Time-dependent conflicting bifuzzy set and its applications in reliability evaluation

Chaube, Shshank; Singh, Surender; Pant, Sangeeta; Kumar, Anuj

doi:10.1201/b22440-6

Cited by 12 publications

(9 citation statements)

References 1 publication

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“…Both sorts of uncertainty can arise from complex systems (Garg & Rani, 2013). Therefore, in order to make successful decisions, the quantification of uncertainty in the analysis of reliability is important (Chaube et al, 2018;Shakshi et al, 2022;Niksirat & Nasseri, 2022). To accommodate uncertainty during the assessment of a system's reliability, a fuzzytheoretic method has been applied by Knezevic & Odoom, (2001).…”

Section: Literature Backgroundmentioning

confidence: 99%

Reliability based framework for failure analysis in milk process industry

Gopal

Panchal

2023

Decis. Mak. Appl. Manag. Eng.

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The suggested hybridized framework offers a paradigm for performance optimization-reliability-based analysis of milk processing unit’s (MPU) failure behavior in the dairy industry. The proposed hybridized framework led to the development of fuzzy Jaya Based Lambda-Tau (JBLT) technique-based mathematical model for computing various performance parameters of the under-consideration unit. The availability of the system drops by 0.044% as the level of uncertainty or spread level increases from ± 15% to ± 25% and drops to 0.088% as the level of uncertainty increases from ± 25% to ± 60%. To corroborate the system’s availability downward trend, the results of JBLT approach were compared with Particle Swarm Optimization-Based Lambda-Tau (PSOBLT) and conventional Fuzzy Lambda-Tau (FLT) techniques. The analysis findings were given to the maintenance manager so they could create the best maintenance schedule for the considered plant.

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Section: Literature Backgroundmentioning

confidence: 99%

Reliability based framework for failure analysis in milk process industry

Gopal

Panchal

2023

Decis. Mak. Appl. Manag. Eng.

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“…Kahraman, (2006) proposed a modified Ant Colony algorithm to solve JSSP in an acceptable amount of time. To assess the system's reliability when the available information is uncertain, use of fuzzy reliability function will be useful (Chaube et al 2018). Pongchairerks (2019) proposed a novel two-level viz.…”

Section: Metaheuristics In Solving Jsspmentioning

confidence: 99%

Job shop scheduling using heuristics through Python programming and excel interface

Narayanan¹,

Kumar²,

Potluru³

et al. 2022

Decis. Mak. Appl. Manag. Eng.

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Job shop scheduling problem (JSSP) has remained a challenge both for the practitioners and the researchers. A JSSP consists of multiple number of machines (m) and jobs (n).As the number of jobs increases, the complexity of the problem increases exponentially and it becomes difficult to schedule manually. Many papers in the literature discuss heuristic and metaheuristic solutions to solve Job shop scheduling problems. But there is no ease of use for practitioners who rely on their experience to schedule jobs in ad hoc sessions resulting in inefficient allocation of jobs and machines. In this paper, a job shop scheduling problem under static and dynamic conditions is solved using heuristic approaches using python programming with an MS Excel user interface. For a supplier of automotive parts with a set of jobs and machines, priority dispatching rules, viz., Shortest Processing Time (SPT), Earliest Due Date (EDD), First-In First-Out (FIFO), Critical Ratio (CR) and Slack Per Remaining Operation (S/RO) are evaluated. The obtained performance metrics such as makespan, and tardiness are compared between the heuristics to select an optimal schedule by the job shop. The user inputs the jobs, machines, start and due dates through the MS Excel interface and obtains faster, practically usable results. This reduces the time taken for job scheduling and helps in making faster productivity-based decisions to maximize resource utilization and the total time to produce the product. Integrating Python at the backend and Excel at the front end will encourage many MSMEs to perform optimized scheduling using heuristics thereby reducing the throughput time.

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“…In today's industrial scenario, DM are bound to take decisions for various multiple conflicting objectives in uncertain environments for optimal RAMS required industrial products [1][2][3][4][5]. Evolutionary algorithms usually designed to find a better approximation to the complete Pareto-optimal front (POF), which then allows the DM to decide, among many available alternatives.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithm-based Approach for Multi-Objective Optimization of a Complex Reliability System

Kumar

Pant²,

Ram

et al. 2023

JGEU

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Dealing with the conflicting objectives in reliability analysis of complex engineering systems is always a challenging task. Here, we have taken two conflicting objectives namely reducing cost and increasing the reliability of a complex reliability system named life support system in space capsule (LS3C) into consideration. A novel multi-objective evolutionary algorithm named MOPSO-CD has been employed to get various Pareto Optimal Fronts (POFs) in accordance with different parameter tuning. The simulation results so obtained provide a wide range of varieties of POFs to decision maker (DM).

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Time-dependent conflicting bifuzzy set and its applications in reliability evaluation

Cited by 12 publications

References 1 publication

Reliability based framework for failure analysis in milk process industry

Reliability based framework for failure analysis in milk process industry

Job shop scheduling using heuristics through Python programming and excel interface

Evolutionary Algorithm-based Approach for Multi-Objective Optimization of a Complex Reliability System

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