Sensitivity Analysis to Determine the Parameters of Genetic Algorithm for Machine Layout

Srinivas, C.; Reddy, B. Ramgopal; Ramji, K.; Naveen, R

doi:10.1016/j.mspro.2014.07.104

Cited by 25 publications

(10 citation statements)

References 10 publications

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“…Concerning the mutation operator, its rate is generally low, since a high probability may lead to a sub-optimal solution. 24 To make our choice, a sensitivity analysis of the parameters of the evolutionary algorithm is performed to determine the parameters that produce good solutions. This study is practically helpful when real-world problems are to be solved and when there is no hint of the optimal solutions.…”

Section: Implementation and Experimentation Resultsmentioning

confidence: 99%

An evolutionary approach to solve the dynamic multihop ridematching problem

2016

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The multihop ridesharing system generates a ridematching solution with an arbitrary number of transfers that respects personal preferences of the users and their time constraints with detour willingness. As it is considered to be NP-complete, an efficient metaheuristic is required in the application to solve the dynamic multihop ridematching problem. In this context, a novel approach, called Metaheuristics Approach Based on Controlled Genetic Operators (MACGeO), which is supported by an original dynamic coding, is developed to address the multihop ridematching problem. The performance of the proposed approach is measured via simulation scenarios, which feature various numbers of carpool drivers (vehicles) and riders (passengers). Experimental results show that the multihop ridematching could greatly increase the number of matched requests while minimizing the number of vehicles required.

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Section: Implementation and Experimentation Resultsmentioning

confidence: 99%

An evolutionary approach to solve the dynamic multihop ridematching problem

2016

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“…The analysis is performed for different values of crossover rate (R c = 0.4/0.6/0.8), mutation rate (R m = 0.05/0.1/0.15), and population size (S p = 100/250/500). By using a traditional experimental design (Srinivas et al 2014), the entire analysis requires 3 3 = 27 experiments to fully investigate the differences and changes, and it is not easy to compare the findings.…”

Section: Sensitivity Analysis For 2pmgaomentioning

confidence: 99%

Integrating Internet of Things and multi-temperature delivery planning for perishable food E-commerce logistics: a model and application

Tsang

Lam

et al. 2020

International Journal of Production Research

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“…Simultaneously, the best values of the model parameters were determined by sensitivity analysis to produce high-quality solutions. This study has practical importance for the solution of real-world problems when the optimal solutions are completely unknown [32]. After some preliminary evaluations, the sensitivity analysis was performed for different values in the GA for crossover (0.4 to 0.9), population size (10 to 20), and mutation probabilities (0.1 to 0.4).…”

Section: Sensitivity Analysis and Tuning The Model Parametersmentioning

confidence: 99%

The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Bolouri

Vafaeinejad

Alesheikh

et al. 2018

IJGI

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Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000) to stations in a discrete space in a GIS, assuming that the stations' capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions.

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Sensitivity Analysis to Determine the Parameters of Genetic Algorithm for Machine Layout

Cited by 25 publications

References 10 publications

An evolutionary approach to solve the dynamic multihop ridematching problem

An evolutionary approach to solve the dynamic multihop ridematching problem

Integrating Internet of Things and multi-temperature delivery planning for perishable food E-commerce logistics: a model and application

The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

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