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In this research, different optimization models are developed to solve the preventive maintenance (PM) optimization problem in a maintainable multi-state series–parallel system. The objective is to determine for each component in the system the maintenance period minimizing a cost function under the constraint of required availability and for a specified horizon of time. Four genetic models based on the cost associated with maintenance schedule and availability characteristic parameters are constructed and analyzed. They are genetic algorithm (GA), hybridization GA and local search (GA-LS), fuzzy logic controlled GA (FLC-GA), and hybridization FLC-GA and LS. The experiment analyzes and compares the efficiency between them. These experiments investigate the effect of the parameters of the GA on the structure of optimal PM schedules in multi-state multi-component series–parallel systems. Results show that the hybridization FLC-GA and LS outperform the other algorithms.

Several researches have been investigated on Multi-Objective Redundancy Allocation Problems (MORAPs), but none of them have considered the redundant dependency at the design stage. This latter which is a special kind of failure dependency can affect significantly the system performance. Due to this fact, this paper deals with the multi-objective system design optimisation with dependent components by focusing on two objectives: maximisation of system availability and minimisation of system cost with components choice and weight constraints. A system consisting of manyk-out-of-nrepairable subsystems connected in series is considered. The components of a subsystem are supposed to be identical and may be dependent. They are selected from a set of available component types. In addition to the redundancy level and the number of repair teams allocated to each subsystem, the choice of components type and the dependency level are also considered as decision variables. Since the described problem is NP hard, we propose three multi-objective meta-heuristic algorithms based on Non-dominated Sorting Genetic Algorithm (NSGA II) and Strength Pareto Evolutionary Algorithm (SPEA II) with different constraints handling. An exact method is also applied. To analyse their performances, numerical applications are provided and comparisons based on different well-known metrics are presented. [ABSTRACT FROM AUTHOR]

This study presents a mathematical formulation to optimize contracting capacity strategies of multisources of energy for institutional or industrial consumers considering demand uncertainties. The objective consists of minimizing the total costs composed of the different types of energy contract capacity costs, penalty price, and an ecofriendly factor. The penalty price is charged on the demand of energy exceeding the total contract capacities. The ecofriendly factor encourages the use of renewable energy and reduces the traditional energy used in the optimal mix of energy sources. The proposed model is tested based on demand of energy inspired from real data. These numerical experiments are analyzed to illustrate the impact of encouraging the use of renewable energy sources by introducing the ecofriendly factor and the influence of penalty price and uncertainty in the demand of energy. The results show that in the presence of low penalty price or low uncertainty a large amount of ecofriendly support is needed for using more renewable energy sources in the optimal contract capacity combination. [ABSTRACT FROM AUTHOR]