Solution of constrained problems using particle swarm optimiziation

Kumari, Sudesh; Khurana, Pooja; Singla, Shakuntla; Kumar, Arun

doi:10.1007/s13198-021-01524-x

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…Kumari et al. [20] discussed the solution of constrained problems using particle swarm optimization (PSO). Li and Zhang [21] used dynamic programming to solve the redundancy allocation optimization for the multi-state series and parallel systems.…”

Section: Materials Methodsmentioning

confidence: 99%

Performability evaluation, validation and optimization for the steam generation system of a coal-fired thermal power plant

et al. 2022

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Section: Materials Methodsmentioning

confidence: 99%

Performability evaluation, validation and optimization for the steam generation system of a coal-fired thermal power plant

et al. 2022

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“…The literature extensively scrutinizes and documents the statistical optimization strategies under consideration across various scholarly publications. Of particular note are the substantive contributions made by scholars such as Kumari et al (19) and Malik et al (20) to the prevailing body of knowledge in this specialized domain.…”

Section: Introductionmentioning

confidence: 99%

No idle flow shop scheduling models for optimization of machine rental costs with processing and separated setup times

Singla,

Kaur,

Gupta

et al. 2024

Front. Appl. Math. Stat.

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Scheduling is one of the many skills required for advancement in today’s modern industry. The flow-shop scheduling problem is a well-known combinatorial optimization challenge. Scheduling issues for flow shops are NP-hard and challenging. The present research investigates a two-stage flow shop scheduling problem with decoupled processing and setup times, where a correlation exists between probabilities, job processing times, and setup times. This study proposes a novel heuristic algorithm that optimally sequences jobs to minimize the makespan and eliminates machine idle time, thereby reducing machine rental costs. The proposed algorithm’s efficacy is demonstrated through several computational examples implemented in MATLAB 2021a. The results are compared with the existing approaches such as those by Johnson, Palmer, NEH, and Nailwal to highlight the proposed algorithm’s superior performance.

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“…In a paper mill washing unit, investigated mathematical formulation and behavior study. The particle Swarm Optimization algorithm was used by Kumari et al (2021) to research limited situations. Using a heuristic approach, Rajbala et al (2022) investigated the redundancy allocation problem in the cylinder manufacturing plant.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Edible Oil Refinery Industry using Machine Learning

Kaur,

Singh,

Garg

2023

IJERR

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This research paper discusses the use of machine learning for sensitivity analysis in the edible oil refinery industry. The edible oil refinery consists of four units, and subsystem A (Cleaning) contains parallel subcomponents that enable it to function in reduced capacity when some of its components fail. As a result, the system operates in reduced capacity and fails entirely when unit A fails. There is single repairman who is available for 24x7. The failure and repair time distributions are distinct and constant for each unit. Subunits B (Shelling), D (Crushing/Ribbing), and E (Expeller Pressing) are connected in series, so, if any of them malfunctions, the entire system fail. System parameters' behavior for various repair and failure rates is discussed graphically and in tabular form. Comparison of the Performance of system parameters is carried out using linear Classifier and Logistic Regression and graphs are drawn. From the comparative analysis of models, it is concluded that the linear classifier is better than logistic regression for MTSF, Busy Period of server and Availability.

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Solution of constrained problems using particle swarm optimiziation

Cited by 11 publications

References 10 publications

Performability evaluation, validation and optimization for the steam generation system of a coal-fired thermal power plant

Performability evaluation, validation and optimization for the steam generation system of a coal-fired thermal power plant

No idle flow shop scheduling models for optimization of machine rental costs with processing and separated setup times

Sensitivity Analysis of Edible Oil Refinery Industry using Machine Learning

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