Can Producers’ Price War End Up in an Optimal Allocation?

The paper presents a theoretical framework for the phenomenon of the price war in the context of general equilibrium, with special attention to the production system. The natural question that arises is whether Nash-optimal production plans being the reactions to the changing prices can finally approximate a Nash-optimal production plan at the end of this war. To provide an answer, the production system is described as a parametric-multicriteria game. Referring to some results on the lower semicontinuty of the parametric weak-multicriteria Nash equilibria, we provide a positive answer for the stated problem.

Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

The power-to-gas (P2G) technology transforms the unidirectional coupling of power network and natural gas network into bidirectional coupling, and its operational characteristics provide an effective way for wind and solar energy accommodation. The paper proposes a bi-level optimal dispatch model for the integrated energy system with carbon capture system and P2G facility. The upper model is an optimal allocation model for coal-fired units, and the lower model is an economic dispatch model for the integrated energy system. Moreover, the upper model is solved by transforming the model into a mixed-integer linear programming problem and calling CPLEX, and the lower model is a multi-objective planning problem, which is solved by improving the small-habitat particle swarm algorithm. Finally, the simulation is validated by the MATLAB platform, and the results show that the simultaneous consideration of carbon capture system and P2G facility improves the economics of the integrated energy system and the capacity of wind and solar energy accommodation.

Pigouvian vs. Coasian approach: Ideas, values, perspectives

The paper reveals the characteristics of competing approaches — Pigouvian and Coasian — to identify the grounds for state regulation. We outline the connections between Pigouvian and Coasian approaches with the values and prospects for their advancement in the field of political decision-making in the context of demand for economic knowledge and the possibilities of organizing compensating transactions. These connections are considered in the light of the externalities problem as one of the manifestations of market failure, as well as different internalization options. We also clarify the provisions from the theory of externalities in terms of their definition, classification and correlation with the conditions for optimal allocation of resources. The key types of structural alternatives for correcting market flaws are considered, and the main properties of the Pigouvian and Coasian approaches in economics, as they relate to the problem of market and government flaws, are determined. This helps explain why the Coasian approach cannot be considered synonymous with liberal fundamentalism. Finally, we indicate the relationship between normative conclusions and prospects of functionalism and two types of fundamentalism in the field of political decision-making. Using the example of intertemporal externalities, the difference in the approaches of Coasianism and Pigouvianism to their internalization is demonstrated.

Optimal Allocation of a Hybrid Photovoltaic Biogas Energy System Using Multi-Objective Feasibility Enhanced Particle Swarm Algorithm

This paper aims to investigate a hybrid photovoltaic (PV) biogas on-grid energy system in Al-Ghabawi territory, Amman, Jordan. The system is accomplished by assessing the system’s reliability and economic viability. Realistic hourly measurements of solar irradiance, ambient temperature, municipal solid waste, and load demand in 2020 were obtained from Jordanian governmental entities. This helps in investigating the proposed system on a real megawatt-scale retrofitting power system. Three case scenarios were performed: loss of power supply probability (LPSP) with total net present cost (TNPC), LPSP with an annualized cost of the system (ACS), and TNPC with the index of reliability (IR). Pareto frontiers were obtained using multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) algorithm. The system’s decision variables were the number of PV panels (Npv) and the number of biogas plant working hours per day (tbiogas). Moreover, three non-dominant Pareto frontier solutions are discussed, including reliable, affordable, and best solutions obtained by fuzzy logic. Double-diode (DD) solar PV model was implemented to obtain an accurate sizing of the proposed system. For instance, the best solution of the third case is held at TNPC of 64.504 million USD/yr and IR of 96.048%. These findings were revealed at 33,459 panels and 12.498 h/day. Further, system emissions for each scenario have been tested. Finally, decision makers are invited to adopt to the findings and energy management strategy of this paper to find reliable and cost-effective best solutions.

Optimal Allocation of Human Resource Structure Based on Capability Maturity Model Integration

In order to improve the economic benefits of enterprises and provide a scientific human resource management method for enterprises, an optimal allocation method of human resource structure based on the integration of capability maturity model is proposed. According to the capability maturity model and its maturity level, the capability maturity integration model is established, and the optimal allocation algorithm of human resources is designed according to the model principle. By constructing the personnel quality evaluation matrix and personnel allocation matrix, the human resource allocation model is established, and the cooperative game method is used to dynamically optimize the human resource allocation model. The experimental results show that this method effectively improves the economic benefits of enterprises, improves the efficiency of human resource allocation, and completes the preset goal.

Optimal Autonomic Management of Service-Based Business Processes in the Cloud

Cloud computing is an emerging paradigm that provides hardware, platform and software resources as services over the internet in a pay-as-you-go model. It is being increasingly used for hosting and executing service-based business processes. These business processes are exposed to dynamic evolution during their life-cycle due to the highly dynamic evolution of cloud environments. The main adopted technique is to couple cloud computing with autonomic management in order to build autonomic computing systems. Almost all the existing approaches on autonomic computing have been focused on modeling and implementing autonomic mechanisms without paying any attention to the optimization of the autonomic management cost. Therefore, in this paper, we propose a novel approach based on binary linear program for determining the optimal allocation of cloud resources to manage a service-based business process which guarantees the specific requirements of customers and minimizes the management monetary cost. Then, to validate our approach under realistic conditions and inputs, we extend the CloudSim simulator to model and simulate the behaviour of business processes and their management in a cloud environment. Experiments conducted on two real datasets highlight the effectiveness of our approach.