Productive Cluster Hire

Discovering a group of experts to complete a set of tasks that require various skills is known as Cluster Hire Problem. Each expert has a set of skills which he/she can offer and charges a monetary cost to offer their expertise. We are given a set of projects that need to be completed and on completion of each project, the organization gets a Profit. For performing a subset of given projects, we are given a predetermined budget. This budget is spent on hiring experts. We extend this problem by introducing the productivity and capacity of experts. We want to hire experts that are more productive, and this factor is determined on the basis of their past experience. We also want to make sure that no expert is overworked as it is not possible for a single expert to provide his/her expertise for unlimited times. Our goal is to hire as many experts as possible in which the sum of their hiring costs (i.e., salary) is under the given budget as we are interested to maximize the profit and also maximize the productivity of the group of experts, our problem is a bi-objective optimization problem. To achieve this, we propose two different approaches that maximize our Profit and Productivity

Improving Quality of the Solution for the Team Formation Problem in Social Networks Using SCAN Variant and Evolutionary Computation

Social Network Analysis helps to visualize and understand the roles and relationships that ease or impede the collaboration and sharing of the information and knowledge in an organization. In this research work, we will focus on the Team Formation Problem (TFP) which is an open problem where we need to identify an ideal team, with members of complementary talent or skills, to solve any given task. Current research suggests that TFP solutions have been attempted with evolutionary computation approach using Cultural Algorithms (CA) and Genetic Algorithms (GA). However, SCAN (Structural Clustering Algorithm for Networks) variants such as WSCAN (Weighted Structural Clustering Algorithm for Networks) demonstrate a high capability to find solutions for another type of network problems. In this thesis, we first propose to use WSCAN-TFP algorithm to deal with the problem of team formation in social networks, and we our findings indicate that WSCAN-TFP algorithm worked faster than the evolutionary algorithms counterparts but was of lower performance compared to CAs and GAs. Next, we propose two hybrid solutions by combining GA and CA with a modified WSCAN-TFP algorithm. To test the performance of our proposed approaches, we define multiple quality criteria based on communication cost (CC), average fitness score (AFS) and average processing time. We used big datasets from DBLP nodes network with sizes 50K and 100K. The results show that our proposed methods HGA and HCA can find the near-optimal solutions faster with minimum communication cost with the improvement of $\approx 66\%$ and $\approx 57\%$ in average fitness in comparison to existing GA and CA methods respectively

Cluster Hire in Social Networks Using Modified Weighted Structural Clustering Algorithm for Networks (MWSCAN)

The concept of effective collaboration within a group is immensely used in organizations as a viable means for improving team performance. Any organization or prominent institute, who works with multiple projects needs to hire a group of experts who can complete a set of projects. When hiring a group of experts, numerous considerations must be taken into account. In the Cluster Hire problem, we are given a set of experts, each having a set of skills. Also, we are given a set of projects, each requiring a set of skills. Upon completion of each project, a profit is generated for an organization. Each expert demands a monetary cost (i.e., salary) to provide his/her expertise in projects. The Cluster Hire problem can be solved by hiring a group of experts for a set of projects within the constraints of a budget for hiring and a working capacity of each expert. An extension to this problem is assuming there exists a social network amongst the experts, which contains their past collaboration information. If two experts have collaborated in the past, then they are preferred to be on the same team in the future. The goal of our research is to find a collaborative group of experts who can work effectively together to complete a set of projects. Currently, the solution to the Cluster Hire problem in social networks is achieved using greedy heuristic algorithms and Integer Linear Programming (ILP) approach. Greedy algorithms often generate fast results, but they make locally optimal choices at each step and do not produce global optimal results. The drawbacks of the ILP approach are that it requires a considerable amount of memory for the creation of variables and constraints and also has a very high processing time for large networks. Whereas, Weighted Structural Clustering Algorithm for Networks (WSCAN) has been proved to produce faster results for Team Formation Problem (i.e., hiring a team of experts for a single project), which is a special case of Cluster Hire problem. We are proposing to solve the Cluster Hire problem in social networks using Modified Weighted Structural Clustering Algorithm for Networks (MWSCAN). We run our experiments on a large dataset of 50K experts. ILP is not capable of working with such large networks. Therefore, we will be comparing our results with the greedy heuristic solution. Our findings indicate that the MWSCAN algorithm generates more efficient results in terms of the number of projects completed and profit produced for the given budget compared to the greedy heuristic algorithm to solve the Cluster Hire problem in social networks

Cluster Hire in Social Networks Using Modified Weighted Structural Clustering Algorithm for Networks (MWSCAN)

The concept of effective collaboration within a group is immensely used in organizations as a viable means for improving team performance. Any organization or prominent institute, who works with multiple projects needs to hire a group of experts who can complete a set of projects. When hiring a group of experts, numerous considerations must be taken into account. In the Cluster Hire problem, we are given a set of experts, each having a set of skills. Also, we are given a set of projects, each requiring a set of skills. Upon completion of each project, a profit is generated for an organization. Each expert demands a monetary cost (i.e., salary) to provide his/her expertise in projects. The Cluster Hire problem can be solved by hiring a group of experts for a set of projects within the constraints of a budget for hiring and a working capacity of each expert. An extension to this problem is assuming there exists a social network amongst the experts, which contains their past collaboration information. If two experts have collaborated in the past, then they are preferred to be on the same team in the future. The goal of our research is to find a collaborative group of experts who can work effectively together to complete a set of projects. Currently, the solution to the Cluster Hire problem in social networks is achieved using greedy heuristic algorithms and Integer Linear Programming (ILP) approach. Greedy algorithms often generate fast results, but they make locally optimal choices at each step and do not produce global optimal results. The drawbacks of the ILP approach are that it requires a considerable amount of memory for the creation of variables and constraints and also has a very high processing time for large networks. Whereas, Weighted Structural Clustering Algorithm for Networks (WSCAN) has been proved to produce faster results for Team Formation Problem (i.e., hiring a team of experts for a single project), which is a special case of Cluster Hire problem. We are proposing to solve the Cluster Hire problem in social networks using Modified Weighted Structural Clustering Algorithm for Networks (MWSCAN). We run our experiments on a large dataset of 50K experts. ILP is not capable of working with such large networks. Therefore, we will be comparing our results with the greedy heuristic solution. Our findings indicate that the MWSCAN algorithm generates more efficient results in terms of the number of projects completed and profit produced for the given budget compared to the greedy heuristic algorithm to solve the Cluster Hire problem in social networks

Exploiting geographical location for team formation in social coding sites

© 2017, Springer International Publishing AG. Social coding sites (SCSs) such as GitHub and BitBucket are collaborative platforms where developers from different background (e.g., culture, language, location, skills) form a team to contribute to a shared project collaboratively. One essential task of such collaborative development is how to form a optimal team where each member makes his/her greatest contribution, which may have a great effect on the efficiency of collaboration. To the best of knowledge, all existing related works model the team formation problem as minimizing the communication cost among developers or taking the workload of individuals into account, ignoring the impact of geographical location of each developer. In this paper, we aims to exploit the geographical proximity factor to improve the performance of team formation in social coding sites. Specifically, we incorporate the communication cost and geographical proximity into a unified objective function and propose a genetic algorithm to optimize it. Comprehensive experiments on a real-world dataset (e.g., GitHub) demonstrate the performance of the proposed model with the comparison of some state-of-the-art ones