Solving Call Center Agent Scheduling Problem through Improved Adaptive Genetic Algorithm

2012 ◽  
Author(s):  
Yue Ma ◽  
Lieli Liu
Keyword(s):  
Genetic Algorithm ◽  
Call Center ◽  
Adaptive Genetic Algorithm ◽  
Scheduling Problem ◽  
Agent Scheduling ◽  
Call Center Agent

A three-agent scheduling problem for minimizing the flow time on two machines

2020 ◽  
Vol 54 (2) ◽  
pp. 307-323
Author(s):  
Wen-Chiung Lee ◽  
Jen-Ya Wang
Keyword(s):  
Genetic Algorithm ◽  
Approximate Solutions ◽  
Scheduling Problem ◽  
Problem Size ◽  
Solution Quality ◽  
Agent Scheduling ◽  
Maximum Completion Time ◽  
Np Hardness ◽  
Two Machines

This study introduces a two-machine three-agent scheduling problem. We aim to minimize the total tardiness of jobs from agent 1 subject to that the maximum completion time of jobs from agent 2 cannot exceed a given limit and that two maintenance activities from agent 3 must be conducted within two maintenance windows. Due to the NP-hardness of this problem, a genetic algorithm (named GA+) is proposed to obtain approximate solutions. On the other hand, a branch-and-bound algorithm (named B&B) is developed to generate the optimal solutions. When the problem size is small, we use B&B to verify the solution quality of GA+. When the number of jobs is large, a relative deviation is proposed to show the gap between GA+ and another ordinary genetic algorithm. Experimental results show that the proposed genetic algorithm can generate approximate solutions by consuming reasonable execution time.

A Hybrid 2-Population Genetic Algorithm for the Job Shop Scheduling Problem

2014 ◽  
Vol 989-994 ◽  
pp. 2609-2612
Author(s):  
Zhuo Xu ◽  
Rui Wang ◽  
Zhong Min Wang
Keyword(s):  
Genetic Algorithm ◽  
Population Genetic ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Population Genetic Algorithm

In this paper, an analysis of a hybrid two-population genetic algorithm (H2PGA) for the job shop scheduling problem is presented. H2PGA is composed of two populations that constitute of similar fit chromosomes. These two branches implement genetic operation separately using different evolutionary strategy and exchange excellent chromosomes using migration strategy which is acquired by experiments. Improved adaptive genetic algorithm (IAGA) and simulated annealing genetic algorithm (SAGA) are applied in two branches respectively. By integrating the advantages of two techniques, this algorithm has comparatively solved the two major problems with genetic algorithm which are low convergence velocity and potentially to be plunged into local optimum. Experimental results show that the H2PGA outperforms the other three methods for it has higher convergence velocity and higher efficiency.

A New Adaptive Genetic Algorithm for Job-Shop Scheduling

2009 ◽  
Vol 626-627 ◽  
pp. 771-776
Author(s):  
Lei Wang ◽  
Dun Bing Tang ◽  
W.D. Yuan ◽  
M.J. Xu ◽  
M. Wan
Keyword(s):  
Genetic Algorithm ◽  
Production Efficiency ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Cycle ◽  
Adaptive Genetic Algorithm ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Slow Evolution

In order to minimize makespan for job-shop scheduling problem (JSP), an improved adaptive genetic algorithm (IAGA) based on hormone modulation mechanism is proposed. This algorithm has characteristics with avoiding overcoming premature phenomenon and slow evolution. The proposed IAGA algorithm is applied to dynamic job-shop scheduling problem (DJSP) and the satisfied result is obtained. By employing the proposed IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently. Therefore it embodies good adaptation to the DJSP (rush order, machine malfunction, and so on).

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