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When earthquakes occur, solar-powered unmanned aerial vehicles (SUAVs), deployed as communication relay points, can construct a signal relay network to assist the ground mobile communication vehicles in resuming communication. Considering the urgency of disaster relief, a practical, accurate, and robust modeling method for multiple SUAVs deployments is vital. For this concern, this article first formalizes the deployment problem of multiple solar-powered UAVs in communication recovery by extending Group MultiRole Assignment (GMRA) (UGRA). In the second step, the success in this assignment process depends on the choice of the agent evaluation method. The evaluation benchmark in UGRA is SUAV path planning in a complex environment with uncertain subpaths and accumulative attitude errors. In response to this issue, we propose two innovative algorithms: 1) dynamic curve path-planning algorithm (DCPPA) and 2) greedy curved straight path-planning algorithm (GCSPPA). Moreover, with the time requirement in mind, one sufficient condition and one necessary condition are established to help the DCPPA achieve fast convergence. With these two novel agent evaluation algorithms, UGRA can rapidly deploy multiple SUAVs to establish a collaborative relay network within an acceptable time. Finally, simulation experiments at different scales are carried out to demonstrate the accuracy and effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

As the number of cloud services (CSs) offering similar functionality is growing, more attention has been payed on the quality of service (QoS) of CSs. However, in a dynamic cloud environment, the explicit and inherent variation of QoS causes the single CS selection via collaborative filtering techniques (CSS-CFT) to be challenging. A variation-aware approach via collaborative QoS prediction is proposed to select an optimal CS according to users’ non-functional requirements. Based on time series QoS data, this approach utilizes a set of specific cloud models to quantify the variation characteristics of QoS from the four aspects including central tendency, variation range, frequency of variation and period. To exactly identify the neighboring users for a current user, this paper employs the double Mahalanobis distances to measure the similarity of QoS cloud models. The variation-aware CSS-CFT is formulated as a multi-criteria decision-making problem, and an improved TOPSIS method is exploited to solve it, by considering both the objective QoS variation and subjective user preferences during different time periods. The experiments based on a real-world dataset demonstrate that the proposed approach can enhance the accuracy of CSS-CFT in a high-variance environment without noticeable increase of selection time, in comparison to the existing approaches. [ABSTRACT FROM AUTHOR]

Recently, growing cloud services (CSs) have been leased by organizations for high-performance computation and massive data storage of data-intensive applications (DiAs). To improve the resource utilization of leased CSs, it has become a challenging task to optimize infrastructure as a service CS composition for DiAs (ICSCDs) from the user side. This paper proposes a resource utilization-aware collaborative optimization approach. Targeting the collaboration features of tasks in a DiA, the environments—classes, agents, roles, groups, and objects model is used to formalize the ICSCD problem from the perspective of role-based collaboration. Aiming at the dynamic characteristics of the cloud environment, an integrated method is presented to evaluate the qualification of CSs via the interval numbers with multiple parameters. Based on the exact qualification values, the ICSCD can be optimized for improving the resource utilization of the CSs. A solution using the IBM ILOG CPLEX optimization package is put forward to solve the problem. The experimental results demonstrate that the approach can provide high precision, performance, stability, resource utilization, and low usage cost for the resource utilization-aware ICSCD from the user side. [ABSTRACT FROM AUTHOR]

Role negotiation is a pivotal step in the role-based collaboration (RBC) process. There are many factors to be considered when decision makers set up criteria to evaluate group performance and agents’ abilities. In this paper, we investigate the applications of three traditional multicriteria decision making methods into role negotiation of RBC, i.e., simple additive weighting, multiplication exponent weighting, and weighted distance. The goal is to acquire ideal and satisfactory methods to set up the evaluation criteria for the role negotiation task. The main contributions of this paper include: 1) a method of generating the matrix of agent qualifications, with a discrete, usually, limited, and finite set of prespecified attributes and values; 2) the upper and lower thresholds of a role’s ability requirements, which are critical parameters for a workable team and its performance; and 3) the potential influences of specialist agents and generalist agents on the group performance. The proposed methods are verified by simulation experiments. The simulation results show that the proposed methods are reasonable, feasible, and practical. [ABSTRACT FROM AUTHOR]

It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments—classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues. [ABSTRACT FROM AUTHOR]

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team’s viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]

Task allocation is a critical phase of project management. Tree-type structures are frequently used constraints to obtain a pertinent task allocation. They can illustrate where one task may require numerous agents and when an agent can be assigned to different tasks (roles). The process of task allocation is made more complex when administrators need to satisfy sequential and fixed branch relationships between/among tasks (roles). This paper formalizes the tree-structured task allocation problem (TSTAP) with group multirole assignment (GMRA) and proves necessary conditions, the necessary and sufficient condition, as well as sufficient conditions, of TSTAP. The formalization makes it easy to find a solution with the IBM ILOG CPLEX optimization package (CPLEX). The necessary conditions improve the CPLEX solution by eliminating infeasible cases. The necessary and sufficient condition describes the solution space of TSTAP completely. Another exciting result is that the sufficient conditions can not only improve the CPLEX solution by describing a practical approximate solution space but also help decision-makers and human resource officers organize a team in order to successfully assign tasks. The proposed approach is verified by simulation experiments with respect to a real-world problem. The experimental results present the practicability of the proposed solutions in this paper. This paper was motivated by general cooperative projects whose tasks have tree-structured relationships. This can make the problem of successful multitask assignment extremely challenging. The traditional method of assignment such as the KM algorithm can no longer solve this problem. To solve the assignment problem with tree-structured relationships, an efficient many-to-many assignment with constraints is required. The proposed approach provides theoretical and technical foundations for efficient assignment of TSTA, which can not only provide a viable and effective assignment scheme for TSTA problems but also help human resource officers to formulate reasonable plans according to the relationships between/among tasks. [ABSTRACT FROM AUTHOR]

The multi-valued evaluations of quality of service (QoS), the complicated constraints between cloud services (CSs) and the collaborative resource assignments add many difficulties to the problem of CS composition for data-intensive applications (DiA) in a hybrid cloud (CSCD-HC). Solving the CSCD-HC problem has become a challenging task due to the uncertain QoS, the diverse hardware configurations and the flexible pricing about CSs. This paper proposes a collaborative optimization approach for CSCD-HC. This approach models a DiA as a role-based collaboration (RBC) system and employs the environments—classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD-HC problem with complicated constraints. To deal with the multi-valued QoS evaluations, this paper exploits the cloud model theory to analyze the performance of CSs, and presents a new method utilizing the Mahalanobis distance to improve the similarity calculation of QoS cloud models. Based on it, the qualification of candidate CSs can be precisely measured for supporting CS composition. A solution via the IBM ILOG CPLEX optimization package is put forward to solve the CSCD-HC problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing CSCD-HC. [ABSTRACT FROM AUTHOR]

Collaboration is complex. To solve a problem occurring in collaboration, using computers, we must first define and specify the problem. This paper presents a challenging problem in collaboration, called group role assignment with cooperation and conflict factors (GRACCFs). This problem’s solution aims at creating a high-performance group by role assignment with consideration of cooperation and conflicts between agents. The contribution of this paper is the formalization of the proposed problem, a confirmation of the complexity of the problem, a practical solution that uses the IBM ILOG CPLEX optimization package (ILOG), a verification of the benefits of solving the GRACCF problem by simulations and a practical way of collecting the required factors to support decision makers in solving such a problem within a real-world scenario. Experiments are used to verify the efficiency of the proposed ILOG solution. [ABSTRACT FROM AUTHOR]

Role assignment is a critical element in the role-based collaboration process. There are many factors to consider when decision makers undertake this task. Such factors include a decision maker’s preferences and the team’s performance. This paper proposes a series of methods, relative to these factors, to solve the group role assignment with balance problem through an association with the one clause at a time approach that is a well-accepted and logic-based association rule mining method. The proposed methods are verified by simulation experiments. The experimental results present the practicability of the proposed solutions. Using the proposed methods, decision makers need only to establish coarse-grain preferences. The fine-grain preferences can be mined. Furthermore, a balance is obtained between the fine-grain preferences and the team’s performance. [ABSTRACT FROM PUBLISHER]

Role assignment is a critical element in the role-based collaboration process. There are many different requirements to be considered when undertaking this task. This correspondence paper formalizes the group multirole assignment (GMRA) problem; proves the necessary and sufficient condition for the problem to have a feasible solution, provides an improved IBM ILOG CPLEX optimization package solution, and verifies the proposed solution with experiments. The contributions of this paper include: 1) the formalization of an important engineering problem, i.e., the GMRA problem; 2) a theoretical proof of the necessary and sufficient condition for GMRA to have a feasible solution; and 3) an improved ILOG solution to such a problem. [ABSTRACT FROM AUTHOR]

Role assignment is a critical element in the role-based collaboration process. There are many constraints to be considered when undertaking this task. This paper formalizes the group role assignment problem when faced with the constraint of conflicting agents, verifies the benefits of solving the problem, proves that such a problem is a subproblem of the extended integer linear programming (x-ILP) problem, proposes a practical approach to the solution, and assures performance based on the results of experiments. The contributions of this paper include: 1) formalization of the proposed problem; 2) verification of the benefit achieved by avoiding conflicts in role assignment through simulation; 3) theoretical proof that conflict avoidance is a subproblem of the x-ILP problem that is nonpolynomial-complete; and 4) a practical solution based on the IBM ILOG CPLEX optimization package (ILOG) and verification of the scale of problems that can be solved with ILOG. The proposed approach is validated by simulation experiments. Its efficiency is verified by comparison with the previous exhaustive search-based approach. [ABSTRACT FROM PUBLISHER]

Role assignment is a critical task in role-based collaboration. It has three steps, i.e., agent evaluation, group role assignment, and role transfer, where group role assignment is a time-consuming process. This paper clarifies the group role assignment problem (GRAP), describes a general assignment problem (GAP), converts a GRAP to a GAP, proposes an efficient algorithm based on the Kuhn–Munkres (K-M) algorithm, conducts numerical experiments, and analyzes the solutions' performances. The results show that the proposed algorithm significantly improves the algorithm based on exhaustive search. The major contributions of this paper include formally defining the GRAPs, giving a general efficient solution for them, and expanding the application scope of the K-M algorithm. This paper offers an efficient enough solution based on the K-M algorithm that outperforms significantly the exhaustive search approach. [ABSTRACT FROM PUBLISHER]

Many-to-Many (M-M) role transfers are generalized problems that are encountered in collaboration. Exhaustive-search-based algorithms are too computationally intensive. This paper introduces the Kuhn–Munkres (or Hungarian) algorithm for the general assignment problems (GAPs) and proposes a new efficient algorithm to solve the M-M role transfer problems by converting them to the GAPs. The experiments and results validate the proposed algorithms. [ABSTRACT FROM PUBLISHER]