Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Project

Lu, Fuqiang; Bi, Hualing; Huang, Min; Duan, Shupeng

doi:10.1155/2017/6916575

Cited by 21 publications

(15 citation statements)

References 42 publications

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“…GAs are widely applied to a wide range of problems. Instances of their use are observed in various fields, including robotics [33], risk management [34], and tunnel lighting [35], among others.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…The obtained solution is then compared with the one obtained in the previous section. The turbine sets assigned to substations 0 1 and 0 2 are, respectively, N 1 = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13} and N 2 = {14, 15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48, 49, 50}. The obtained objective value corresponds to the cost EUR 4,800,839.0.…”

Section: Alto Da Coutada Wind Farmmentioning

confidence: 99%

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Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming

Pires,

Cerveira,

Baptista

2023

Computation

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This work addresses the wind farm (WF) optimization layout considering several substations. It is given a set of wind turbines jointly with a set of substations, and the goal is to obtain the optimal design to minimize the infrastructure cost and the cost of electrical energy losses during the wind farm lifetime. The turbine set is partitioned into subsets to assign to each substation. The cable type and the connections to collect wind turbine-produced energy, forwarding to the corresponding substation, are selected in each subset. The technique proposed uses a genetic algorithm (GA) and an integer linear programming (ILP) model simultaneously. The GA creates a partition in the turbine set and assigns each of the obtained subsets to a substation to optimize a fitness function that corresponds to the minimum total cost of the WF layout. The fitness function evaluation requires solving an ILP model for each substation to determine the optimal cable connection layout. This methodology is applied to four onshore WFs. The obtained results show that the solution performance of the proposed approach reaches up to 0.17% of economic savings when compared to the clustering with ILP approach (an exact approach).

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Section: Genetic Algorithmsmentioning

confidence: 99%

Section: Alto Da Coutada Wind Farmmentioning

confidence: 99%

Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming

Pires,

Cerveira,

Baptista

2023

Computation

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“…A significantly improved performance of the algorithm was reported compared to other competing algorithms. Fuqiang et al [42] developed a technique including SA and GA for scheduled risk management of IT outsourcing projects. They concluded that SA, in combination with GA, is the superior algorithm in terms of stability and convergence.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybridization of Particle Swarm Optimization with Variable Neighborhood Search and Simulated Annealing for Improved Handling of the Permutation Flow-Shop Scheduling Problem

Hayat

Tariq

Shahzad

et al. 2023

Systems

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Permutation flow-shop scheduling is the strategy that ensures the processing of jobs on each subsequent machine in the exact same order while optimizing an objective, which generally is the minimization of makespan. Because of its NP-Complete nature, a substantial portion of the literature has mainly focused on computational efficiency and the development of different AI-based hybrid techniques. Particle Swarm Optimization (PSO) has also been frequently used for this purpose in the recent past. Following the trend and to further explore the optimizing capabilities of PSO, first, a standard PSO was developed during this research, then the same PSO was hybridized with Variable Neighborhood Search (PSO-VNS) and later on with Simulated Annealing (PSO-VNS-SA) to handle Permutation Flow-Shop Scheduling Problems (PFSP). The effect of hybridization was validated through an internal comparison based on the results of 120 different instances devised by Taillard with variable problem sizes. Moreover, further comparison with other reported hybrid metaheuristics has proved that the hybrid PSO (HPSO) developed during this research performed exceedingly well. A smaller value of 0.48 of ARPD (Average Relative Performance Difference) for the algorithm is evidence of its robust nature and significantly improved performance in optimizing the makespan as compared to other algorithms.

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“…A GA is one of the most famous meta-heuristic algorithms to solve diverse scheduling problems such as project scheduling [20,21], job-shop scheduling [22], parallel machine scheduling [23], and flow-shop scheduling [24,25] and usually performs well. In addition, a multiobjective genetic algorithm (MOGA) has also been successfully applied to various multiobjective problems, such as a scheduling problem to minimize both production cost and time.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

Ahn

Hur

2021

Mathematical Problems in Engineering

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In cloud manufacturing, customers register customized requirements, and manufacturers provide appropriate services to complete the task. A cloud manufacturing manager establishes manufacturing schedules that determine the service provision time in a real-time manner as the requirements are registered in real time. In addition, customer satisfaction is affected by various measures such as cost, quality, tardiness, and reliability. Thus, multiobjective and real-time scheduling of tasks is important to operate cloud manufacturing effectively. In this paper, we establish a mathematical model to minimize tardiness, cost, quality, and reliability. Additionally, we propose an approach to solve the mathematical model in a real-time manner using a multiobjective genetic algorithm that includes chromosome representation, fitness function, and genetic operators. From the experimental results, we verify whether the proposed approach is effective and efficient.

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Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Project

Cited by 21 publications

References 42 publications

Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming

Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming

Hybridization of Particle Swarm Optimization with Variable Neighborhood Search and Simulated Annealing for Improved Handling of the Permutation Flow-Shop Scheduling Problem

Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

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