Operations Research (OR) tools and techniques need the ability to balance conflicting objectives (goals or interests) where there are many alternative courses of action available to the decision-makers while resources, time, and funds are limited. Therefore, beyond the domain of theoretical knowledge and model building activities, OR has many applications in decision-making in societal and business problems. OR tools and techniques have applications in all facets of business operations irrespective of the nature of organizations, such as asset allocation, portfolio selection, fraud prevention, direct marketing campaigns, predicting customer response, supply chain planning, distribution, routing, scheduling, traffic flow optimization, staff allocation, merchandise optimization, product mix and blending, industrial waste reduction, energy conservation, optimization of the ore-handling process, simulation of protein folding, and city planning. Over the years, applications of OR have been extended to solve the problems of communication of information and socio-economic fields. In this context, the academic community needs to take the lead in the design, development, and demonstration of sustainable operational research models, endowed and supported by organizations.

However, the attributes of real systems are observed to deviate from their classical state due to the effect of different types of uncertainties. Randomness and impreciseness are the two major sources of uncertainties in real systems. Impreciseness or uncertainty exists within the parameters due to imperfect knowledge of information, measurement uncertainty, sampling uncertainty, mathematical modeling uncertainty, etc. Theories like probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, neutrosophic sets, and uncertainty theory available in the existing literature deal with such uncertainties. Nevertheless, the uncertain multi-objective characteristics in such problems have yet to be explored in-depth and there is much to achieve. Hence, different mathematical models of real-life multi-objective optimization problems can be developed on various uncertain frameworks with special emphasis on combinatorial optimization problems. Optimization of combinatory problems is a fundamental issue in various fields, including applied mathematics, computer science, engineering, management, and operations research. Nevertheless, combinatorial problems will have a more realistic sense if the multi-objective nature of the problem can be explored.

The aim of this Special Issue is to collate original research and review articles related to this field.

Potential topics include but are not limited to the following:

• Rough path analysis and random matrix
• Uncertainty and mathematical programming
• Hybrid uncertainty decision analysis
• OR tools in logistics scheduling
• Multi-criteria crew scheduling and timetabling
• Network optimization
• Complex networks and social networking
• Networking with multi-criteria games
• Risk analysis/modeling and sensitivity/robustness analysis
• Innovative applications of multi-criteria networking