Optimization and estimation in system reliability allocation problem

Modibbo, Umar Muhammad; Arshad, Mohd Anuar; Abdalghani, Omer; Ali, Irfan

doi:10.1016/j.ress.2021.107620

Cited by 39 publications

(15 citation statements)

References 59 publications

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“…, 2020; Kamal et al. , 2021; Modibbo et al ., 2019, 2021; Modibbo, 2020). Recently, bi-level, multi-level and intuitionistic fuzzy programmings have been formulated for selecting green and sustainable suppliers in a supply chain and production management (Gupta et al ., 2018, 2021; Charles et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS

Modibbo

Mai

Ahmed

et al. 2022

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PurposeSupplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.Design/methodology/approachThe concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.FindingsThis study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.Research limitations/implicationsThis research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.Practical implicationsThis work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.Originality/valueIn this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS

Modibbo

Mai

Ahmed

et al. 2022

Self Cite

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“…System availability is the probability that a system can perform its mission within a specific time frame without failure [28]. In other words, it is a probability that such a system will not fail in a given time.…”

Section: A Estimation and Best-fitting Procedures For Probability Dis...mentioning

confidence: 99%

“…here α and θ represent parameters, y(x) is an increasing function of x that is real positive valued, with y(x)=0 and y (x) is the derivatives of y(x). For more details see [28].…”

Section: A Estimation and Best-fitting Procedures For Probability Dis...mentioning

confidence: 99%

Estimation and Optimization for System Availability Under Preventive Maintenance

et al. 2022

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Engineers design systems to be reliable and work to fulfil their missions without failure for a specific period. However, the system components deteriorate with time and lead to its failures. A frequent system failure increases the management costs, hence posing a challenge to decision-makers. Therefore, for the avoidance of frequent system failures, preventive maintenance is necessary. The objective of any manufacturing firm is to maximize profit and minimize costs. The interval for preventive maintenance can be optimized if the system's availability is maximized and its cost function minimized. This study evaluates the availability and cost function for a continuous operating series-parallel system under a fixed time environment. A multiobjective model is formulated to maximize the availability and minimize the cost function of the system. The study illustrated a numerical example and solved using goal programming (GP), fuzzy goal programming (FGP), genetic algorithm (GA), and particle swarm optimization (PSO) techniques. The results are compared using a robust statistical test and, the PSO proves to be better. A simulation study was carried out further to evaluate the availability and cost function using R and MATLAB packages.

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“…The dependability characteristics of a computer network system comprised of load balancers, distributed database servers, and a centralized server arranged as a series-parallel system with three subsystems are discussed in Yusuf et al (2020). With an architecture-based self-adaptive framework, Modibbo et al (2021) proposed two concepts for estimating reliability functions based on Maximum Likelihood Estimators (MLE) and Uniformly Minimum Variance Unbiased Estimators (UMVUE) techniques. They used the proposed method to estimate some lifetime distribution parameters and employed the optimization technique to solve an engineering problem relating to system component cost optimization.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Multi-Hardware–Software System with Failure Interaction

John

Sanusi

Ibrahim

et al. 2022

JCCE

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The effect of hardware and software plays a critical role in achieving high device reliability. Failure to communicate with hardware and software can be responsible for a short life and low system operation. The failure of the corresponding hardware is one factor contributing to the program’s failure and vice versa. Hardware and software reliability research activities and failure encounters are being undertaken, and hardware and software reliability models are being proposed. The reliability study of a multi-hardware–software system with hardware–software–hardware, hardware–software–software, software–hardware–software, and software–hardware–software failure interaction receives less attention. This paper analyzes the reliability of a multi-hardware–software system whose failure is classified into hardware–software–hardware, hardware–software–software, software–hardware–software, and software–hardware–hardware. The failure and repair time of the running hardware–software and standby hardware–software is assumed to be exponentially distributed. Differential difference equations are built and resolved to derive explicit formulations for profit, mean time to failure, and steady-state availability. In addition, some significant results are presented in the graphs and tables.

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Optimization and estimation in system reliability allocation problem

Cited by 39 publications

References 59 publications

Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS

Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS

Estimation and Optimization for System Availability Under Preventive Maintenance

Reliability Analysis of Multi-Hardware–Software System with Failure Interaction

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