Agent-Based Modeling of the Immune System: NetLogo, a Promising Framework

Chiacchio, Francesco; Pennisi, Marzio; Russo, Giulia; Motta, Santo; Pappalardo, Francesco

doi:10.1155/2014/907171

Cited by 88 publications

(65 citation statements)

References 35 publications

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“…Agent-based modeling is a bottom-up approach among different simulation modeling approaches in which agents interact with each other and also with the environment [6]. Agent-based modeling facilitates simulation optimization loop of the related optimization of behavioral parameters [7].…”

Section: Agent-based Modelingmentioning

confidence: 99%

Simulation based optimization of a stochastic supply chain considering supplier disruption: An agent-based modeling and reinforcement learning

Aghaie

Heidary

2018

Scientia Iranica

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Many researchers and practitioners in the recent years have been attracted to investigate the role of uncertainties in the supply chain management concept. In this paper a multi-period stochastic supply chain with demand uncertainty and supplier disruption is modeled. In the model, two types of retailers including risk sensitive and risk neutral, with many capacitated suppliers are considered. Autonomous retailers have three choices to satisfy demands: ordering from primary suppliers, reserved suppliers and spot market. The goal is to find the best behavior of the risk sensitive retailer, regarding the forward and option contracts, during several contract periods based on the profit function. Hence, an agent-based simulation approach has been developed to simulate the supply chain and transactions between retailers and unreliable suppliers. In addition, a Q-learning approach (as a method of reinforcement learning) has been developed to optimize the simulation procedure. Furthermore, different configurations for simulation procedure are analyzed. The R-netlogo package is used to implement the algorithm. Also a numerical example has been solved using the proposed simulation-optimization approach. Several sensitivity analyzes are conducted regarding different parameters of the model. Comparison of the numerical results with a genetic algorithm shows a significant efficiency of the proposed Q-leaning approach.Keywords Supply chain management, simulation based optimization, reinforcement learning, demand uncertainty, supplier disruption. IntroductionThe Importance of uncertainties and consequent costs of ignoring them, caused a shift from deterministic configurations of the supply chain to the stochastic models. One of the most important problems in the stochastic supply chain ordering management is the newsvendor (NV) problem. The basic form of the NV problem consists of a buyer and a seller in which the buyer must decide on the amount of ordering from the seller while demand of the customers is not predetermined. In the basic form, the buyer only has an overall information about the customer demand such as the distribution function. Also the decision is made only at one period. The objective is to optimize the profit of the buyer. Two extensions of the problem have been done by the researchers: the multi-period NV Problem (MNVP), the NV Problem with Supplier Disruption (NVPSD). In the MNVP, the buyer(s) decides on the amount of ordering from the seller(s) at the beginning of each period. The buyer(s) decides on the amount of orders based on the uncertain demands of its customers and the inventory remained from the previous period. In the NVPSD (which is often consisted of one period) the buyer(s) decides on the amount of orders based on the uncertain customer demand and remained fixed capacities of the sellers. In the related literature of the NVPSD it is usually assumed that the network consists of many uncertain sellers and one buyer (e.g. [1], [2], [3]). On the other hand, in the literature of the MNVP, ...

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Section: Agent-based Modelingmentioning

confidence: 99%

Simulation based optimization of a stochastic supply chain considering supplier disruption: An agent-based modeling and reinforcement learning

Aghaie

Heidary

2018

Scientia Iranica

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“…Agent-based models are of value in most branches of social science: urban models [106], opinion dynamics [107], consumer behavior [108], industrial networks [109], electricity markets [110], immunology [111,112].…”

Section: Agent-based Modelsmentioning

confidence: 99%

Towards a unified approach in the modeling of fibrosis: A review with research perspectives

Amar

Bianca

2016

Physics of Life Reviews

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Pathological fibrosis is the result of a failure in the wound healing process. The comprehension and the related modeling of the different mechanisms that trigger fibrosis is a challenge of many researchers that work in the field of medicine and biology. The modern scientific analysis of a phenomenon generally consists of three major approaches: theoretical, experimental, and computational. Different theoretical tools coming from mathematics and physics have been proposed for the modeling of the physiological and pathological fibrosis. However a complete framework is missing and the development of a general theory is required. This review aims at finding a unified approach in the modeling of fibrosis diseases that takes into account the different phenomena occurring at each level: molecular, cellular and tissue. Specifically by means of a critical analysis of the different models that have been proposed in the mathematical, computational and physical biology, from molecular to tissue scales, a multiscale approach is proposed, an approach that has been strongly recommended by top level biologists in the past decades.

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“…Models based on cellular automata (CA) Seiden and Celada (1992); Mallet and De Pillis (2006); Puzone et al (2002); Castiglione and Bernaschi (2004) and agent-based modelling (ABMs) Chiacchio et al (2014); Noble (2002); Van Dyke Parunak et al (1998);Folcik et al (2007) address this shortcoming. Both CA and ABMs are designated as "individual-based models" in ecology because of the fact that both models are bottom-up.…”

Section: Mathematical Modelling Of the Immune Systemmentioning

confidence: 99%

PI-FLAME: A parallel immune system simulator using the FLAME graphic processing unit environment

2016

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Agent-based models (ABMs) are increasingly being used to study population dynamics in complex systems such as the human immune system. Previously, Folcik et al. developed a Basic Immune Simulator (BIS) and implemented it using the RePast ABM simulation framework. However, frameworks such as RePast are designed to execute serially on CPUs and therefore cannot efficiently handle large model sizes. In this paper, we report on our implementation of the BIS using FLAME-GPU, a parallel computing ABM simulator designed to execute on Graphics Processing Units (GPUs). To benchmark our implementation, we simulate the response of the immune system to a viral infection of generic tissue cells. We compared our results with those obtained form the original RePast implementation for statistical accuracy. We observe that our implementation has 13× performance advantage over the original RePast implementation.

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Agent-Based Modeling of the Immune System: NetLogo, a Promising Framework

Cited by 88 publications

References 35 publications

Simulation based optimization of a stochastic supply chain considering supplier disruption: An agent-based modeling and reinforcement learning

Simulation based optimization of a stochastic supply chain considering supplier disruption: An agent-based modeling and reinforcement learning

Towards a unified approach in the modeling of fibrosis: A review with research perspectives

PI-FLAME: A parallel immune system simulator using the FLAME graphic processing unit environment

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