An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times

2019 ◽  
Vol 39 (4) ◽  
pp. 581-595 ◽  
Author(s):  
Faruk Serin ◽  
Süleyman Mete ◽  
Erkan Çelik
Keyword(s):  
Genetic Algorithm ◽  
Cycle Time ◽  
Efficient Algorithm ◽  
Assembly Line ◽  
Operating Cost ◽  
Practical Case ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Content Type ◽  
Demand Variation

Purpose Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times. Design/methodology/approach In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times. Findings The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost. Practical implications Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case. Originality/value As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

2010 ◽  
Vol 26-28 ◽  
pp. 620-624 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang ◽  
Tuan Liang Li
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Research Attention ◽  
Simply Supported ◽  
Source Data

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

Dynamic testing resource allocation modeling for multi-release software using optimal control theory and genetic algorithm

2020 ◽  
Vol 37 (6/7) ◽  
pp. 1049-1069
Author(s):  
Vijay Kumar ◽  
Ramita Sahni
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Resource Allocation ◽  
Real Life ◽  
Theoretic Approach ◽  
Numerical Illustration ◽  
Data Set ◽  
Allocation Model ◽  
Content Type ◽  
Software Firms

PurposeThe use of software is overpowering our modern society. Advancement in technology is directly proportional to an increase in user demand which further leads to an increase in the burden on software firms to develop high-quality and reliable software. To meet the demands, software firms need to upgrade existing versions. The upgrade process of software may lead to additional faults in successive versions of the software. The faults that remain undetected in the previous version are passed on to the new release. As this process is complicated and time-consuming, it is important for firms to allocate resources optimally during the testing phase of software development life cycle (SDLC). Resource allocation task becomes more challenging when the testing is carried out in a dynamic nature.Design/methodology/approachThe model presented in this paper explains the methodology to estimate the testing efforts in a dynamic environment with the assumption that debugging cost corresponding to each release follows learning curve phenomenon. We have used optimal control theoretic approach to find the optimal policies and genetic algorithm to estimate the testing effort. Further, numerical illustration has been given to validate the applicability of the proposed model using a real-life software failure data set.FindingsThe paper yields several substantive insights for software managers. The study shows that estimated testing efforts as well as the faults detected for both the releases are closer to the real data set.Originality /valueWe have proposed a dynamic resource allocation model for multirelease of software with the objective to minimize the total testing cost using the flexible software reliability growth model (SRGM).

An immune genetic algorithm for simple assembly line balancing problem of type 1

2019 ◽  
Vol 39 (1) ◽  
pp. 113-123 ◽  
Author(s):  
Han-ye Zhang
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Immune Genetic Algorithm ◽  
Content Type ◽  
Assembly Line Balancing Problem ◽  
Simple Assembly ◽  
First Time

Purpose The purpose of this study is to develop an immune genetic algorithm (IGA) to solve the simple assembly line balancing problem of type 1 (SALBP-1). The objective is to minimize the number of workstations and workstation load for a given cycle time of the assembly line. Design/methodology/approach This paper develops a new solution method for SALBP-1, and a user-defined function named ψ(·) is proposed to convert all the individuals to satisfy the precedence relationships during the operation of IGA. Findings Computational experiments suggest that the proposed method is efficient. Originality/value An IGA is proposed to solve the SALBP-1 for the first time.

Balancing a mixed-model assembly line with unskilled temporary workers: algorithm and case study

2018 ◽  
Vol 38 (4) ◽  
pp. 511-523 ◽  
Author(s):  
Dongwook Kim ◽  
Dug Hee Moon ◽  
Ilkyeong Moon
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Temporary Workers ◽  
Labor Costs ◽  
Content Type ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

PurposeThe purpose of this paper is to present the process of balancing a mixed-model assembly line by incorporating unskilled temporary workers who enhance productivity. The authors develop three models to minimize the sum of the workstation costs and the labor costs of skilled and unskilled temporary workers, cycle time and potential work overloads.Design/methodology/approachThis paper deals with the problem of designing an integrated mixed-model assembly line with the assignment of skilled and unskilled temporary workers. Three mathematical models are developed using integer linear programming and mixed integer linear programming. In addition, a hybrid genetic algorithm that minimizes total operation costs is developed.FindingsComputational experiments demonstrate the superiority of the hybrid genetic algorithm over the mathematical model and reveal managerial insights. The experiments show the trade-off between the labor costs of unskilled temporary workers and the operation costs of workstations.Originality/valueThe developed models are based on practical features of a real-world problem, including simultaneous assignments of workers and precedence restrictions for tasks. Special genetic operators and heuristic algorithms are used to ensure the feasibility of solutions and make the hybrid genetic algorithm efficient. Through a case study, the authors demonstrated the validity of employing unskilled temporary workers in an assembly line.

Reconfiguration of assembly line balancing–an automobile case study solved by the exact solution procedure

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ashish Yadav ◽  
Shashank Kumar ◽  
Sunil Agrawal
Keyword(s):  
Cycle Time ◽  
Assembly Line ◽  
Real Life ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Mixed Integer ◽  
Second Step ◽  
Automobile Plant ◽  
Content Type

PurposeMulti-manned assembly lines are designed to produce large-sized products, such as automobiles. In this paper, a multi-manned assembly line balancing problem (MALBP) is addressed in which a group of workers simultaneously performs different tasks on a workstation. The key idea in this work is to improve the workstation efficiency and worker efficiency of an automobile plant by minimizing the number of workstations, the number of workers, and the cycle time of the MALBP.Design/methodology/approachA mixed-integer programming formulation for the problem is proposed. The proposed model is solved with benchmark test problems mentioned in research papers. The automobile case study problem is solved in three steps. In the first step, the authors find the task time of all major tasks. The problem is solved in the second step with the objective of minimizing the cycle time for the sub-tasks and major tasks, respectively. In the third step, the output results obtained from the second step are used to minimize the number of workstations using Lingo 16 solver.FindingsThe experimental results of the automobile case study show that there is a large improvement in workstation efficiency and worker efficiency of the plant in terms of reduction in the number of workstations and workers; the number of workstations reduced by 24% with a cycle time of 240 s. The reduced number of workstations led to a reduction in the number of workers (32% reduction) working on that assembly line.Practical implicationsFor assembly line practitioners, the results of the study can be beneficial where the manufacturer is required to increased workstation efficiency and worker efficiency and reduce resource requirement and save space for assembling the products.Originality/valueThis paper is the first to apply a multi-manned assembly line balancing approach in real life problem by considering the case study of an automobile plant.

A novel optimal method of robotic weld assembly line balancing problems with changeover times: a case study

2018 ◽  
Vol 38 (4) ◽  
pp. 376-386 ◽  
Author(s):  
Binghai Zhou ◽  
Qiong Wu
Keyword(s):  
Local Search ◽  
Cycle Time ◽  
Assembly Line ◽  
Research Work ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Assembly Lines ◽  
Optimal Method ◽  
Content Type ◽  
Changeover Times

PurposeThe balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both assembly tasks and type of robots to every workstation, and present an optimal method of robotic weld assembly line balancing (ALB) problems with the additional concern of changeover times. An industrial case of a robotic weld assembly line problem is investigated with an objective of minimizing cycle time of workstations.Design/methodology/approachThis research proposes an optimal method for balancing robotic weld assembly lines. To solve the problem, a low bound of cycle time of workstations is built, and on account of the non-deterministic polynomial-time (NP)-hard nature of ALB problem (ALBP), a genetic algorithm (GA) with the mechanism of simulated annealing (SA), as well as self-adaption procedure, was proposed to overcome the inferior capability of GA in aspect of local search.FindingsTheory analysis and simulation experiments on an industrial case of a car body welding assembly line are conducted in this paper. Satisfactory results show that the performance of GA is enhanced owing to the mechanism of SA, and the proposed method can efficiently solve the real-world size case of robotic weld ALBPs with changeover times.Research limitations/implicationsThe additional consideration of tool changing has very realistic significance in manufacturing. Furthermore, this research work could be modified and applied to other ALBPs, such as worker ALBPs considering tool-changeover times.Originality/valueFor the first time in the robotic weld ALBPs, the fixtures’ (tools’) changeover times are considered. Furthermore, a mathematical model with an objective function of minimizing cycle time of workstations was developed. To solve the proposed problem, a GA with the mechanism of SA was put forth to overcome the inferior capability of GA in the aspect of local search.

A Methodology of Task Allocation to Design a Human-Robot Assembly Line

2021 ◽  
Vol 12 (3) ◽  
pp. 21-52
Author(s):  
Anh Vo Ngoc Tram ◽  
Morrakot Raweewan
Keyword(s):  
Cycle Time ◽  
Task Allocation ◽  
Assembly Line ◽  
Lower Cost ◽  
Operating Cost ◽  
Time Cost ◽  
Assembly Lines ◽  
Optimal Weights ◽  
Robot Task ◽  
Single Objective

There are successful cases in lean manual assembly lines; however, in some cases, such as the ease of assembly in quicker cycle time, the designs are not satisfactory and must be transformed to semi-automation. This research studies human-robot task allocation when designing for semi-automation considering not only time-cost effectiveness as in the existing research but also assembly difficulty and ergonomic issues. A proposed methodology optimally determines what tasks should be performed by humans or robots, at which station, and in what sequence. A multi-objective linear programming (MOLP) model is proposed to simultaneously minimize total operating cost, cycle time, and ergonomic difficulty. Solving the model has two approaches: with and without optimal weights. The methodology is applied to a Lego-car assembly line. To illustrate the benefits of the proposed MOLP, a comparison between it and three single-objective models is made. Results show that the optimal-weight MOLP yields a better performance (a shorter cycle time, a lower cost, and especially, a significant ergonomic improvement) when compared to the other MOLP and single-objective models.

A multiple rule-based genetic algorithm for cost-oriented stochastic assembly line balancing problem

2019 ◽  
Vol 39 (1) ◽  
pp. 124-139 ◽  
Author(s):  
Ahad Foroughi ◽  
Hadi Gökçen
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Superior Performance ◽  
Line Balancing ◽  
Rule Based ◽  
Content Type ◽  
Assembly Line Balancing Problem ◽  
The Cost ◽  
Stochastic Assembly

Purpose This research aims to address the cost-oriented stochastic assembly line balancing problem (ALBP) and propose a chance-constrained programming model. Design/methodology/approach The cost-oriented stochastic ALBP is solved for small- to medium-sized problems. Owing to the non-deterministic polynomial-time (NP)-hardness problem, a multiple rule-based genetic algorithm (GA) is proposed for large-scale problems. Findings The experimental results show that the proposed GA has superior performance and efficiency compared to the global optimum solutions obtained by the IBM ILOG CPLEX optimization software. Originality/value To the best of the authors’ knowledge, only one study has discussed the cost-oriented stochastic ALBP using the new concept of cost. Owing to the NP-hard nature of the problem, it was necessary to develop a heuristic or meta-heuristic algorithm for large data sets; this research paper contributes to filling this gap.

A genetic algorithm approach for balancing two-sided assembly lines with setups

2019 ◽  
Vol 39 (5) ◽  
pp. 827-839 ◽  
Author(s):  
Yilmaz Delice
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Complex Structure ◽  
Real Life ◽  
Setup Time ◽  
Assembly Lines ◽  
Content Type ◽  
Sequence Dependent ◽  
Time Required ◽  
First Time

Purpose This paper aims to discuss the sequence-dependent forward setup time (FST) and backward setup time (BST) consideration for the first time in two-sided assembly lines. Sequence-dependent FST and BST values must be considered to compute all of the operational times of each station. Thus, more realistic results can be obtained for real-life situations with this new two-sided assembly line balancing (ALB) problem with setups consideration. The goal is to obtain the most suitable solution with the least number of mated stations and total stations. Design/methodology/approach The complex structure it possesses has led to the use of certain assumptions in most of the studies in the ALB literature. In many of them, setup times have been neglected or considered superficially. In the real-life assembly process, potential setup configurations may exist between each successive task and between each successive cycle. When two tasks are in the same cycle, the setup time required (forward setup) may be different from the setup time required if the same two tasks are in consecutive cycles (backward setup). Findings Algorithm steps have been studied in detail on a sample solution. Using the proposed algorithm, the literature test problems are solved and the algorithm efficiency is revealed. The results of the experiments revealed that the proposed approach finds promising results. Originality/value The sequence-dependent FST and BST consideration is applied in a two-sided assembly line approach for the first time. A genetic algorithm (GA)-based algorithm with ten different heuristic rules was used in this proposed model.

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