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This paper focuses on new efficient and adaptive optimization algorithms to cope with the maintenance grouping problem for series, parallel, and complex systems. We propose a Particle Swarm Optimization approach to cope with small and medium problem sizes, and that will be used to benchmark existing heuristic solutions such as Genetic Algorithms. To address scalability and adaptability issues, we propose a new dynamic optimization algorithm based on a clustering technique. This clustering-based solution is formulated using an Integer Linear Programing approach to guarantee the convergence to global optimal solutions of the considered problem. We show the performance of the proposed approaches with a clear advantage to the clustering-based algorithm that we recommend for large industrial systems.

With the recent developments of autonomous vehicles, extensive studies were conducted on Adaptive Cruise Control (ACC, for short), which is an essential component of advanced driver-assistant systems (ADAS). The safety assessment must be performed on the ACC system before it goes to market. The validation process is generally conducted via simulation due to insufficient on-road data and the diversity of driving scenarios. Our paper aims to develop an optimization-based reference generation model for ACC, which can be used as a benchmark for assessment and evaluation. The model minimizes the difference between the actual and reference inter-car distance while respecting constraints about vehicle dynamics and road regulations. ACC sensors can be impacted by external factors, e.g., weather conditions and produce inaccurate data. To handle the resulting uncertainty, we propose a copula-based chance-constrained stochastic model to model the dependence between the random variables. Our numerical experiments show the performances of our model on randomly generated driving scenarios.

In this paper, we focus on n-player strategic chance-constrained games where the payoff of each player follows either Cauchy or normal distributions. We prove the equivalence between a Nash equilibrium problem and a variational inequality problem. We reformulate the latter as a nonlinear complementarity problem (NCP) through the Karush–Kuhn–Tucker (KKT) conditions. Then, we prove the existence of the Nash equilibrium via Brouwer’s fixed-point theorem. To show the efficiency of our approach, we perform numerical experiments on a set of randomly generated instances.