Two tuned multi-objective meta-heuristic algorithms for solving a fuzzy multi-state redundancy allocation problem under discount strategies

Mousavi, Seyed Mohsen; Alikar, Najmeh; Niaki, Seyed Taghi Akhavan; Bahreininejad, Ardeshir

doi:10.1016/j.apm.2015.02.040

Cited by 37 publications

(14 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They showed that the algorithm performs better than the non-dominated sorting genetic algorithm II (NSGA-II). Mousavi et al 19 proposed a redundancy allocation problem under multi-state and bi-objective of series–parallel systems including some serial subsystems, based on which some non-repairable components were considered under a discount strategy. It should be mentioned that both the performance rates and the component reliability were considered fuzzy in their work.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

Karimi

Niaki

Niknamfar

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

Self Cite

View full text Add to dashboard Cite

The reliability of machinery and automated guided vehicle has been one of the most important challenges to enhance production efficiency in several manufacturing systems. Reliability improvement would result in a simultaneous reduction of both production times and transportation costs of the materials, especially in automated guided vehicles. This article aims to conduct a practical multi-objective reliability optimization model for both automated guided vehicles and the machinery involved in a job-shop manufacturing system, where different machines and the storage area through some parallel automated guided vehicles handle materials, parts, and other production needs. While similar machines in each shop are limited to failures based on either an Exponential or a Weibull distribution via a constant rate, the machines in different shops fail based on different failure rates. Meanwhile, as the model does not contain any closed-form equation to measure the machine reliability in the case of Weibull failure, a simulation approach is employed to estimate the shop reliability to be further maximized using the proposed model. Besides, the automated guided vehicles are restricted to failures according to an Exponential distribution. Furthermore, choosing the best locations of the shops is proposed among some potential places. The proposed NP-Hard problem is then solved by designing a novel non-dominated sorting cuckoo search algorithm. Furthermore, a multi-objective teaching-learning-based optimization, as well as a multi-objective invasive weed optimization are designed to validate the results obtained. Ultimately, a novel AHP-TOPSIS method is carried out to rank the algorithms in terms of six performance metrics.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

Karimi

Niaki

Niknamfar

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

Self Cite

View full text Add to dashboard Cite

show abstract

“…The suggested algorithm is as follows: 1) Estimate the probability density function for carbon emission quotient In this paper, the centroid method is applied for the defuzzification of the trapezoidal fuzzy number. The centroid of the trapezoidal fuzzy number t u is shown in Equation 23 [47].…”

Section: Trcementioning

confidence: 99%

A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study

Nasrollahi

Razmi

Ghodsi

2018

PROMET

View full text Add to dashboard Cite

Measuring carbon emissions is an essential step in taking required action to fight global warming. This research presents a computational method for measuring transport related carbon emissions in a healthcare supply network. The network configuration significantly impacts carbon emissions. First, a multi-objective mathematical programing model is developed for designing a healthcare supply network in the form of a two-graph location routing problem under demand and fuel consumption uncertainty. Objective functions are minimizing total cost and minimizing total fuel consumption. In the presented model, the demand of each customer must be completely satisfied in each time period, and backlog is not permitted. The number and capacity of vehicles are determined, and vehicles are heterogeneous. Furthermore, fuel consumption depends on traveling distance, vehicle and road conditions, and the load of a vehicle. The centroid method is applied to face demand uncertainty. Next, a multi-objective non-dominated ranked genetic algorithm (M-NRGA) is proposed to solve the model. Then, a Monte Carlo based approach is presented for measuring transport-related carbon emissions based on fuel consumption in supply network. Finally, the proposed approach is applied to the case of a healthcare supply network in the Fars province in Iran. The obtained results illustrate that the proposed approach is a practical tool in designing healthcare supply networks and measuring transport-related carbon emissions in the network.

show abstract

“…[35]2015BinaryHeteroGA, MASingleRSMTime-dependentMousavi et al. [36]2015MultiHomosCE-NRGAMultipleTaguchiConstantZaretalab et al. [37]2015MultiHomosMOSAMultiple-ConstantCurrent Research---MultiHomosNSGA-II/NRGASingleRSMConstant…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Redundancy Allocation Problem with weighted-k-out-of-n subsystems

Sharifi

Moghaddam

Shahriari

2019

Heliyon

View full text Add to dashboard Cite

Redundancy Allocation Problem (RAP) is one of the most practical problems in the reliability area. Many assumptions have been added to RAP in recent years. The aim was to better represent real-world problems with RAP. One of these assumptions is considering weighted-k-out-of-n sub-systems. This method has been used to model various systems like power and hydro transitions systems. In this paper, we present a new multi-objective RAP (MORAP) model for optimizing the reliability and cost of the weighted-k-out-of-n parallel systems. In our model, the sub-systems are considered as weighted-k-out-of-n. Also, we use the universal generating function and adapt this technique to obtain an exact formula to calculate each sub-system reliability. Since RAP belongs to NP-hard class of problems, we decided to employ the non-dominated sorting genetic algorithm and non-dominated ranked genetic algorithm. Several criteria were used to compare the result of these two algorithms.

show abstract

Two tuned multi-objective meta-heuristic algorithms for solving a fuzzy multi-state redundancy allocation problem under discount strategies

Cited by 37 publications

References 45 publications

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study

Multi-objective Redundancy Allocation Problem with weighted-k-out-of-n subsystems

Contact Info

Product

Resources

About