TY - GEN
T1 - A Scatter Search Algorithm using Biological Evolution Strategies to Solve Multilevel Uncapacitated Facility Location Problem
AU - Setyawati, Indah Dwi
AU - Pratiwi, Asri Bekti
AU - Winarko, Edi
N1 - Publisher Copyright:
© 2023 American Institute of Physics Inc.. All rights reserved.
PY - 2023/12/22
Y1 - 2023/12/22
N2 - Multi-Level Uncapacitated Facility Location Problem is a problem branched by Uncapacitated Facility Location Problem consists a set of facility portioned into several level. This problem discusses the placement of a facility to be built and serving customers in order to minimize the total cost of constructions and services. Every customer will be served by exactly one facility in each level. This research studies how to solve Multi-Level Uncapacitated Facility Location Problem using Modified Scatter Search Algorithm. The Modified Scatter Search Algorithm is a modified algorithm from Scatter Search adopted by biological evolution strategies of genetic algorithm which can be classified as evolution algorithm because the individual in this algorithm act as parents who does the reproduce to produce offspring. This algorithm uses references set as the population. The processes which carried out by Modified Scatter Search to solve Multi-Level Uncapacitated Facility Location Problem includes data input, parameter initialization, generate the initial solution, calculate the value of the objective function, create the Reference Set, create the subset of Reference Set, solution combination method, and applying the selection, crossover and mutation to a good individual as a form of modification. To validate its performance, the proposed algorithm is implemented in data using 2 levels of facilities and 3 levels of facilities. The result of running program implemented in two types of data shows that the greater the number of Reference Set, the greater the number of good individuals and the greater the number of iterations, the solution obtained tends to be better with a smaller total cost.
AB - Multi-Level Uncapacitated Facility Location Problem is a problem branched by Uncapacitated Facility Location Problem consists a set of facility portioned into several level. This problem discusses the placement of a facility to be built and serving customers in order to minimize the total cost of constructions and services. Every customer will be served by exactly one facility in each level. This research studies how to solve Multi-Level Uncapacitated Facility Location Problem using Modified Scatter Search Algorithm. The Modified Scatter Search Algorithm is a modified algorithm from Scatter Search adopted by biological evolution strategies of genetic algorithm which can be classified as evolution algorithm because the individual in this algorithm act as parents who does the reproduce to produce offspring. This algorithm uses references set as the population. The processes which carried out by Modified Scatter Search to solve Multi-Level Uncapacitated Facility Location Problem includes data input, parameter initialization, generate the initial solution, calculate the value of the objective function, create the Reference Set, create the subset of Reference Set, solution combination method, and applying the selection, crossover and mutation to a good individual as a form of modification. To validate its performance, the proposed algorithm is implemented in data using 2 levels of facilities and 3 levels of facilities. The result of running program implemented in two types of data shows that the greater the number of Reference Set, the greater the number of good individuals and the greater the number of iterations, the solution obtained tends to be better with a smaller total cost.
UR - http://www.scopus.com/inward/record.url?scp=85181575974&partnerID=8YFLogxK
U2 - 10.1063/5.0181142
DO - 10.1063/5.0181142
M3 - Conference contribution
AN - SCOPUS:85181575974
T3 - AIP Conference Proceedings
BT - AIP Conference Proceedings
A2 - Pusporani, Elly
A2 - Millah, Nashrul
A2 - Hariyanti, Eva
PB - American Institute of Physics Inc.
T2 - International Conference on Mathematics, Computational Sciences, and Statistics 2022, ICoMCoS 2022
Y2 - 2 October 2022 through 3 October 2022
ER -