Demand-side management (DSM) plays a key role in the future of smart grids. Recently, DSM researchers have developed various mathematical models to optimize the demand response. Most of these works ignore the channel impairments' impact on the optimization process. In this paper, we propose a new noncooperative game theoretic model for the management of smart grid's demand considering the packet error rate in our formulation. We set the Nash equilibrium conditions for the proposed model. Under an assumption on the form of the utility functions, we develop a 0-1 mixed linear programming approach to compute nondominated extreme Nash equilibria. Results on a numerical example are provided and some useful insights are presented. Under some assumptions and a fully proven proposition, a feasible nondominated Nash equilibrium solution is found. Finally, we report and comment on computational experiments on randomly generated smart grid DSM game instances with different characteristics.

Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used to provide communication services for ground users in different scenarios, such as transportation systems, disaster situations, emergency cases, and surveillance. However, covering a specific area under a dynamic environment for a long time using UAV technology is quite challenging due to its limited energy resources, short communication range, and flying regulations and rules. Hence, a distributed solution is needed to overcome these limitations and to handle the interactions among UAVs, which leads to a large state space. In this paper, we introduced a novel distributed control solution to place a group of UAVs in the candidate area in order to improve the coverage score with minimum energy consumption and a high fairness value. The new algorithm is called the state-based game with actor–critic (SBG-AC). To simplify the complex interactions in the problem, we model SBG-AC using a state-based potential game. Then, we merge SBG-AC with an actor–critic algorithm to assure the convergence of the model, to control each UAV in a distributed way, and to have learning capabilities in case of dynamic environments. Simulation results show that the SBG-AC outperforms the distributed DRL and the DRL-EC3 in terms of fairness, coverage score, and energy consumption.

This paper presents a new framework for the optimization of real-life multiple-objective vehicle routing problems with multiple time windows. This framework uses a hybrid variable neighborhood tabu search heuristic that chooses Pareto nondominated solutions from the search space of solutions that satisfy a set of Nash equilibrium conditions for a multiple-agent game theory model. Even though the framework is general and can tackle different classes of vehicle routing problems, it is herein tested on three objectives: minimizing the total travel cost (expressed in time units), maximizing the minimal customers’ utility, and maximizing the minimal drivers’ utility. The results on real-life instances provided by a Canadian transportation company highlight the benefits of the multiple-criteria model; an important motivation to the transportation industry for its real-life implementation.