Uncertainty exists in various decision-making problems. There is no surprise that, with the ever-increasing complexity of problems, sound decisions should be made based on uncertain information granules, such as probabilistic information granules, fuzzy set, interval and set, and rough set. In uncertain mathematical programming, different branches of uncertainty theories play an important role in formulating and solving uncertain decision-making problems.

This special issue aims to deliver a platform where researchers coming from academia and industry can elaborate the state-of-the-art information granulation methods (i.e., how to transform the real-life data to available information granules, present the innovative uncertain programming methodologies, and report the linkages between methodology and practice of uncertain programming).

Potential topics include but are not limited to the following:

• Stochastic programming
• Fuzzy programming
• Interval programming
• Rough programming
• Granular computing
• Uncertain multiobjective programming
• Uncertain multilevel programming
• Uncertain dynamic programming
• Evolutionary algorithms
• Applications to the fields of transportation networks, logistics and supply chains, portfolio optimization, marketing engineering, risk management, robust design, network reliability, redundancy optimization, decision support, and related areas