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In practical applications, there are many task allocation problems involving the participation of self-interested agents, including Witkey, crowdsourcing and electronic markets. In these cases, to improve the efficiency of task allocation, a reasonable distribution of utilities is critical. To the best of our knowledge, few studies have examined the complex task allocation and utility distribution of self-interested agents, and good solutions are lacking. To address this issue, the following works are done in this paper: first, based on a task allocation model for self-interested agents and by studying the Nash bargaining solution and the bargaining characteristics of the agents, an efficiency utility distribution algorithm satisfying individual rationality and budget effectiveness is proposed. Second, based on the best response strategy of the self-interested agent, a complex task allocation algorithm for multiple self-interested agents is proposed. Finally, the effectiveness of the proposed algorithm is verified by comparing the system revenues with other utility distribution and task allocation algorithms.

In this paper, a new generalized Nash bargaining framework is proposed for joint channel and power allocation in device-to-device (D2D) communication underlying cellular networks. In the considered system, cellular users (CUs) must jointly determine to share radio resources with D2D users (DUs) and to charge the DUs accordingly. The proposed framework aims to maximize the overall throughput of the communication system by guaranteeing the minimum rate of each CU and proportional fairness and efficient power allocation among the CUs and DUs. To make this NP-hard problem more tractable, it is decomposed into two sub-problems: channel assignment and power allocation. First, an optimal channel assignment strategy is derived using a max-weighted max-flow algorithm. Then, the optimal power allocation strategy for both DUs and CUs is analyzed using the Lagrangian multiplier method. Numerical results are presented to show the throughput performance characteristics of different resource allocation solutions. Comparisons between the proposed and traditional policies show the significant effect of fairness on the transmission performance.

Optimizing reservoir operation is critical to ongoing sustainable water resources management. However, different stakeholders in reservoir management often have different interests and resource competition may provoke conflicts. Resource competition warrants the use of bargaining solution approaches to develop an optimal operational scheme. In this study, the Nash bargaining solution method was used to formulate an objective function for water allocation in a reservoir. Additionally, the genetic and ant colony optimization algorithms were used to achieve optimal solutions of the objective function. The Mahabad Dam in West Azerbaijan, Iran, was used as a case study site due to its complex water allocation requirements for multiple stakeholders, including agricultural, domestic, industrial, and environmental sectors. The relative weights of different sectors in the objective function were determined using a discrete kernel based on the priorities stipulated by the government (the Lake Urmia National Restoration Program). According to the policies for the agricultural sector, water allocation optimization for different sectors was carried out using three scenarios: (1) the current situation, (2) optimization of the cultivation pattern, and (3) changes to the irrigation system. The results showed that the objective function and the Nash bargaining solution method led to a water utility for all stakeholders of 98%. Furthermore, the two optimization algorithms were used to achieve the global optimal solution of the objective function, and reduced the failure of the domestic sector by 10% while meeting the required objective in water-limited periods. As the conflicts among stakeholders may become more common with a changing climate and an increase in water demand, these results have implications for reservoir operation and associated policies.

A multi-energy system can supply both electric and thermal energy simultaneously to the end-users to achieve a high energy use-efficiency. However conventional operational strategies for multi-energy systems, in the existing works, do not consider renewable energy generation plants and CCHP plants as belonging to different interests. To cope with this problem, this article studies a cooperative operation framework model for a Wind-Solar-CCHP Multi-energy system. Instead of the conventional non-cooperative solution-based method, the optimal operation scheduling problem of a multi-energy system is described as an optimization problem of Nash Bargaining. Then the optimization problem of Nash Bargaining for the multi-energy system decomposed into two subproblems: social welfare maximization problem (P1) and energy trading payment problem (P2). The optimal energy transaction profiles can be obtained by solving the P1 problem and the problem of payment of energy trading between PV, WT plant agents and the CCHP plant agent can be obtained by solving the P2 problem. Both problems P1 and P2 can be formulated into mixed-integer linear programming problems and solved by the alternating directional multiplier method. Numerical studies demonstrate the correctness and effectiveness of the proposed cooperative operation framework and distributed algorithm for solving the two subproblems. Moreover, the bargaining-based cooperative operation method can further induce the multi-energy system to improve the operating profit with the reduced feed-in tariff of distributed generation.