Solving Multi-Objective Paired Single Row Facility Layout Problem Using Hybrid Variable Neighborhood Search

The footwear industry is distinguished by its manual assembly line and a high proportion of shared workstation configuration. This study focuses on a subset of the single row facility layout problem known as the paired single row facility layout problem. As one of type of single-row facility layout,...

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Bibliographic Details
Published inJurnal Teknik Industri Vol. 23; no. 2; pp. 171 - 182
Main Author Maghfiroh, Meilinda Fitriani Nur
Format Journal Article
LanguageEnglish
Published Surabaya Universitas Kristen Petra / Petra Christian University 01.12.2021
Petra Christian University
Subjects
adjacency
Assembly lines
Configurations
Facilities management
Genetic algorithms
Heuristic methods
Layouts
material handler
multi-objectives
Multiple objective analysis
Optimization
paired single-row layout problem
Representations
variable neighborhood search
Work stations
Workstations
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Summary:The footwear industry is distinguished by its manual assembly line and a high proportion of shared workstation configuration. This study focuses on a subset of the single row facility layout problem known as the paired single row facility layout problem. As one of type of single-row facility layout, the paired single row facility layout problem cannot be solved quickly. Further, different objectives also need to be considered in the decision-making process. Therefore, multi-objective approaches are proposed to minimize the penalty of material handler usage while maximizing the adjacency function based on each workstation's closeness rating. A Single Row Facility Layout is an NP-hard problem; this problem also belongs to the NP-hard problem class. As a result, we propose a hybrid method combining variable neighborhood search (VNS) and genetic algorithm (GA) to solve the problem of obtaining the optimal configuration of a multi-objective paired single-row assembly line. A heuristic approach was used to create the schematic representation solution. To obtain the neighborhood solutions, a hybrid VNSGA was used. The schematic representation solution employs crossover and variable neighborhood descent. Using the concept of VNS, the neighborhood was changed in each generation.
ISSN:1411-2485
2087-7439
DOI:10.9744/jti.23.2.171-182