Integer programming approaches to the multiple team formation problem

2021 ◽  
pp. 105354
Author(s):  
Manoel Campêlo ◽  
Tatiane Fernandes Figueiredo
Keyword(s):  
Integer Programming ◽  
Team Formation ◽  
Formation Problem

Project Team Formation Based on Knowledge and Cooperation Degree

2010 ◽  
Vol 44-47 ◽  
pp. 3143-3147
Author(s):  
Xiao Rong Huang ◽  
Shun Sheng Guo ◽  
Li Bo Sun
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Calculation Method ◽  
Project Team ◽  
Team Formation ◽  
Individual Member ◽  
Formation Model ◽  
Collaborative Model ◽  
Team Members ◽  
Formation Problem

To aim at the project team formation problem, this study proposes a formation model based on knowledge and cooperation degree. The ability of individual member and cooperation degree of team members are considered. In addition ,it presents a way of measuring candidate’s ability about knowledge, and establishes a collaborative model to measure the cooperation degree between team members. Furthermore, a calculation method of knowledge and cooperation degree is proposed, and then a mathematical model is established. Finally it presented a solution base on Genetic Algorithm for this model.

scholarly journals An improved Jaya algorithm with a modified swap operator for solving team formation problem

2020 ◽  
Vol 24 (21) ◽  
pp. 16627-16641
Author(s):  
Walaa H. El-Ashmawi ◽  
Ahmed F. Ali ◽  
Adam Slowik
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Single Point ◽  
Real Life ◽  
Team Formation ◽  
Jaya Algorithm ◽  
African Buffalo ◽  
Team Members ◽  
Formation Problem ◽  
Swap Operator

Abstract Forming a team of experts that can match the requirements of a collaborative task is an important aspect, especially in project development. In this paper, we propose an improved Jaya optimization algorithm for minimizing the communication cost among team experts to solve team formation problem. The proposed algorithm is called an improved Jaya algorithm with a modified swap operator (IJMSO). We invoke a single-point crossover in the Jaya algorithm to accelerate the search, and we apply a new swap operator within Jaya algorithm to verify the consistency of the capabilities and the required skills to carry out the task. We investigate the IJMSO algorithm by implementing it on two real-life datasets (i.e., digital bibliographic library project and StackExchange) to evaluate the accuracy and efficiency of proposed algorithm against other meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, African buffalo optimization algorithm and standard Jaya algorithm. Experimental results suggest that the proposed algorithm achieves significant improvement in finding effective teams with minimum communication costs among team members for achieving the goal.

Manufacturing cell formation with flexible processing capabilities and worker assignment: Comparison of constraint programming and integer programming approaches

2017 ◽  
Vol 232 (11) ◽  
pp. 2054-2068 ◽  
Author(s):  
Adil Baykasoğlu ◽  
Şeyda Topaloğlu ◽  
Filiz Şenyüzlüler
Keyword(s):  
Integer Programming ◽  
Constraint Programming ◽  
Manufacturing Systems ◽  
High Efficiency ◽  
Programming Model ◽  
Cell Formation ◽  
Programming Approach ◽  
Cell Formation Problem ◽  
Formation Problem ◽  
Complex Constraints

Cell formation deals with grouping of machines and parts in manufacturing systems according to their compatibility. Manufacturing processes are surrounded with an abundance of complex constraints which should be considered carefully and represented clearly for obtaining high efficiency and productivity. Constraint programming is a new approach to combinatorial optimization and provides a rich language to represent complex constraints easily. However, the cell formation problems are well suited to be solved by constraint programming approach since the problem has many constraints such as part-machine requirements, availabilities in the system in terms of capacity, machine and worker abilities. In this study, the cell formation problem is modeled using machine, part processing and worker flexibilities via resource element–based representation. Resource elements define the processing requirements of parts and processing capabilities of machines and workers, which are resource-independent capability units. A total of 12 case problems are generated, and different search phases of constraint programming are defined for the solution procedure. The cell formation problem is modeled in both constraint programming and integer programming, and a comparative analysis of constraint programming and integer programming model solutions is done. The results indicate that both the models are effective and efficient in the solution of the cell formation problem.

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