Optimization of two sided assembly line balancing with resource constraint

Two-sided assembly line balancing (2S-ALB) problems are practically useful in improving the production of large-sized high-volume products. Many research has proposed various approaches to study and balance this well-known ALB problem. Although much attention has been given to solve and optimize 2S-ALB, the majority of the research assumed the workstation has similar capabilities. This research has been conducted in an automotive assembly line, where most of the equipment used in assembly is different from one workstation to another. The assumption that all workstation has similar capabilities lead to inefficient resource utilization in assembly line design. This research aims to model and optimize 2S-ALB with resource constraints. Besides optimizing the line balancing, the proposed model also will minimize the number of resources in the two-sided assembly line. The research begins with problem formulation by establishing four optimization objectives. The considered optimization objectives were to minimize the number of workstations, number of mated-workstation, total idle time, and number of resources. For optimization purpose, Particle Swarm Optimization is modified to find the best solution besides reducing the dependencies on a single best solution. This is conducted by replacing the best solution with the top three solutions in the reproduction process. A set of benchmark problems for 2S-ALB were used to test the proposed Modified Particle Swarm Optimization (MPSO) in the computational experiment. Later, the proposed 2S-ALB with resource constraint model and algorithm was validated using a case study problem. The computational experiment result using benchmark test problems indicated that the proposed MPSO was able to search for better solution in 91.6% of the benchmark problems. The good performance of MPSO is attributed to its ability to maintain particle diversity over the iteration. Meanwhile, the case study result indicated that the proposed 2S-ALB with resource constraint model and MPSO algorithm are able to be utilized for the real problem. In the future, the multiobjective optimization problem will be considered to be optimized for other types of general assembly lines

Type-E Parallel Two-Sided Assembly Line Balancing Problem: Mathematical Model and Ant Colony Optimisation based Approach with Optimised Parameters

Copyright © 2015 Elsevier. This is a PDF file of an unedited manuscript that has been accepted for publication in Computers and Industrial Engineering (doi:10.1016/j.cie.2014.12.037). The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.There are many factors which affect the performance of a complex production system. Efficiency of an assembly line is one of the most important of these factors since assembly lines are generally constructed as the last stage of an entire production system. Parallel two-sided assembly line system is a new research domain in academia though these lines have been utilised to produce large sized products such as automobiles, trucks, and buses in industry for many years. Parallel two-sided assembly lines carry practical advantages of both parallel assembly lines and two-sided assembly lines. The main purpose of this paper is to introduce type-E parallel two-sided assembly line balancing problem for the first time in the literature and to propose a new ant colony optimisation based approach for solving the problem. Different from the existing studies on parallel assembly line balancing problems in the literature, this paper aims to minimise two conflicting objectives, namely cycle time and number of workstations at the same time and proposes a mathematical model for the formal description of the problem. To the best of our knowledge, this is the first study which addresses both conflicting objectives on a parallel two-sided assembly line configuration. The developed ant colony optimisation algorithm is illustrated with an example to explain its procedures. An experimental design is also conducted to calibrate the parameters of the proposed algorithm using response surface methodology. Results obtained from the performed computational study indicate that minimising cycle time as well as number of workstations help increase system efficiency. It is also observed that the proposed algorithm finds promising results for the studied cases of type-E parallel two-sided assembly line balancing problem when the results are compared with those obtained from other three well-known heuristics

Research Trends and Outlooks in Assembly Line Balancing Problems

This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works

Two-sided assembly line balancing using teaching-learning based optimization algorithm

Assembly line balancing plays a crucial role in modern manufacturing companies in terms of the growth in productivity and reduction in costs. The problem of assigning tasks to consecutive stations in such a way that one or more objectives are optimized subject to the required tasks, processing times and some specific constraints is called the assembly line balancing problem (ALBP). Depending on production tactics and distinguishing working conditions in practice, assembly line systems show a large diversity. Although, a growing number of researchers addressed ALBP over the past fifty years, real-world assembly systems which require practical extensions to be considered simultaneously have not been adequately handled. This study deals with an industrial assembly system belonging to the class of two-sided line with additional assignment restrictions which are often encountered in practice. Teaching-learning based optimization (TLBO), which is a recently developed nature-inspired search method, is employed to solve the line balancing problem. Computational results are compared with the current situation in terms of the line efficiency, and the solution structure with workload assigned to the stations is presented. (C) 2014 Elsevier Ltd. All rights reserved