scholarly journals Diversity Controlling Genetic Algorithm for Order Acceptance and Scheduling Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Cheng Chen ◽  
Zhenyu Yang ◽  
Yuejin Tan ◽  
Renjie He
Keyword(s):  
Genetic Algorithm ◽  
Production Systems ◽  
Hamming Distance ◽  
Scheduling Problem ◽  
Order Acceptance ◽  
Solution Quality ◽  
Survival Selection ◽  
Benchmark Instances ◽  
Order Acceptance And Scheduling ◽  
Dependent Setup Times

Selection and scheduling are an important topic in production systems. To tackle the order acceptance and scheduling problem on a single machine with release dates, tardiness penalty, and sequence-dependent setup times, in this paper a diversity controlling genetic algorithm (DCGA) is proposed, in which a diversified population is maintained during the whole search process through survival selection considering both the fitness and the diversity of individuals. To measure the similarity between individuals, a modified Hamming distance without considering the unaccepted orders in the chromosome is adopted. The proposed DCGA was validated on 1500 benchmark instances with up to 100 orders. Compared with the state-of-the-art algorithms, the experimental results show that DCGA improves the solution quality obtained significantly, in terms of the deviation from upper bound.

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.

scholarly journals Extended Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problem

2021 ◽  
Vol 10 (02) ◽  
pp. 017-020
Author(s):  
Sumaia E. Eshim ◽  
Mohammed M. Hamed
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Solution Quality ◽  
Shop Scheduling

In this paper, a hybrid genetic algorithm (HGA) to solve the job shop scheduling problem (JSSP) to minimize the makespan is presented. In the HGA, heuristic rules are integrated with genetic algorithm (GA) to improve the solution quality. The purpose of this research is to investigate from the convergence of a hybrid algorithm in achieving a good solution for new benchmark problems with different sizes. The results are compared with other approaches. Computational results show that a hybrid algorithm is capable to achieve good solution for different size problems.

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