Multiagent systems for modeling the information game in a financial market

Abdelaziz, Fouad Ben; M’Rad, Fatma Wyème Ben

doi:10.1111/itor.12944

Cited by 5 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MAS is a decentralized system that includes a set of independent, autonomous, and self-interested agents (Jennings and Wooldridge, 1995). And the MAS has been applied in many research areas, such as supply chain (Gharaei and Jolai, 2021), project scheduling (Zhao and Xu, 2022), and financial market (Ben Abdelaziz and Mrad, 2023). Here, the MAS consists of several project agents (PAs) and a coordinating agent (CA).…”

Section: Two-layer Modelmentioning

confidence: 99%

A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

You,

Xu,

et al. 2023

Int Trans Operational Res

View full text Add to dashboard Cite

Uncertain activity durations and multi‐skilled staff allocation are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized resource‐constrained multiproject scheduling problem with uncertain activity durations and multi‐skilled staff allocation. The robust project scheduling is employed to tackle uncertainty. We name it the robust decentralized resource‐constrained multiproject scheduling problem with multi‐skilled staff (RDMPSP‐MS). To formulate this problem, a two‐layer model containing local scheduling and global coordination decision‐making is established based on the multiagent system. To solve this model, a two‐layer approach (TLA) is developed. In the TLA, a starting time criticality heuristic method is adopted to handle the local scheduling problem, and a global resource allocation heuristic algorithm is designed to allocate multi‐skilled staff. The computational experiments are conducted on the adapted Multi‐Project Scheduling Problem LIBrary (MPSPLIB) data set. The results show that the proposed TLA outperforms seven heuristic algorithms based on priority rules regarding solution quality and efficiency on most problem subsets. It is further verified that the robustness of the baseline schedule deteriorates with the increase in problem size and activity duration variability level. Additionally, the results of this research provide practical guidance for managers who expect to determine reasonable project deadlines in a decentralized multiproject environment.

show abstract

Section: Two-layer Modelmentioning

confidence: 99%

A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

You,

Xu,

et al. 2023

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…Game-theoretic models have been used in blockchain to detect security attacks and to discover honest and selfish miners [26,27]. According to [28,29], game theory and advanced blockchain integration can guarantee profit maximization and secure transactions in microgrids. For instance, in [30], an extensive form game was presented to detect data collisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Secure and Robust Demand Response Using Stackelberg Game Model and Energy Blockchain

Samadi,

Ruj,

Schriemer

et al. 2023

Sensors

View full text Add to dashboard Cite

Demand response (DR) has been studied widely in the smart grid literature, however, there is still a significant gap in approaches that address security, privacy, and robustness of settlement processes simultaneously. The need for security and robustness emerges as a vital property, as Internet of Things (IoT) devices become part of the smart grid; in the form of smart meters, home energy management systems (HEMSs), intelligent transformers, and so on. In this paper, we use energy blockchain to secure energy transactions among customers and the utility. In addition, we formulate a mixed-strategy stochastic game model to address uncertainties in DR contributions of agents and achieve optimal demand response decisions. This model utilizes the processing hardware of customers for block mining, stores customer DR agreements as distributed ledgers, and offers a smart contract and consensus algorithm for energy transaction validation. We use a real dataset of residential demand profiles and photovoltaic (PV) generation to validate the performance of the proposed scheme. The results show the impact of electric vehicle (EV) discharging and customer demand reduction on increasing the probability of successful block mining and improving customer profits. Moreover, the results demonstrate the security and robustness of our consensus algorithm for detecting malicious activities.

show abstract

“…With the growing number of user energy transaction blockchain-based aspects of DR in MG, we must consider the revenue of the participant from a cooperative viewpoint, since this can actively prompt local RES consumption in the MG. In addition, game theory and advanced blockchain integration can ensure profit maximization (Abdelaziz and Mrad, 2023) and trust transactions in the MG (Cheng and Yu, 2019). When adjusting the load demand (transferring the peak load to valley hours), it is important to construct win-win benefits for the players in the MG.…”

Section: Coalitional Game-theoretic Approach With a Prediction For Drmentioning

confidence: 99%

Intelligent optimal demand response implemented by blockchain and cooperative game in microgrids

Bai

Zhang

2023

Int Trans Operational Res

View full text Add to dashboard Cite

Distributed renewable energy supply (RES) is a new pattern for the transformation of power grids. As a characteristic case of RES, microgrids have an advantage in convenient operation. However, the energy management of microgrids remains as a major concern. With the emergence of the decentralized paradigm, blockchain potentially provides a reliable energy data metering and payment for the whole life cycle of energy management. In particular, the demand response (DR) in the microgrid can stimulate demanders to spontaneously manage their load consumption and maintain the balance of the energy trading market. To achieve optimal DR, a dynamic pricing strategy under the blockchain and game‐theoretic approach is proposed. First, the blockchain‐based architecture is applied to ensure the reliability of energy data and lay a foundation of binding agreements for games. Then, the pricing mechanism under the cooperative game is formulated to optimize DR. Moreover, to help resolve the optimal response quantity and reduce the supply punishment of the RES providers, the DR requires accurate forecasting of the energy generation and consumption profiles. Therefore, an ensemble method Long Short‐Term Gate Support (LSTGS) is designed to forecast the RES and load power for intelligent agent to make decision on effective energy scheduling and DR. Taking the classic distributed energy context as a case study, we demonstrate the effectiveness of our approach and show that it can achieve DR profits maximized and improve the stability of the energy‐trading market.

show abstract

Multiagent systems for modeling the information game in a financial market

Cited by 5 publications

References 37 publications

A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

Secure and Robust Demand Response Using Stackelberg Game Model and Energy Blockchain

Intelligent optimal demand response implemented by blockchain and cooperative game in microgrids

Contact Info

Product

Resources

About