High-Mix, Low-Volume (HMLV) manufacturing or assembly refer to environments in which a wide variety of product lines "high-mix" are manufactured or assembled in relatively small quantities "low-volume". Designing a manufacturing or assembly system to operate efficiently and effectively in this environment, and to achieve the same level of productivity as systems designed for Low-Mix, High-Volume (LMHV) environments, requires that a high degree of flexibility, agility, and adaptability of both the machines and the people be built into the design. This flexibility, agility and adaptability in turn creates a number of challenges that need to be addressed in depth, ranging from solving the resulting complex planning and scheduling problems, to designing and operating efficient and effective external and internal logistics processes, to managing the workforce and maintaining employee well-being. This research project aims to address these challenges by developing appropriate optimization models and algorithms that take into account the stochasticity of the system's critical parameters, such as demand mix and volume, yield, and machine downtime, as well as the cognitive and ergonomic overload of the operators. These optimization models usually fall into the class of stochastic mixed-integer programming problems, and some even belong to the class of bilevel stochastic mixed-integer optimization problems.