Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many r...Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.展开更多
Teamwork is gaining increasing attention in a broader management research. In addition to previous research on the relationship between team structure and innovation performance, this study draws from an interesting e...Teamwork is gaining increasing attention in a broader management research. In addition to previous research on the relationship between team structure and innovation performance, this study draws from an interesting experience-based theory advanced by Kelley and Littman （2005）, which examines teams from design thinking perspective, and tests its contributions and effects on team＇s innovation performance. According to Kelley and Littman any team should include the following team roles： The anthropologist, the experimenter, the cross-pollinator, the hurdler, the collaborator, the director, the experience architect, the set designer, the storyteller, and the caregiver. We develop theoretical logics to explain how team structure that includes these key team roles and competences lead to a better innovation performance, and propose pertinent hypotheses. Experimental-empirical research and quantitative analysis were used in the study. The study conducted multiple experiments on three samples： a group of foreign entrepreneurship students, a group of technical students, and an additional group of randomly selected individuals, aged between 20 and 58, with diverse backgrounds. A special approach was implemented and a new instrument was developed to evaluate individuals in teams. While the results show that team that possess the major competences proposed by Kelley and Littman are more innovative, preliminary results also show that not all team roles are equally important. Moreover, team roles should be allocated equally among members for better collaboration, member satisfaction, and quick response, and within one team, one prevailing personality is optimal in terms of innovativeness. We discuss the implications of our findings for future research and managerial practice.展开更多
The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned with the task assignment prob...The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned with the task assignment problem related to the online labor market, termed as CLUSTERHIRE. We improve the definition of the CLUSTERHIRE problem, and propose an efficient and effective algorithm, entitled INFLUENCE. In addition, we place a participation constraint on CLUSTERHIRE. It constrains the load of each expert in order to keep all members from overworking. For the participation-constrained CLUSTERHIRE problem, we devise two algorithms, named PROJECTFIRST and ERA. The former generates a participation- constrained team by adding experts to an initial team, and the latter generates a participation-constrained team by removing the experts with the minimum influence from the universe of experts. The experimental evaluations indicate that 1） INFLU- ENCE performs better than the state-of-the-art algorithms in terms of effectiveness and time efficiency; 2） PROJECTFIRST performs better than ERA in terms of time efficiency, yet ERA performs better than PROJECTFIRST in terms of effectiveness.展开更多
如何利用社会网络信息来寻找一个合作高效、高质量的团队,已成为热门的研究话题.但现有团队生成问题中对个体拥有技能的度量大多都采用0-1方式,而在实际应用中如何界定个体是否拥有该技能的方法会在很大程度上影响团队完成任务的效率....如何利用社会网络信息来寻找一个合作高效、高质量的团队,已成为热门的研究话题.但现有团队生成问题中对个体拥有技能的度量大多都采用0-1方式,而在实际应用中如何界定个体是否拥有该技能的方法会在很大程度上影响团队完成任务的效率.另外在目前的基于社会网络的团队生成方法研究中,计算个体间关系强度时只考虑个体间曾经合作任务的数目,并没有深入挖掘社会网络条件下个体间的社会关系类别以及个体自身的其他属性,这些因素很大程度上也会影响个体间的社会关系,进而影响个体间的团队合作.针对以上问题,该文首先给出团队生成问题的具体定义和相关概念,给出技能贡献度的定义,并利用社会网络个体间的关系类别和个体间对应社会属性相似度引入一种关系模型来进一步量化团队成员个体间的关系强度;然后根据团队的不同形式分别进行了无领导者团队生成方法的研究和有领导者团队生成方法的研究,并分别提出了MCSTFA算法(Minimum Covering Steiner-based Team Forming Algorithm)和MSCTFA算法(Minimum Set Covering-based Team Forming Algorithm)来寻找最佳无领导者团队以及提出MLDTFA算法(Minimum Leader Distance based Team Forming Algorithm)来寻找最佳领导者和最佳团队.最后,利用DBLP数据集设计和实现实验以验证上述所有方法的可行性和有效性,并从团队合作代价、团队成员数量、团队连通性以及社会网络影响因素对算法的影响对比结果等方面进行比较和分析,实验结果验证了文中所提算法的可行性和高效性.展开更多
文摘Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.
文摘Teamwork is gaining increasing attention in a broader management research. In addition to previous research on the relationship between team structure and innovation performance, this study draws from an interesting experience-based theory advanced by Kelley and Littman （2005）, which examines teams from design thinking perspective, and tests its contributions and effects on team＇s innovation performance. According to Kelley and Littman any team should include the following team roles： The anthropologist, the experimenter, the cross-pollinator, the hurdler, the collaborator, the director, the experience architect, the set designer, the storyteller, and the caregiver. We develop theoretical logics to explain how team structure that includes these key team roles and competences lead to a better innovation performance, and propose pertinent hypotheses. Experimental-empirical research and quantitative analysis were used in the study. The study conducted multiple experiments on three samples： a group of foreign entrepreneurship students, a group of technical students, and an additional group of randomly selected individuals, aged between 20 and 58, with diverse backgrounds. A special approach was implemented and a new instrument was developed to evaluate individuals in teams. While the results show that team that possess the major competences proposed by Kelley and Littman are more innovative, preliminary results also show that not all team roles are equally important. Moreover, team roles should be allocated equally among members for better collaboration, member satisfaction, and quick response, and within one team, one prevailing personality is optimal in terms of innovativeness. We discuss the implications of our findings for future research and managerial practice.
基金The work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61472299, 61540008, 61672417 and 61602354, the Fundamental Research Funds for the Central Universities of China under Grant No. BDY10, the Shaanxi Postdoctoral Science Foundation, and the Natural Science Basic Research Plan of Shaanxi Province of China under Grant No. 2014JQ8359.
文摘The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned with the task assignment problem related to the online labor market, termed as CLUSTERHIRE. We improve the definition of the CLUSTERHIRE problem, and propose an efficient and effective algorithm, entitled INFLUENCE. In addition, we place a participation constraint on CLUSTERHIRE. It constrains the load of each expert in order to keep all members from overworking. For the participation-constrained CLUSTERHIRE problem, we devise two algorithms, named PROJECTFIRST and ERA. The former generates a participation- constrained team by adding experts to an initial team, and the latter generates a participation-constrained team by removing the experts with the minimum influence from the universe of experts. The experimental evaluations indicate that 1） INFLU- ENCE performs better than the state-of-the-art algorithms in terms of effectiveness and time efficiency; 2） PROJECTFIRST performs better than ERA in terms of time efficiency, yet ERA performs better than PROJECTFIRST in terms of effectiveness.
文摘如何利用社会网络信息来寻找一个合作高效、高质量的团队,已成为热门的研究话题.但现有团队生成问题中对个体拥有技能的度量大多都采用0-1方式,而在实际应用中如何界定个体是否拥有该技能的方法会在很大程度上影响团队完成任务的效率.另外在目前的基于社会网络的团队生成方法研究中,计算个体间关系强度时只考虑个体间曾经合作任务的数目,并没有深入挖掘社会网络条件下个体间的社会关系类别以及个体自身的其他属性,这些因素很大程度上也会影响个体间的社会关系,进而影响个体间的团队合作.针对以上问题,该文首先给出团队生成问题的具体定义和相关概念,给出技能贡献度的定义,并利用社会网络个体间的关系类别和个体间对应社会属性相似度引入一种关系模型来进一步量化团队成员个体间的关系强度;然后根据团队的不同形式分别进行了无领导者团队生成方法的研究和有领导者团队生成方法的研究,并分别提出了MCSTFA算法(Minimum Covering Steiner-based Team Forming Algorithm)和MSCTFA算法(Minimum Set Covering-based Team Forming Algorithm)来寻找最佳无领导者团队以及提出MLDTFA算法(Minimum Leader Distance based Team Forming Algorithm)来寻找最佳领导者和最佳团队.最后,利用DBLP数据集设计和实现实验以验证上述所有方法的可行性和有效性,并从团队合作代价、团队成员数量、团队连通性以及社会网络影响因素对算法的影响对比结果等方面进行比较和分析,实验结果验证了文中所提算法的可行性和高效性.
