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.

Optimized Crossover JumpX in Genetic Algorithm for General Routing Problems

2018 ◽  
pp. 205-230
Author(s):  
Hicham El Hassani ◽  
Said Benkachcha ◽  
Jamal Benhra
Keyword(s):  
Genetic Algorithm ◽  
Optimization Methods ◽  
Search Space ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Path Representation ◽  
Representation Technique ◽  
Hard Problems ◽  
The Traveling Salesman Problem

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied Supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. We adopt the path representation technique for our chromosome which is the most direct representation and a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.

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.

scholarly journals Compact Genetic Algorithm for Performance Improvement in Hierarchical Sensor Networks Management

2006 ◽  
Vol 07 (01) ◽  
pp. 101-115 ◽  
Author(s):  
Ming-Hui Jin ◽  
D. Frank Hsu ◽  
Cheng-Yan Kao
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Scheduling Algorithm ◽  
Wireless Sensor ◽  
Experimental Result ◽  
Scheduling Problem ◽  
Solution Quality ◽  
Compact Genetic Algorithm ◽  
Hierarchical Wireless Sensor Networks

This paper introduces a novel scheduling problem called the active interval scheduling problem in hierarchical wireless sensor networks for long-term periodical monitoring applications. To improve the report sensitivity of the hierarchical wireless sensor networks, an efficient scheduling algorithm is desired. Therefore, in this paper, we propose a compact genetic algorithm (CGA) to optimize the solution quality for sensor network maintenance. The experimental result shows that the proposed CGA brings better solutions in acceptable calculation time.

A Genetic Algorithm Based Solution with Schedule Mode for RCPSP

2011 ◽  
Vol 268-270 ◽  
pp. 1802-1805 ◽  
Author(s):  
Yan Hua Ren ◽  
De Cai Kong ◽  
Wu Liang Peng
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Hard Problem ◽  
Solution Quality ◽  
Encoding Scheme ◽  
Intelligent Optimization ◽  
Intelligent Optimization Algorithms ◽  
Np Hard Problem ◽  
Project Scheduling Problem

Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known NP hard problem and more intelligent optimization algorithms are developed to solve it. In this paper, genetic algorithm(GA) is employed to deal with RCPSP. A priority value encoding scheme is designed to in the algorithm. The numerical results indicate that our methods is slightly better as far as solution quality is concerned and requires smaller solution time than the GA where an activity list encoding with schedule mode is used.

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.

Application of Genetic Algorithm in Denoising MRI Images Clouded with Rician Noise

2016 ◽  
pp. 14-38
Author(s):  
Debajyoti Misra ◽  
Ankur Ganguly ◽  
Dewaki Nandan Tibarewala
Keyword(s):  
Genetic Algorithm ◽  
Adaptive Mutation ◽  
Mean Square ◽  
Rician Noise ◽  
Signal To Noise ◽  
Solution Quality ◽  
Magnitude Image ◽  
Low Snr ◽  
The Mean

In this research Genetic Algorithm (GA) is suggested for remotion of Rician Noise. This type of disturbance primarily occurs in low signal to noise (SNR) regions. Original low signal is clouded due to presence of Rician noise and measurement gets hindered in low SNR areas. To defeat the trouble real and imaginary data in the image field are rectified, before construction of the magnitude image. The noise diminution filtering (or denoising) is attained by Genetic Algorithm. New genetic manipulator is used that blends crossover and adaptive mutation to improve the convergence rate and solution quality of GA. For validating the results, the proposed filter was tested successfully by keeping the number of generations fixed and gradually increasing the noise level. Similar trends of decrease were obtained in the mean square error values after the filtering was performed. This new proficiency efficaciously reduces the standard deviation and significantly lowers the rectified noise after the filtering was performed.

scholarly journals A NOVEL GREEDY GENETIC ALGORITHM TO SOLVE COMBINATORIAL OPTIMIZATION PROBLEM

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 117-120
Author(s):  
M. A. Basmassi ◽  
L. Benameur ◽  
J. A. Chentoufi
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Sequential Algorithm ◽  
Solution Quality ◽  
Graph Coloring Problem ◽  
Benchmark Instances ◽  
Crossover And Mutation

Abstract. In this paper, a modified genetic algorithm based on greedy sequential algorithm is presented to solve combinatorial optimization problem. The algorithm proposed here is a hybrid of heuristic and computational intelligence algorithm where greedy sequential algorithm is used as operator inside genetic algorithm like crossover and mutation. The greedy sequential function is used to correct non realizable solution after crossover and mutation which contribute to increase the rate of convergence and upgrade the population by improving the quality of chromosomes toward the chromatic number. Experiments on a set of 6 well-known DIMACS benchmark instances of graph coloring problem to test this approach show that the proposed algorithm achieves competitive results in comparison with three states of art algorithms in terms of either success rate and solution quality.

Solving a bus driver scheduling problem: A genetic algorithm approach

2013 ◽  
Author(s):  
Razamin Ramli ◽  
Haslinda Ibrahim ◽  
Tze Shung Lim
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Problem ◽  
University Campus ◽  
Campus Environment ◽  
Driver Scheduling ◽  
Transport Services ◽  
Bus Driver ◽  
Genetic Algorithm Approach ◽  
The University

Many transport companies face problems in regulating their transport services due to various challenges and issues. These problems affect the quality of the services provided especially in a university campus environment, where students heavily depend on the university transport services for their daily commuting.What are the problems faced by the management of the campus transport company? What are the issues raised by the drivers operating the on-campus buses?Hence, in assisting the management of the transport company the authors have identified the inefficiency of their bus driver scheduling system as one of the main problems, which needed to be tackled.For that reason, the authors developed an efficient bus driver scheduling model based on the Genetic Algorithm (GA) approach.The GA model is able to provide some resolutions and insight in relation to these inquiries: What are the constraints being considered in this bus driver scheduling problem?How were the drivers break times being distributed in this GA approach?How was the time taken to generate an efficient schedule?

A Multi-Objective Immune Genetic Algorithm for Project Scheduling on Multi-Skill Resources

2015 ◽  
Vol 719-720 ◽  
pp. 1268-1274 ◽  
Author(s):  
Lu Cheng ◽  
Guang Rui Liao ◽  
Zhen Yuan Liu
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Project Managers ◽  
Scheduling Problem ◽  
Immune Genetic Algorithm ◽  
Feasible Schedule ◽  
Multi Objective ◽  
Project Scheduling Problem ◽  
Quality Of Products

In this paper, we address the project scheduling problem with the aim of making the best use of people's talents while minimizing project makespan and the amount of wasted resources. The purpose of proposing this problem is to assist project managers to improve the quality of products and save cost. To solve this problem, we also proposed an immune genetic algorithm (IGA). This algorithm designs feasible schedule for projects. By designing computational experiments carried out on j60 from PSPLIB, we evaluate the performance of proposed IGA as well as compare it with traditional GA. It turns out that proposed IGA performs much better in the aspect of improving diversity and minimizing makespan, which provides more diverse and effective solutions.

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