Solving type-2 assembly line balancing problem with fuzzy binary linear programming

2013 ◽  
Vol 25 (3) ◽  
pp. 517-524 ◽  
Author(s):  
Giada La Scalia
Keyword(s):  
Linear Programming ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Binary Linear Programming ◽  
Assembly Line Balancing Problem

Assembly Line Balancing Type-2 Using Unequal Multiple Operators

2013 ◽  
Vol 816-817 ◽  
pp. 1169-1173
Author(s):  
Usman Attique ◽  
Abdul Ghafoor ◽  
Riaz Ahmed ◽  
Shahid Ikramullah
Keyword(s):  
Cycle Time ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Computational Effort ◽  
Line Balancing ◽  
Heuristic Methods ◽  
Novel Approach ◽  
Assembly Line Balancing Problem

Various exact and heuristic methods have been proposed for assembly line balancing problem (ALBP) but unequal multiple operators have not been considered much. In this paper we present a novel approach of assembly line balancing Type-2 with unequal multiple operators by using an already developed code in Matlab (Tomlab modeling platform). The adopted approach can be applied at line balancing problems ranging from few to hundreds of work elements to achieve minimum cycle time with very less computational effort.

scholarly journals Simple Assembly Line Balancing Problem Type 2 By Variable Neighborhood Strategy Adaptive Search: A Case Study Garment Industry

2020 ◽  
Vol 6 (1) ◽  
pp. 21
Author(s):  
Ganokgarn Jirasirilerd ◽  
Rapeepan Pitakaso ◽  
Kanchana Sethanan ◽  
Sasitorn Kaewman ◽  
Worapot Sirirak ◽  
...  
Keyword(s):  
Assembly Line ◽  
Assembly Line Balancing ◽  
Black Box ◽  
Line Balancing ◽  
Problem Type ◽  
Adaptive Search ◽  
Assembly Line Balancing Problem ◽  
Simple Assembly

This article aims to minimize cycle time for a simple assembly line balancing problem type 2 by presenting a variable neighborhood strategy adaptive search method (VaNSAS) in a case study of the garment industry considering the number and types of machines used in each workstation in a simple assembly line balancing problem type 2 (SALBP-2M). The variable neighborhood strategy adaptive search method (VaNSAS) is a new method that includes five main steps, which are (1) generate a set of tracks, (2) make all tracks operate in a specified black box, (3)operate the black box, (4) update the track, and (5) repeat the second to fourth steps until the termination condition is met. The proposed methods have been tested with two groups of test instances, which are datasets of (1) SALBP-2 and (2) SALBP-2M. The computational results show that the proposed methods outperform the best existing solution found by the LINGO modeling program. Therefore, the VaNSAS method provides a better solution and features a much lower computational time.

A Heuristic Approach for Type 2 Assembly Line Balancing Problem

2015 ◽  
Vol 789-790 ◽  
pp. 1296-1300
Author(s):  
Bukhari Pakeeza ◽  
Ahmad Riaz ◽  
Muhammad Umer
Keyword(s):  
Assembly Line ◽  
Experimental Testing ◽  
Assembly Line Balancing ◽  
Task Assignment ◽  
Line Balancing ◽  
Cycle Times ◽  
Time In Literature ◽  
Assembly Line Balancing Problem

An assembly system consists of work stations where specific tasks are carried in such a way that last station gives the complete product. An assembly line balancing Problem (ALBP) involves optimally assigning tasks among workstations with respect to some performance objective. ALBP problems are of NP hard nature and in literature; many efforts are there to solve the problem efficiently through heuristics. This paper proposes a heuristic algorithm for solving type 2 ALBP for single objective optimization. The proposed algorithm assigns tasks to a fixed no of stations with objective of minimizing cycle time. In literature, type 2 ALBP is mostly solved through Type 1 problem. However, this paper proposes a direct approach to Type 2 ALBP. The effectiveness is tested through application on Gunther problem of 35 tasks with forty five precedence constraints. The task assignment for six stations is computed and it shows competitive performance. The number of fixed stations is varied and corresponding cycle times are computed. The algorithm is also tested on a real industrial problem of 24 tasks. The experimental testing indicates tendency of the proposed algorithm to give effective optimal results.

An integer-coded differential evolution algorithm for simple assembly line balancing problem of type 2

2016 ◽  
Vol 36 (3) ◽  
pp. 246-261 ◽  
Author(s):  
Haijun Zhang ◽  
Qiong Yan ◽  
Yuanpeng Liu ◽  
Zhiqiang Jiang
Keyword(s):  
Differential Evolution ◽  
Assembly Line ◽  
Differential Evolution Algorithm ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Content Type ◽  
Evolution Algorithm ◽  
Assembly Line Balancing Problem ◽  
Simple Assembly

Purpose This paper aims to develop a new differential evolution algorithm (DEA) for solving the simple assembly line balancing problem of type 2 (SALBP-2). Design/methodology/approach Novel approaches of mutation operator and crossover operator are presented. A self-adaptive double mutation scheme is implemented and an elitist strategy is used in the selection operator. Findings Test and comparison results show that the proposed IDEA obtains better results for SALBP-2. Originality/value The presented DEA is called the integer-coded differential evolution algorithm (IDEA), which can directly deal with integer variables of SALBP-2 on a discrete space without any posterior conversion. The proposed IDEA will be an alternative in evolutionary algorithms, especially for various integer/discrete-valued optimization problems.

scholarly journals Multi-Objective Assembly Line Balancing Problem with Setup Times Using Fuzzy Goal Programming and Genetic Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 333
Author(s):  
Amy H. I. Lee ◽  
He-Yau Kang ◽  
Chong-Lin Chen
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Assembly Line ◽  
Programming Model ◽  
Assembly Line Balancing ◽  
Linear Programming Model ◽  
Line Balancing ◽  
Multi Objective ◽  
Assembly Line Balancing Problem ◽  
Genetic Algorithm Model

Assembly lines are often indispensable in factories, and in order to attain a certain level of assembly line productivity, multiple goals must be considered at the same time. However, these multiple goals may conflict with each other, and this is a multi-objective assembly line balancing problem. This study considers four objectives, namely minimizing the cycle time, minimizing the number of workstations, minimizing the workload variance, and minimizing the workstation idle time. Since the objectives conflict with each other, for example, minimizing the cycle time may increase the number of workstations, the fuzzy multi-objective linear programming model is used to maximize the satisfaction level. When the problem becomes too complicated, it may not be solved by the fuzzy multi-objective linear programming model using a mathematical software package. Therefore, a genetic algorithm model is proposed to solve the problem efficiently. By studying practical cases of an automobile manufacturer, the results show that the proposed fuzzy multi-objective linear programming model and the genetic algorithm model can solve small-scale multi-objective assembly line balancing problems efficiently, and the genetic algorithm model can obtain good solutions for large-scale problems in a short computational time. Datasets from previous works are adopted to examine the applicability of the proposed models. The results show that both the fuzzy multi-objective linear programming model and the genetic algorithm model can solve the smaller problem cases and that the genetic algorithm model can solve larger problems. The proposed models can be applied by practitioners in managing a multi-objective assembly line balancing problem.

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