An Interactive Simulated Annealing Multi-agents Platform to Solve Hierarchical Scheduling Problems with Goals

2009 ◽  
pp. 177-187
Author(s):  
Souhail Dhouib ◽  
Sana Kouraïchi ◽  
Taïcir loukil ◽  
Habib Chabchoub
Keyword(s):  
Simulated Annealing ◽  
Scheduling Problems ◽  
Hierarchical Scheduling ◽  
Multi Agents

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

scholarly journals Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

2018 ◽  
Vol 8 (12) ◽  
pp. 2621 ◽  
Author(s):  
Hongjing Wei ◽  
Shaobo Li ◽  
Houmin Jiang ◽  
Jie Hu ◽  
Jianjun Hu
Keyword(s):  
Simulated Annealing ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Regulation Mechanism ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Wide Range ◽  
Random Crossover ◽  
Crossover And Mutation ◽  
Makespan Criterion

Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm’s quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.

scholarly journals Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Ibrahim Attiya ◽  
Mohamed Abd Elaziz ◽  
Shengwu Xiong
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Scheduling Algorithms ◽  
Scheduling Problems ◽  
It Services ◽  
On Demand

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

scholarly journals Comparison of a Fuzzy Genetic and Simulated Annealing Algorithm Approach for Project Time-Cost Tradeoff

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Nataša Glišović
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Project Planning ◽  
Time Cost ◽  
Scheduling Problems ◽  
Project Networks ◽  
Resource Leveling ◽  
Time Period ◽  
The Cost ◽  
Fuzzy Genetic

Project planning, defining the limitations and resources by leveling the resources available, have a great importance for the management projects. All these activities directly affect the duration and the cost of the project. To get a competitive value on the market, the project must be completed at the optimum time. In other to be competitive enough the optimum or near optimum solutions of time cost tradeoff and the resource leveling and resource constrained scheduling problems should be obtained in the planning phase of the project. One important aspect of the project management is activity crashing, that is, reducing activity time by adding more resources such as workers and overtime. It is important to decide the optimal crash plan to complete the project within the desired time period. The comparison of fuzzy simulated annealing and the genetic algorithm based on the crashing method is introduced in this paper to evaluate project networks and determine the optimum crashing configuration that minimizes the average project cost, caused by being late and crashing costs in the presence of vagueness and uncertainty. The evaluation results based on a real case study indicate that the method can be reliably applied to engineering projects.

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