Electric vehicles (EVs) provide a cleaner alternative that not only reduces greenhouse gas emissions but also improves air quality and reduces noise pollution. The consumer market for electrical vehicles is growing very rapidly. Designing a network with adequate capacity and types of public charging stations is a challenge that needs to be addressed to support the current trend in the EV market. In this research, we propose a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers' behaviors. Some of the key random data parameters considered in this study are: EV's dwell time at parking location, battery's state of charge, distance from home, willingness to walk, drivers' arrival patterns, and traffic on weekdays and weekends. The two-stage model uses the sample average approximation method, which asymptotically converges to an optimal solution. To address the computational challenges for large-scale instances, we propose an outer approximation decomposition algorithm. We conduct extensive computational experiments to quantify the efficacy of the proposed approach. In addition, we present the results and a sensitivity analysis for a case study based on publicly available data sources.

In this paper, we present an integrated multi-objective framework of a single machine for a single cutting tool problem. Our maintenance policy is based on performing minimal repairs in case of a minor failure and Preventive Maintenance (PM) to avoid a major failure that results in the replacement of the tool. This framework allows simultaneous optimization of the two conflicting time and cost objectives. A redundant system is proposed as a part of the model to assist the production line under a major failure. In addition, the tool’s preventive maintenance time is synchronized with the completion of the machine tool’s work cycle to reduce the machine’s set-up time. The model was optimized using a customized Non-dominated Sorting Genetic Algorithm (NSGA-II). An experimental study based on real-market data was conducted and the results were compared with the ones obtained from classical methods.