Genetic Algorithm (GA) is a particular class of evolutionary algorithm used as a search technique in

computing to find exact and approximate solutions to optimization and search problems, and it is categorized as

global search heuristics. Genetic Algorithm (GA) use methods inspired by evolutionary biology such as

inheritance, mutation, selection, and crossover (Dwijayanti et. al, 2010). In evolutionary algorithm, Genetic

Algorithm (GA) has been one of the most studied topics which is used to mimic natural processes of genetic

operators, natural selection, inheritance and mutation. Genetic Algorithms have been successful in solving

various optimization problems. (Lim, 2012)

Figure 1. Flow chart illustrating the process of Genetic Algorithm

The figure above shows the process of Genetic Algorithm where initialization are being conducted up until

conclusion. The operation of the GA starts with determining the total number of goods or items available inside

the warehouse randomly or by the use of heuristics. The fitness value can also be used to assess the members of

the population and rank based on the performances. After evaluating all the members of the population, the

lower rank chromosomes are omitted and the remaining populations are used for reproduction. Then, the

crossover step randomly selects two members of the remaining population and exchanges them. Mutation will be

the final process of the algorithm. For this case, the mutation operator randomly mutates on a gene of a

chromosome. Mutation serves as the crucial step in Genetic Algorithm since it ensures that every region of the

problem space can be reached (Rongali, 2017).

Researchers are using mathematical techniques in facility layout planning have developed many forms to

represent their optimization goals or objective functions. Those functions can be categorized to minimize the

total transportation costs of resources between facilities. (Mihajlovic et.al, 2006). Genetic Algorithms engage in

random however directed search in finding directed globally optimal solutions. Usually, a set of GAs require a

depiction scheme to encode optimal solutions to the optimization problem. Length varies with each application,

wherein a solution can be represented as a linear string and it is called chromosome. In order to construct better

solutions, measures of fitness is applied to the current solution. There are three basic operators in the basic GA

system: (a) Crossover, (b) Reproduction or Selection, and (c) Mutation. A process where newly reproduced

strings is randomly coupled and each couple of string exchanges information partially is called crossover.

Reproduction, on the other hand, is a process in which the strings are duplicated according to their fitness level.

Mutation is the rare random alteration of the value of one of the bits in the string (Li and Love, 2000).

In the description of General Algorithm (GA), fitness function and chromosome defined as a paramount

importance. The fitness function is used in quantifying the appropriateness of the solution, which is closely

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linked with the objective of the algorithm or the process of optimization. The fitness level can be used in

evaluating possible solutions, wherein, the values being generated characterize the solutions. Chromosomes are

abstract representation of possible solution. (Shende et. al, 2016)

A facility layout optimization technique is presented that takes into consideration the dynamic

characteristics and operational constraints of the system as a whole, and is able to solve the facility layout design

problem based on a system’s performance measures, such as the cycle time and productivity. Each layout

solution is presented in the form of a string that is suitable for analysis by a genetic algorithm

technique. (Azadivar and Wang, 2010) According to Hernandez et. al (2013), ggenerally, the problem of

designing a physical layout involves the minimization of the material handling cost as one of the main

objectives, although other quantitative aspects can be taken into account (e.g. closeness or distance relationships,

adjacency requirements and aspect ratio). In solving facility layout problems, one of the widely used

optimization techniques that is used is Genetic Algorithm (GA) wherein different crossover operators are

used to solve these type of problems. (Misola et. al., 2013)

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