An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications

Uniyal, Nitin; Pant, Sangeeta; Kumar, Anuj

doi:10.33889/ijmems.2020.5.4.058

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…Future work can also extend the number of echelons in a supply chain beyond the two-echelon problem as was the case in this study. Apart from that, novel and computationally efficient metaheuristics proposed in the literature, such as Grey Wolf Optimization (Negi et al, 2021), Flower Pollination Algorithm (Pant et al, 2017a), Cuckoos Search Algorithm, have demonstrated promising results in other research areas (Kumar et al, 2019;Pant et al, 2017b;Uniyal et al, 2020). This study can be extended by exploring one of these metaheuristics to obtain better results.…”

Section: Conclusion and Future Researchmentioning

confidence: 86%

Modeling Multi-Plant Capacitated Lot Sizing Problem with Interplant Transfer

Patil¹,

Badhotiya²,

Nepal³

et al. 2021

Int J Math, Eng, Manag Sci

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Lot sizing models involve operational and tactical decisions. These decisions may entail multi-level production processes such as assembly operations with multiple plants and limited capacities. Lot sizing problems are widely recognized as NP-hard problems therefore difficult to solve, especially the ones with multiple plants and capacity constraints. The level of complexity rises to an even higher level when there is an interplant transfer between the plants. This paper presents a Genetic Algorithm (GA) based solution methodology applied to large scale multi-plant capacitated lot sizing problem with interplant transfer (MPCLSP-IT). Although the GA has been a very effective and widely accepted meta-heuristic approach used to solve large scale complex problems, it has not been employed for MPCLSP-IT problem. This paper solves the MPCLSP-IT problem in large scale instances by using a genetic algorithm, and in doing so successfully obtains a better solution in terms of computation time when compared to the results obtained by the other methods such as Lagrangian relaxation, greedy randomized adaptive search procedure (GRASP) heuristics, and GRASP-path relinking techniques used in extant literature.

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Section: Conclusion and Future Researchmentioning

confidence: 86%

Modeling Multi-Plant Capacitated Lot Sizing Problem with Interplant Transfer

Patil¹,

Badhotiya²,

Nepal³

et al. 2021

Int J Math, Eng, Manag Sci

View full text Add to dashboard Cite

show abstract

“…The multi-criterion decision-making (MCDM) tool of grey-analytical hierarchy process (G-AHP) has been implemented in the study to prioritize the drivers of SHCSC performance measurement. The MCDM technique considers the uncertainty and vagueness of thoughts at the time of decision-making (Mirjalili et al, 2014;Thakur and Ramesh, 2017;Pant et al, 2017aPant et al, , b, 2019Kumar et al, 2019;Uniyal et al, 2020;Negi et al, 2021). The G-AHP methodology is the combination of the advantage of the grey system theory and the classical analytic hierarchy process (AHP).…”

Section: Literature On the Solution Methodologymentioning

confidence: 99%

Drivers of sustainable healthcare supply chain performance: multi-criteria decision-making approach under grey environment

Hossain

Thakur

2021

IJQRM

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PurposeThis paper aims to explore the drivers of sustainable healthcare supply chain (SHCSC) performance measurement through extensive literature review and experts' opinions. The drivers are then scrutinized and their priority vector is calculated to provide quality and cost-effective healthcare supply chain (HCSC) services.Design/methodology/approachThe drivers of the SHCSC performance measurement are validated using the grey-Delphi technique. After validating the drivers, they are prioritized using the grey-analytic hierarchy process (G-AHP), a multi-criteria decision-making tool.FindingsThe findings of the study highlight the prioritized drivers based on the preferences given by the experts. The findings of the study highlight the most prioritized drivers of healthcare (HC) by-product management system, coordinating and facilitating green suppliers in the HCSC and green packaging of pharmaceutical as well as other essential items.Practical implicationsThe HCSC managers should coordinate with all the stakeholders across the supply chain and involve them in the decision-making process to make products and services greener and become complicit in complying with the sustainable policy guidelines. The study highlights the strategic policy and managerial implications for implementing sustainability in the HCSC.Originality/valueThe validation and prioritization of the drivers of SHCSC in developing nations' contexts is the key contribution of the study. Grey-AHP enables a practical approach towards enhancing the sustainability of the HCSC and opening the doors for generalizing the study for future research works.

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“…They focused on increasing the operational time of the individual components of a system to maintain higher system reliability and improve productivity and profit by the application of nature-inspired optimization techniques such as Grey Wolf Optimization (GWO) and the Cuckoo Search Algorithm (CSA). Uniyal et al (2020) reviewed nature-inspired optimization along with a background of fundamentals, classification, and their reliability applications. They also demonstrate the difference between multi-objective optimization and singleobjective optimization.…”

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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An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications

Cited by 16 publications

References 31 publications

Modeling Multi-Plant Capacitated Lot Sizing Problem with Interplant Transfer

Modeling Multi-Plant Capacitated Lot Sizing Problem with Interplant Transfer

Drivers of sustainable healthcare supply chain performance: multi-criteria decision-making approach under grey environment

Job shop scheduling using heuristics through Python programming and excel interface

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