This paper addresses the community detection problem as one of the significant problems in the field of social network analysis. The goal of the community detection problem is to find sub-graphs of a network where they have high density of within-group connections, while they have a lower density of between-group connections. Due to high practical usage of community detection in scientific fields, many researchers developed different algorithms to meet various scientific requirements. However, single-objective optimization algorithms may fail to detect high quality communities of complex networks. In this paper, a novel Multi-objective Multi-agent optimization Algorithm, named the MAOA is proposed to detect communities of complex networks. The MAOA aims to optimize modularity and community score as objective functions, simultaneously. In the proposed algorithm, each feasible solution is considered as an agent and the MAOA organizes agents in Multiple groups. The MAOA uses new search operators based on social, autonomous and self-learning behaviors of agents. Moreover, the MAOA uses the weighted sum method (WSM) in finding the global best agent and leader agent of each group. The Pareto solutions obtained by the MAOA is evaluated in terms of several performance measures. The results of the proposed method are compared with the outputs of three meta-heuristics. Experiments results based on five real-world networks show that the MAOA is more efficient in finding better communities than other methods.

This paper addresses a bi-objective mixed integer optimization model under uncertainty for population partitioning problem. The objective functions are to minimize the number of communications between partitions and to balance their population. The main constraints are defined for creating contiguous and compact partitions as well as assigning uniquely each basic unit to one partition. To deal with the uncertainty of parameters, a robust programming method is proposed that causes the uncertainty parameters lie between the interval of bestcase (the deterministic mode) and worst-case (the highest uncertainty level for all parameters). As the suggested method is NP-Hard, three meta-heuristic algorithms NSGAII, PESA, and SPEA are developed and, to evaluate the efficiency of the algorithms, 10 small-size examples, 10 medium-size examples and, 10 large-size examples are generated and solved. According to computational results, the SPEA has the best performance. The method is examined for a real-world application, as a case study in Iran.

Numerous optimization problems comprise uncertain data in practical circumstances and such uncertainty can be suitably addressed using the concept of fuzzy logic. This paper proposes a computationally efficient solution methodology to generate a set of fuzzy non-dominated solutions of a fully fuzzy Multi-objective linear programming problem, which incorporates all its parameters and decision variables expressed in form of triangular fuzzy numbers. The fuzzy parameters associated with the objective functions are transformed into interval forms by utilizing the fuzzy-cuts, which subsequently generates the equivalent interval valued objective functions. The concept of centroid of triangular fuzzy numbers derives the deterministic form of the constraints. Furthermore, the scalarization process of weighting sum approach and certain concepts of interval analysis are used to generate the fuzzy non-dominated solutions from which the compromise solution can be determined based on the corresponding real valued expressions of fuzzy optimal objective values resulted due to the ranking function. Three numerical problems and one practical problem are solved for illustration and validation of the proposed approach. The computational results are also discussed as compared to some existing methods.

According to the real projects’ data, activity durations are affected by numerous parameters. In this research, we have developed a Multi-resource Multi objective Multi-mode resource constrained scheduling problem with stochastic durations where the mean and the standard deviation of activity durations are related to the mode in which each activity is performed. The objective functions of model were to minimize the net present value and makespan of the project. A simulation-based optimization approach was used to handle the problem with several stochastic events. This feature helped us to find several solutions quickly while there was no need to take simplification assumptions. To test the efficiency of the proposed algorithm, several test problems were taken from the PSPLIB directory and solved. The results show the efficiency of the proposed algorithm both in quality of the solutions and the speed.

In today's dynamic and unpredictable world, the planning and management of humanitarian supply chains hold paramount importance. Efficient logistics management is crucial for effectively delivering essential aid and resources to affected areas during disasters and emergencies, ensuring timely support and relief to vulnerable populations. In this research, we addressed a novel humanitarian supply chain network design problem that considers product differentiation and demand uncertainty. Specifically, we simultaneously incorporate non-perishable, perishable, and blood products as critical components of the network. The problem is formulated as a Multi-objective mixed-integer linear programming model aiming to minimize the total cost and total traveled distance of products by making location, allocation, and production decisions. To enhance realism, we account for demand uncertainty in affected areas. To tackle this challenging problem, we proposed a two-phase solution methodology. Firstly, we employed a robust optimization approach to establish a deterministic counterpart for the stochastic model. Subsequently, an efficient fuzzy programming-based approach reformulates the model into a single-objective form, effectively accommodating decision-makers' preferences. Numerical instances are solved to investigate the performance of the model and solution methodologies. The results demonstrate the effectiveness of our fuzzy approach in finding non-dominated solutions, enabling decision-makers to explore trade-offs. Also, sensitivity analyses were conducted to provide more insights. Finally, some suggestions are presented to extend the current work by feature researchers.

This study presents a Multi-objective nurse scheduling model by considering andintegrating teamwork and decision making styles in order to maximize jobsatisfaction. To achieve high job satisfaction, teamwork that minimizesincompatibility among team members is considered. Teamwork has a sustainable impact on job satisfaction in healthcare. In this study, a new mathematical model isproposed for scheduling nurses based on teamwork. First, nursing teams are generated by considering decision making styles. Then, each team is assigned towork shifts in the planning horizon. The unique Multi-objective mathematical model considers the inconsistency of nurses’,decision making styles, reliability ofteams, allocation costs and penalty of violating soft constraints as the objective functions. A real case study is considered to show the applicability of the proposedmodel. Finally, the proposed Multi-objective model is solved using the goal programming method. Sensitivity analysis shows the robustness of the proposedmathematical programming model and solution methodology.