develop an interoperable simulator based on computer-generated forces (CGF) to create complex scenarios devoted to supporting evaluation and training in relation to different C4 solutions. 12,13 Usually, development of innovative C4 solutions has a strong technological focus and does not rely too much on people and organizations, but with urban asymmetric scenarios the presence of human behavior modifiers at individual and organizational levels has a very strong influence on the operations. 14 Therefore, in addition to the technology network, it is necessary to include effective models of human factors the impact the C4.…”

Section: Intelligent Agents and C4 In Network-centric Urban Asymmetric Environmentsmentioning

confidence: 99%

Simulation of an urban environment by using intelligent agents within asymmetric scenarios for assessing alternative command and control network-centric maturity models

Massei

Tremori

2013

Journal of Defense Modeling & Simulation

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The research proposes the development of a simulation framework for assessing the effectiveness and efficiency of different alternative command, control, communications and computers (C4) solutions in an urban environment affected by asymmetric warfare. The authors present their approach in developing intelligent agents computer-generated forces (IA-CGF) in a non-conventional framework related to the project named CGF C4 IT. In the current military context, characterized by new asymmetric threats (i.e. terrorism, biological attacks) and affected by new technologies solutions, it is critical to measure the effectiveness of different command and control (C2) maturity models involving local and coalition forces, police and other resources in an overseas urban framework. As a matter of fact, this is one of the main goals of the CGF C4 IT project which is devoted to investigating alternative C2 models for guaranteeing agility in complex scenarios. The CGF C4 IT federation is a high level architecture simulator, designed by the authors, and currently devoted to supporting Italian Army simulation capabilities. This represents an innovative and effective solution for investigating, by experimental analysis, the C2 agility concepts within a complex framework with special attention to human behavior models.

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“…Since many of them are multi-objective nondeterministic polynomial (NP) hard combinatorial optimization problems, optimization decisions in MS become more and more difficult. Therefore, research on how to promote the quality and efficiency of these multi-objective combinatorial optimizations (MOCO) particularly in large-scale service-oriented distributed simulation systems (SoDSSs), such as Wu et al, 34 Pan et al, 35 Boukerche et al 36 and Bruzzone et al 37,38 are extremely crucial. At present, these studies can be classified into two categories: (1) MOCO modeling and analysis in SoDSSs and (2) algorithms for MOCO in SoDSSs.…”

Section: Introductionmentioning

confidence: 99%

QMAEA: A quantum multi-agent evolutionary algorithm for multi-objective combinatorial optimization

et al. 2013

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Multi-objective combinatorial optimization (MOCO) is an essential concern for the implementation of large-scale distributed modeling and simulation (MS) system. It is more complex than general computing systems, with higher dynamics and stricter demands on real-time performance. The quality and speed of the optimal decision directly decides the efficiency of the simulation. However, few works have been carried out for multi-objective combinatorial optimization MOCO especially in large-scale and service-oriented distributed simulation systems (SoDSSs). The existing algorithms for MOCO in SoDSSs are far from enough owing to their low accuracy or long decision time. To overcome this bottleneck, in this paper, a quantum multi-agent evolutionary algorithm (QMAEA), for addressing MOCO in large-scale SoDSSs is proposed. In QMAEA, the concept and characteristics of agent and quantum encoding are introduced for high intelligent searching. Each agent represented by a quantum bit, called a quantum agent (QAgent), is defined as a candidate solution for a MOCO problem, and each QAgent is assigned an energy, which denotes the fitness or objective function value of the candidate solution represented by it. Each QAgent is connected by four other QAgents nearby, and all QAgents are organized by an annular grid, called a multi-agent grid (MAG). In a MAG system, the population of QAgents can reproduce, perish, compete for survival, observe and communicate with the environment, and make all their decisions autonomously. Several operators, i.e. energy-evaluation-operator, competition-operator, crossover-operator, mutation-operator and trimming-operator, are designed to specify the evolvement of the MAG. The theory of predatory search strategy of animals is introduced in the evolution of QMAEA. Multiple evolutionary strategies, such as local-evolution-strategy, local-mutation-strategy and global-mutation-strategy are designed and used to balance the exploration (global search ability) and the exploitation (local search ability) of QMAEA. The framework and procedures of QMAEA are presented in detail. The simulation and comparison results demonstrate the proposed method is very effective and efficient for addressing MOCO in SoDSSs.

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Training and recruitment in logistics node design by using web-based simulation

Cited by 20 publications

References 8 publications

Causal simulation models for facing third millennium air transport sustainability

Causal simulation models for facing third millennium air transport sustainability

Simulation of an urban environment by using intelligent agents within asymmetric scenarios for assessing alternative command and control network-centric maturity models

QMAEA: A quantum multi-agent evolutionary algorithm for multi-objective combinatorial optimization

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