43 research outputs found
An investigation of production and transportation policies for multi-item and multi-stage production systems
Die vorliegende kumulative Dissertation besteht aus fรผnf Artikeln, einem Arbeitspapier und vier Artikeln, die in wissenschaftlichen Zeitschriften verรถffentlicht wurden. Alle fรผnf Artikel beschรคftigen sich mit der Losgrรถรenplanung, jedoch mit unterschiedlichen Schwerpunkten. Artikel 1 bis 4 untersuchen das Economic Lot Scheduling Problem (ELSP), wรคhrend sich der fรผnfte Artikel mit einer Variante des Joint Economic Lot Size (JELS) Problems beschรคftigt. Die Struktur dieser Dissertation trรคgt diesen beiden Forschungsrichtungen Rechnung und ordnet die ersten vier Artikel dem Teil A und den fรผnften Artikel dem Teil B zu
Setup Change Scheduling Under Due-date Constraints Using Deep Reinforcement Learning with Self-supervision
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ฐ์
ยท์กฐ์ ๊ณตํ๋ถ, 2021.8. ๋ฐ์ข
ํ.๋ฉ๊ธฐ ์ ์ฝ ํ์์ ์
์
์ค์ผ์ค์ ์๋ฆฝํ๋ ๊ฒ์ ํ์ค์ ์ฌ๋ฌ ์ ์กฐ ์ฐ์
์์ ์ฝ๊ฒ ์ฐพ์ ๋ณผ ์ ์์ผ๋ฉฐ ํ๊ณ์ ๋ง์ ๊ด์ฌ์ ๋๊ณ ์๋ ์ค๋ํ ๋ฌธ์ ์ด๋ค. ๊ทธ๋ฌ๋ ๋ฉ๊ธฐ์ ์
์
์ ์ฝ์ด ๋์์ ์กด์ฌํจ์ ๋ฐ๋ผ ๋ฌธ์ ์ ๋ณต์ก๋๊ฐ ์ฆ๊ฐํ๊ฒ ๋๋ฉฐ, ์์๊ฐ๊ฐ ์๋ก์ด ์์ฐ ๊ณํ์ด ์ฃผ์ด์ง๊ณ ์ด๊ธฐ ์ค๋น ์ํ๊ฐ ๋ณํ๋๋ ํ๊ฒฝ์์ ๊ณ ํ์ง์ ์ค์ผ์ค ์๋ฆฝ์ ๋ ์ด๋ ค์์ง๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ํ์ต๋ ์ฌ์ธต์ ๊ฒฝ๋ง์ด ์๊ธฐํ ๋ณํ๊ฐ ๋ฐ์ํ ์ค์ผ์ค๋ง ๋ฌธ์ ๋ ์ฌํ์ต ์์ด ํด๊ฒฐํ ์ ์๋๋ก, ์๊ธฐ์ง๋ ๊ธฐ๋ฐ ์ฌ์ธต๊ฐํํ์ต ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก, ์ํ์ ํ๋ ํํ์ ์์ฐ ๊ณํ๊ณผ ์ค๋น ์ํ์ ๋ฌด๊ดํ ์ฐจ์์ ๊ฐ๋๋ก ์ค๊ณํ๋ค. ๋์์ ์ฃผ์ด์ง ์ํ๋ก๋ถํฐ ํจ์จ์ ์ผ๋ก ์ ๊ฒฝ๋ง์ ํ์ตํ๊ธฐ ์ํด ํ๋ผ๋ฏธํฐ ๊ณต์ ๊ตฌ์กฐ๋ฅผ ๋์
ํ๋ค. ์ด์ ๋ํ์ฌ, ์ค์ผ์ค๋ง ๋ฌธ์ ์ ์ ํฉํ ์๊ธฐ์ง๋๋ฅผ ๊ณ ์ํ์ฌ ์ค๋น์ ์ก์ ์, ์์ฐ ๊ณํ์ ๋ถํฌ๊ฐ ์์ดํ ํ๊ฐ ํ๊ฒฝ์ผ๋ก๋ ์ผ๋ฐํ ๊ฐ๋ฅํ ์ฌ์ธต์ ๊ฒฝ๋ง์ ํ์ตํ๋ค. ์ ์ ๊ธฐ๋ฒ์ ์ ํจ์ฑ์ ๊ฒ์ฆํ๊ธฐ ์ํด ํ์ค์ ๋ณ๋ ฌ์ค๋น ๋ฐ ์ก์ต ๊ณต์ ์ ๋ชจ์ฌํ ๋๊ท๋ชจ ๋ฐ์ดํฐ์
์์ ์ง์ฝ์ ์ธ ์คํ์ ์ํํ์๋ค. ์ ์ ๊ธฐ๋ฒ์ ๋ฉํํด๋ฆฌ์คํฑ ๊ธฐ๋ฒ๊ณผ ๋ค๋ฅธ ๊ฐํํ์ต ๊ธฐ๋ฐ ๊ธฐ๋ฒ, ๊ท์น ๊ธฐ๋ฐ ๊ธฐ๋ฒ๊ณผ ๋น๊ตํจ์ผ๋ก์จ ๋ฉ๊ธฐ ์ค์ ์ฑ๋ฅ๊ณผ ์ฐ์ฐ ์๊ฐ ๊ด์ ์์ ์ฐ์์ฑ์ ์
์ฆํ์๋ค.
๋๋ถ์ด ์ํ ํํ, ํ๋ผ๋ฏธํฐ ๊ณต์ , ์๊ธฐ์ง๋ ๊ฐ๊ฐ์ผ๋ก ์ธํ ํจ๊ณผ๋ฅผ ์กฐ์ฌํ ๊ฒฐ๊ณผ, ๊ฐ๋ณ์ ์ผ๋ก ์ฑ๋ฅ ๊ฐ์ ์ ๊ธฐ์ฌํจ์ ๋ฐํ๋๋ค.Setup change scheduling under due-date constraints has attracted much attention from academia and industry due to its practical applications. In a real-world manufacturing system, however, solving the scheduling problem becomes challenging since it is required to address urgent and frequent changes in demand and due-dates of products, and initial machine status. In this thesis, we propose a scheduling framework based on deep reinforcement learning (RL) with self-supervision in which trained neural networks (NNs) are able to solve unseen scheduling problems without re-training even when such changes occur. Specifically, we propose state and action representations whose dimensions are independent of production requirements and due-dates of jobs while accommodating family setups. At the same time, an NN architecture with parameter sharing was utilized to improve the training efficiency. Finally, we devise an additional self-supervised loss specific to the scheduling problem for training the NN scheduler robust to the variations in the numbers of machines and jobs, and distribution of production plans.
We carried out extensive experiments in large-scale datasets that simulate the real-world wafer preparation facility and semiconductor packaging line. Experiment results demonstrate that the proposed method outperforms the recent metaheuristics, rule-based, and other RL-based methods in terms of the schedule quality and computation time for obtaining a schedule. Besides, we investigated individual contributions of the state representation, parameter sharing, and self-supervision on the performance improvements.์ 1 ์ฅ ์๋ก 1
1.1 ์ฐ๊ตฌ ๋๊ธฐ ๋ฐ ๋ฐฐ๊ฒฝ 1
1.2 ์ฐ๊ตฌ ๋ชฉ์ ๋ฐ ๊ณตํ 4
1.3 ๋
ผ๋ฌธ๊ตฌ์ฑ 6
์ 2 ์ฅ ๋ฐฐ๊ฒฝ 7
2.1 ์์ ์์กด์ ์
์
์ด ์๋ ๋ฉ๊ธฐ ์ ์ฝ ํ์์์ ์ค์ผ์ค๋ง ๋ฌธ์ 7
2.1.1 ๋ฉ๊ธฐ ์ ์ฝ ํ์์์ ์ค์ผ์ค๋ง ๋ฌธ์ 7
2.1.2 ํจ๋ฐ๋ฆฌ ์
์
์ ๊ณ ๋ คํ ๋ณ๋ ฌ์ค๋น ์ค์ผ์ค๋ง 8
2.1.3 ์
์
์ ์ฝ์ด ์๋ ์ก์ต ์ค์ผ์ค๋ง 9
2.2 ๊ฐํํ์ต ๊ธฐ๋ฐ ์ค์ผ์ค๋ง 12
2.2.1 ์ด๋ก ์ ๋ฐฐ๊ฒฝ 12
2.2.2 ๊ฐํํ์ต์ ์ด์ฉํ ์ ์กฐ ๋ผ์ธ ์ค์ผ์ค๋ง 13
2.2.3 ์ค์ผ์ค๋ง ๋ฌธ์ ์์์ ์ฌ์ธต๊ฐํํ์ต 15
2.3 ์๊ธฐ์ง๋ ๊ธฐ๋ฐ ์ฌ์ธต๊ฐํํ์ต 19
์ 3 ์ฅ ๋ฌธ์ ์ ์ 22
3.1 ๋ณ๋ ฌ์ค๋น ์ค์ผ์ค๋ง ๋ฌธ์ 22
3.1.1 ์ง์ฐ์๊ฐ ์ต์ํ๋ฅผ ์ํ ๋ณ๋ ฌ์ค๋น ์ค์ผ์ค๋ง ๋ฌธ์ 22
3.1.2 ํผํฉ์ ์๊ณํ ๋ชจํ 24
3.1.3 ์์ ๊ณต์ 25
3.2 ์ก์ต ์ค์ผ์ค๋ง ๋ฌธ์ 26
3.2.1 ํฌ์
๋ ์ต๋ํ๋ฅผ ์ํ ์ ์ฐ์ก์ต ์ค์ผ์ค๋ง 26
3.2.2 ์์ ๊ณต์ 27
์ 4 ์ฅ ์๊ธฐ์ง๋ ๊ธฐ๋ฐ ์ฌ์ธต๊ฐํํ์ต์ ์ด์ฉํ ๋ณ๋ ฌ์ค๋น ์ค์ผ์ค๋ง 31
4.1 MDP ๋ชจํ 31
4.1.1 ํ๋ ์ ์ 31
4.1.2 ์ํ ํํ 32
4.1.3 ๋ณด์ ์ ์ 37
4.1.4 ์ํ ์ ์ด 38
4.1.5 ์์ 39
4.2 ์ ๊ฒฝ๋ง ํ์ต 41
4.2.1 ์ฌ์ธต์ ๊ฒฝ๋ง ๊ตฌ์กฐ 41
4.2.2 ์์ค ํจ์ 42
4.2.3 DQN ํ์ต ์ ์ฐจ 43
4.2.4 DQN ํ๊ฐ ์ ์ฐจ 44
4.3 ์ค์ผ์ค๋ง ๋ฌธ์ ์์์ ์๊ธฐ์ง๋ 46
4.3.1 ๋ด์ฌ์ ๋ณด์ ์ค๊ณ 46
4.3.2 ์
์
์ค์ผ์ค๋ง์ ์ํ ์ ํธ๋ ์ ์ ์ค๊ณ 47
4.4 ์๊ธฐ์ง๋ ๊ธฐ๋ฐ DQN ํ์ต 49
4.4.1 ์๊ธฐ์ง๋ ์์ค ํจ์ 49
4.4.2 ํ์ต ์ ์ฐจ 50
์ 5 ์ฅ ์๊ธฐ์ง๋ ๊ธฐ๋ฐ ์ฌ์ธต๊ฐํํ์ต์ ์ด์ฉํ ์ก์ต ์ค์ผ์ค๋ง 53
5.1 ์ค์ผ์ค๋ง ํ๋ ์์ํฌ 53
5.1.1 ๋ณ๋ชฉ ๊ณต์ ์ ์ 53
5.1.2 ๋์คํจ์น ๊ท์น 54
5.1.3 ์ด์ฐ ์ฌ๊ฑด ์๋ฎฌ๋ ์ดํฐ 55
5.1.4 ์ค์ผ์ค๋ฌ ํ์ต 56
5.2 ํฌ์
์ ์ฑ
๊ณผ ์๊ธฐ์ง๋ 58
5.3 MDP ๋ชจํ ์์ 59
5.3.1 ํ๋ ์ ์ 59
5.3.2 ์ํ ํํ 59
5.3.3 ๋ณด์ ์ ์ 61
์ 6 ์ฅ ์คํ ๋ฐ ๊ฒฐ๊ณผ 62
6.1 ๋ณ๋ ฌ์ค๋น ์ค์ผ์ค๋ง ๋ฌธ์ 62
6.1.1 ๋ฐ์ดํฐ์
62
6.1.2 ์คํ ์ธํ
64
6.1.3 ์ง์ฐ์๊ฐ ์ดํฉ ์ฑ๋ฅ ๋น๊ต 67
6.1.4 ์ํ ํํ ๋ฐฉ์์ ๋ฐ๋ฅธ ์ฑ๋ฅ ๋น๊ต 72
6.2 ์ก์ต ์ค์ผ์ค๋ง ๋ฌธ์ 74
6.2.1 ๋ฐ์ดํฐ์
74
6.2.2 ์คํ ์ธํ
75
6.2.3 ํฌ์
๋ ์ฑ๋ฅ ๋น๊ต 77
6.2.4 ํ๋ ์ ์ ๋ฐฉ์์ ๋ฐ๋ฅธ ์ฑ๋ฅ ๋น๊ต 80
6.3 ์๊ธฐ์ง๋๋ก ์ธํ ํจ๊ณผ 84
6.3.1 ๋ฐ์ดํฐ์
84
6.3.2 ์คํ ์ธํ
86
6.3.3 ํ๋ผ๋ฏธํฐ ๊ณต์ ์ฌ๋ถ์ ๋ฐ๋ฅธ ์๊ธฐ์ง๋์ ํจ๊ณผ 87
6.3.4 ํ์ต ์์ ๋ค๋ฅธ ๋ฐ์ดํฐ์
์์์ ์ฑ๋ฅ ํ๊ฐ 91
์ 7 ์ฅ ๊ฒฐ๋ก ๋ฐ ํฅํ ์ฐ๊ตฌ ๋ฐฉํฅ 96
7.1 ๊ฒฐ๋ก 96
7.2 ํฅํ ์ฐ๊ตฌ ๋ฐฉํฅ 98
์ฐธ๊ณ ๋ฌธํ 100
Abstract 118
๊ฐ์ฌ์ ๊ธ 120๋ฐ
Hybrid Meta-heuristic Algorithms for Static and Dynamic Job Scheduling in Grid Computing
The term โgrid computingโ is used to describe an infrastructure that connects geographically
distributed computers and heterogeneous platforms owned by multiple organizations
allowing their computational power, storage capabilities and other resources to be selected
and shared. Allocating jobs to computational grid resources in an efficient manner is one
of the main challenges facing any grid computing system; this allocation is called job
scheduling in grid computing. This thesis studies the application of hybrid meta-heuristics
to the job scheduling problem in grid computing, which is recognized as being one of
the most important and challenging issues in grid computing environments. Similar to
job scheduling in traditional computing systems, this allocation is known to be an NPhard
problem. Meta-heuristic approaches such as the Genetic Algorithm (GA), Variable
Neighbourhood Search (VNS) and Ant Colony Optimisation (ACO) have all proven their
effectiveness in solving different scheduling problems. However, hybridising two or more
meta-heuristics shows better performance than applying a stand-alone approach. The new
high level meta-heuristic will inherit the best features of the hybridised algorithms, increasing
the chances of skipping away from local minima, and hence enhancing the overall
performance. In this thesis, the application of VNS for the job scheduling problem in grid
computing is introduced. Four new neighbourhood structures, together with a modified
local search, are proposed. The proposed VNS is hybridised using two meta-heuristic
methods, namely GA and ACO, in loosely and strongly coupled fashions, yielding four
new sequential hybrid meta-heuristic algorithms for the problem of static and dynamic
single-objective independent batch job scheduling in grid computing. For the static version
of the problem, several experiments were carried out to analyse the performance of the
proposed schedulers in terms of minimising the makespan using well known benchmarks.
The experiments show that the proposed schedulers achieved impressive results compared
to other traditional, heuristic and meta-heuristic approaches selected from the bibliography.
To model the dynamic version of the problem, a simple simulator, which uses
the rescheduling technique, is designed and new problem instances are generated, by
using a well-known methodology, to evaluate the performance of the proposed hybrid
schedulers. The experimental results show that the use of rescheduling provides significant
improvements in terms of the makespan compared to other non-rescheduling approaches
Data-Intensive Computing in Smart Microgrids
Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area
Mejoramiento de programaciรณn de producciรณn en planta de inyecciรณn de plรกsticos usando un algoritmo genรฉtico
70 pรกginasEste proyecto de grado buscรณ solucionar el problema de secuenciar un conjunto de trabajos y moldes de inyecciรณn en mรกquinas inyectoras en una planta de inyecciรณn, con el fin de reducir el makespan y tardanza. La planta de inyecciรณn es el primer eslabรณn de la cadena de producciรณn en una fรกbrica de productos de consumo masivo. El proyecto se caracterizรณ como un problema de secuenciaciรณn de trabajos en mรกquinas no relacionadas paralelas. El proyecto aborda el problema con dos mรฉtodos, el primero usando programaciรณn lineal entera mixta (MILP) y el segundo usando un algoritmo genรฉtico. El mรฉtodo exacto funciona bien con instancias pequeรฑas de mรกximo 10 trabajos y cinco mรกquinas. Respecto al segundo mรฉtodo se diseรฑรณ un algoritmo genรฉtico con una funciรณn fitness completamente original, el algoritmo genรฉtico permite encontrar soluciones de calidad en corto tiempo para instancias mรกs grandes y complejas si se compara con el mรฉtodo exacto. El algoritmo genรฉtico requiriรณ un ajuste de sus parรกmetros usando diseรฑo factorial multinivel. Con el objetivo de probar el mรฉtodo exacto de soluciรณn y lograr una comparaciรณn estricta entre los dos mรฉtodos de soluciรณn se desarrollaron instancias de dos tipos: random y reales con informaciรณn de la planta de inyecciรณn. Luego se desarrollaron experimentos computacionales solucionando las instancias con los dos mรฉtodos. Los resultados de los experimentos permitieron establecer que el algoritmo genรฉtico propuesto genera soluciones iguales o mejores en instancias random comparado con el mรฉtodo exacto.This thesis developed a method to find solutions to a scheduling problem in an
injection mold factory, intending to reduce the makespan and tardiness. The injection
mould factory is the first stage in a massive consumer product factory location. The
project was characterized as a nonrelated parallel machine scheduling problem. The
project approached the problem with two methods: the 1st one using mixed-integer
linear programming (MILP) and the 2nd one using a genetic algorithm. The exact
solution method works fine with small instances, with a maximal size of ten jobs and
five machines. About the second method, a genetic algorithm was designed with a
completely original fitness function, the genetic algorithm was able to find several
quality solutions in a shorter time for larger and complex instances if it is compared
with the exact solution method. The genetic algorithm required several parameter
adjustments, the multilevel factorial design was used to do so. With the objective of
testing the exact method and to can achieve a strict comparison between both
solution methods, two kinds of instances were developed: 1 kind with random data
and the other one with real production data. After it, several computational
experiments were developed, solving the whole instances with both solution
methods. Experiments results allowed the researchers to conclude that the
proposed genetic algorithm creates equal or better solutions if it is compared with
the exact solution method with random instances. One study case was developed,
this case represents the production schedule for a month for injected components
labeled as type A, the study case was used to compare the current scheduling
method used in the injection factory versus the proposed genetic algorithm. The
genetic algorithm provides a scheduling program with Cmax and Tardiness values
30% lower than the current scheduling method.Maestrรญa en Gerencia de IngenierรญaMagรญster en Gerencia de Ingenierรญ
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
Improvements on the bees algorithm for continuous optimisation problems
This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithmโs performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum.
Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success.
Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems
Adaptive and Scalable Controller Placement in Software-Defined Networking
Software-defined networking (SDN) revolutionizes network control by externalizing and centralizing the control plane. A critical aspect of SDN is Controller Placement (CP), which involves identifying the ideal number and location of controllers in a network to fulfill diverse objectives such as latency constraints (node-to-controller and controller-controller delay), fault tolerance, and controller load. Existing optimization techniques like Multi-Objective Particle Swarm Optimisation (MOPSO), Adapted Non-Dominating Sorting Genetic Algorithm-III (ANSGA-III), and Non-Dominating Sorting Genetic Algorithm-II (NSGA-II) struggle with scalability (except ANSGA-III), computational complexity, and inability to predict the required number of controllers. This thesis proposes two novel approaches to address these challenges. First, an enhanced version of NSGA-III with a repair operator-based approach (referred to as ANSGA-III) is introduced, enabling efficient CP in SD-WAN by optimizing multiple conflicting objectives simultaneously. Second, a Stochastic Computational Graph Model with Ensemble Learning (SCGMEL) is developed, overcoming scalability and computational inefficiency associated with existing methods. SCGMEL employs stochastic gradient descent with momentum, a learning rate decay, a computational graph model, a weighted sum approach, and the XGBoost algorithm for optimization and machine learning. The XGBoost predicts the number of controllers needed and a supervised classification algorithm called Learning Vector Quantization (LVQ) is used to predict the optimal locations of controllers. Additionally, this research introduces the Improved Switch Migration Decision Algorithm (ISMDA) as part of the holistic contribution. ISMDA is implemented on each controller to ensure even load distribution throughout the controllers. It functions as a plug-and-play module, periodically checking if the load surpasses a certain limit. ISMDA improves controller throughput by approximately 7.4% over CAMD and roughly 1.1% over DALB. ISMDA also outperforms DALB and CAMD with a decrease of 5.7% and 1%, respectively, in terms of controller response time. Additionally, ISMDA outperforms DALB and CAMD with a decrease of 1.7% and 5.6%, respectively, in terms of the average frequency of migrations. The established framework results in fewer switch migrations during controller load imbalance. Finally, ISMDA proves more efficient than DALB and CAMD, with an estimated 1% and 6.4% lower average packet loss, respectively. This efficiency is a result of the proposed migration efficiency strategy, allowing ISMDA to handle higher loads and reject fewer packets.
Real-world experiments were conducted using the Internet Zoo topology dataset to evaluate the proposed solutions. Six objective functions, including worst-case switch-to-controller delay, load balancing, reliability, average controller-to-controller latency, maximum controller-to-controller delay, and average switch-to-controller delay, were utilized for performance evaluation. Results demonstrated that ANSGA-III outperforms existing algorithms in terms of hypervolume indicator, execution time, convergence, diversity, and scalability. SCGMEL exhibited exceptional computational efficiency, surpassing ANSGA-III, NSGA-II, and MOPSO by 99.983%, 99.985%, and 99.446% respectively. The XGBoost regression model performed significantly better in predicting the number of controllers with a mean absolute error of 1.855751 compared to 3.829268, 3.729883, and 1.883536 for KNN, linear regression, and random forest, respectively. The proposed LVQ-based classification method achieved a test accuracy of 84% and accurately predicted six of the seven controller locations.
To culminate, this study presents a refined and intelligent framework designed to optimize Controller Placement (CP) within the context of SD-WAN. The proposed solutions effectively tackle the shortcomings associated with existing algorithms, addressing challenges of scalability, intelligence (including the prediction of optimal controller numbers), and computational efficiency in the pursuit of simultaneous optimization of multiple conflicting objectives. The outcomes underscore the supremacy of the suggested methodologies and underscore their potential transformative influence on SDN deployments. Notably, the findings validate the efficacy of the proposed strategies, ANSGA-III and SCGMEL, in enhancing the optimization of controller placement within SD-WAN setups. The integration of the XGBoost regression model and LVQ-based classification technique yields precise predictions for both optimal controller quantities and their respective positions. Additionally, the ISMDA algorithm emerges as a pivotal enhancement, enhancing controller throughput, mitigating packet losses, and reducing switch migration frequencyโcollectively contributing to elevated standards in SDN deployments