Leveraging human insight and intuition has been identified as having the potential for the improvement of traditional algorithmic methods. For example, in a video game, a user may not only be entertained but may also be challenged to beat the score of another player; additionally, the user can learn complicated concepts, such as multi-objective optimization, with two or more conflicting objectives. Traditional methods, including Tabu search and genetic algorithms, require substantial computational time and resources to find solutions to multi-objective optimization problems (MOPs). In this paper, we report on the use of video games as a way to gather novel solutions to optimization problems. We hypothesize that humans may find solutions that complement those found mechanically either because the computer algorithm did not find a solution or because the solution provided by the crowdsourcing of video games approach is better. We model two different video games (one for the facility location problem and one for scheduling problems), we demonstrate that the solution space obtained by a computer algorithm can be extended or improved by crowdsourcing novel solutions found by humans playing a video game.
The economic development and social well‐being of modern societies are highly dependent on networked critical infrastructures to satisfy the necessary demand. In the case that the prescribed demand cannot be satisfied with each component of a network normally loaded, certain components need to be overloaded. To study the influence of overloading on the network reliability, this research proposes a new flow‐redistribution rule based on minimal cuts to determine the components to overload when necessary and the way to redistribute the network flow. The proposed rule can help to keep the prescribed demand for a network satisfied, while trying to minimize the adverse influence of overloading on the network reliability. Based on the proposed rule, we then propose a bi‐objective optimization model to identify a network's critical components that, among all the possible combinations of components with the same amount, influence the network reliability the most when incapacitated, while considering the possibility of overloading and its influence on the reliability.
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