This paper presents a real world case study where a multiobjective programming (MOP) framework under uncertainty is used for simultaneous integration of environmentally benign solvent (EBS) selection and in-process solvent (IPS) recycling. At the EBS selection level within this framework, the Hammersley stochastic annealing algorithm is applied to design candidate EBSs under uncertainty. This algorithm can efficiently optimize stochastic combinatorial optimization problems and generate a different set of candidate EBSs from that of the deterministic EBS selection model. At the IPS recycling level, Aspen Plus with a nonlinear programming technique is used to optimize the acetic acid recovery process. Then, these EBS selection and IPS recycling models are integrated under the MOP framework. At the MOP level, an efficient constraint MOP algorithm is employed to evenly approximate the Pareto solution surface (i.e., tradeoff surface). Four objectives-acetic acid recovery, process flexibility, and two environmental impacts based on LC50 and LD50-are evaluated. The resulting MOP framework provides very distinctive chemical and process design alternatives (i.e., Pareto optimal solutions), and uncertainties in this framework significantly affect the size and shape of the Pareto set. This novel MOP framework can be applied to any large-scale stochastic mixed-integer nonlinear optimization problems because this framework is computationally efficient even in the case of combinatorial explosion and uncertainty inclusion.