An elitist strategy genetic algorithm using simulated annealing algorithm as local search for facility layout design

Leno, I. Jerin; Sankar, S. Saravana; Ponnambalam, S. G.

doi:10.1007/s00170-013-5519-3

Cited by 14 publications

(8 citation statements)

References 32 publications

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“…These kinds of algorithms have in common an improvement of the exploration of the searching space by means of maintaining the diversity of the individuals in the population, which allows the achievement of good results within a reasonable computational time. Other remarkable approaches to the problem have been Simulated Annealing [29][30][31][32], Tabu Search [33][34][35], Wang-Landau sampling algorithms [36], and Particle Swarm Optimization [37][38][39], among others. More recently, Coral Reef Optimization (CRO) algorithms have been used to improve the solutions obtained.…”

Section: Introductionmentioning

confidence: 99%

A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

Salas-Morera¹,

García-Hernández²,

Carmona-Muñoz³

2021

Applied Sciences

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The problem of Unequal Area Facility Layout Planning (UA-FLP) has been addressed by a large number of approaches considering a set of quantitative criteria. Moreover, more recently, the personal qualitative preferences of an expert designer or decision-maker (DM) have been taken into account too. This article deals with capturing more than a single DM’s personal preferences to obtain a common and collaborative design including the whole set of preferences from all the DMs to obtain more complex, complete, and realistic solutions. To the best of our knowledge, this is the first time that the preferences of more than one expert designer have been considered in the UA-FLP. The new strategy has been implemented on a Coral Reef Optimization (CRO) algorithm using two techniques to acquire the DMs’ evaluations. The first one demands the simultaneous presence of all the DMs, while the second one does not. Both techniques have been tested over three well-known problem instances taken from the literature and the results show that it is possible to obtain sufficient designs capturing all the DMs’ personal preferences and maintaining low values of the quantitative fitness function.

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Section: Introductionmentioning

confidence: 99%

A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

Salas-Morera¹,

García-Hernández²,

Carmona-Muñoz³

2021

Applied Sciences

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“…Anjos and Vieira [19] firstly optimized the relative location of departments in the region and then determined the inner precise layout. The model solved sequentially may lead to suboptimal solutions, so Leno et al [20] proposed a hybrid algorithm to optimize the internal and external layout at the same time. Various algorithms were adopted, such as GA [21,22] and the simulated annealing algorithm [23,24], and both of these have obtained reasonable results.…”

Section: Introductionmentioning

confidence: 99%

Coupling Layout Optimization of Key Plant and Industrial Area

Zhao

et al. 2020

Processes

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Layout problems are an engineering task that heavily relies on project experience. During the design of a plant, various factors need to be considered. Most previous efforts on industrial layout design have focused on the arrangement of facilities in a plant. However, the area-wide layout was not thoroughly studied and the relationship between plant layout and area-wide layout was rarely mentioned. In this work, the key plant that has the greatest impact on the industrial area is figured out first, and then the coupling relationships between the key plant and the industrial area are studied by changing the occupied area and length-width ratio of the key plant. Both of them are achieved by changing the floor number. A hybrid algorithm involving the genetic algorithm (GA) and surplus rectangle fill algorithm (SRFA) is applied. Various constraints are considered to make the layout more reasonable and practical. In the case study, a refinery with 20 plants is studied and the catalytic cracking plant is found to be the key plant. After the retrofit, the total cost of the refinery is 1,806,100 CNY/a less than that before, which illustrates the effectiveness of the method.

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“…Zhang, et al [17] used ASA to track differences in areas experiencing drought based on time intervals. Jerin Leno, et al [18] proposed a strategy to increase the convergence of the SA algorithm.…”

Section: Introductionmentioning

confidence: 99%

The Determination of Preventive Maintenance using Simulated Annealing Algorithm based on Weighted Fitness Function

Franty

Handoko

2019

JTI

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This study aims to determine the machine maintenance schedule. We use the Simulated Annealing Algorithm. Fitness and reliability functions are functions that are used in the optimization process. Several weighting scenarios are done to see the unity of the function. The results of the scenario produce several alternative schedules. This algorithm is implemented on machines that have more than one sub-machine. This sub-machine is a smaller engine system part. This sub-machine also has one particular function. The results of the study show that the optimal engine maintenance period to use is six periods. There are five scheduling scenarios used in this problem. The resulting schedule can increase the value of reliability and can minimize costs.

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An elitist strategy genetic algorithm using simulated annealing algorithm as local search for facility layout design

Cited by 14 publications

References 32 publications

A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

Coupling Layout Optimization of Key Plant and Industrial Area

The Determination of Preventive Maintenance using Simulated Annealing Algorithm based on Weighted Fitness Function

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