An optimization method for task assignment for industrial manufacturing organizations

Li, Ni; Li, Yuhong; Sun, Mengyuan; Kong, Haipeng; Gong, Guanghong

doi:10.1007/s10489-017-0940-1

Cited by 11 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This makes it a very attractive algorithm compared with other ones. In chemistry, it has been used in the optimization of force field parameters 65 , the prediction of the protein secondary structure 66 , etc. For example, in a study 56 of the global optimization of 23 benchmark functions, it was found that ABC performs better than or at least similar to the GA, DE or PSO algorithms.…”

Section: Artificial Bee Colony Algorithm In Abclustermentioning

confidence: 99%

ABCluster: the artificial bee colony algorithm for cluster global optimization

Zhang

Dolg

2015

Phys. Chem. Chem. Phys.

483

353

View full text Add to dashboard Cite

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

show abstract

Section: Artificial Bee Colony Algorithm In Abclustermentioning

confidence: 99%

ABCluster: the artificial bee colony algorithm for cluster global optimization

Zhang

Dolg

2015

Phys. Chem. Chem. Phys.

483

353

View full text Add to dashboard Cite

show abstract

“…The main characteristic of employee deployment is the assignment of available workforce to activities or organizational units at a certain time [3]. At the current stage, the assignment of workforce to tasks is managed subjectively by opinion or experience of respective managers [4]. Therefore, several human and organizational factors that impact the work performance and thus the cost efficiency of the employee deployment, are not considered comprehensively.…”

Section: Employee Deploymentmentioning

confidence: 99%

A comparison of methods for determining performance based employee deployment in production systems

Ast¹,

Wasseghi²,

Nyhuis³

2021

Prod. Eng. Res. Devel.

View full text Add to dashboard Cite

Employee deployment is a crucial process in production systems. Based on qualification and individual performance of employees, deployment decisions can lead to ambiguous outcomes. This paper first reviews the state of the art and further compares two methods based on combinatorial analysis for employee deployment. Therefore, this paper emphasizes the costs and benefits of a Brute Force and an alternative Greedy method. When considering the qualification and individual performance of each employee, both algorithms provide working solutions. In direct comparison, the outcome of the alternative Greedy algorithm is more efficient in terms of calculation time whereas the Brute Force method provides the combination with the global optimum. This means calculation time as well as quality of outcome differ. The exponential growth of employee allocation possibilities depends on the amount of employees and leads to high calculation times, when using a Brute Force method. The comparison of both methods reveal that the proposed alternative Greedy algorithm reaches nearly as high outcomes as the Brute Force does, with significantly less calculation time. Furthermore, this paper offers an insight into the impact of deployment decisions within production systems.

show abstract

“…Zhang and Su [18] used fuzzy variables to describe processing time by introducing a fuzzy triangular number, and they pointed that different teams vary in their knowledge and abilities, so the execution time is different for each team when they carry out each task. For the complex industrial manufacturing process, Li et al [19] solved the task assignment optimization problem by establishing a dynamic process model and developing an improved quantum genetic algorithm with a heuristic principle.…”

Section: Related Workmentioning

confidence: 99%

A Method for Efficient Task Assignment Based on the Satisfaction Degree of Knowledge

Wang

Liu

et al. 2020

Complexity

View full text Add to dashboard Cite

For the product R&D process, it is a challenge to effectively and reasonably assign tasks and estimate their execution time. This paper develops a method system for efficient task assignment in product R&D. The method system consists of three components: similar tasks identification, tasks’ execution time calculation, and task assignment model. The similar tasks identification component entails the retrieval of a similar task model to identify similar tasks. From the knowledge-based view, the tasks’ execution time calculation component uses the BP neural network to predict tasks’ execution time according to the previous similar tasks and the Task–Knowledge–Person (TKP) network. When constructing the BP neural network, the satisfaction degree of knowledge and the execution time are set as the input and output, respectively. Considering the uncertain factors associated with the whole R&D process, the task assignment model component serves as a robust optimization model to assign tasks. Then, an improved genetic algorithm is developed to solve the task assignment model. Finally, the results of numerical experiment are reported to validate the effectiveness of the proposed methods.

show abstract

An optimization method for task assignment for industrial manufacturing organizations

Cited by 11 publications

References 16 publications

ABCluster: the artificial bee colony algorithm for cluster global optimization

ABCluster: the artificial bee colony algorithm for cluster global optimization

A comparison of methods for determining performance based employee deployment in production systems

A Method for Efficient Task Assignment Based on the Satisfaction Degree of Knowledge

Contact Info

Product

Resources

About