Assembly Line Balancing Problem Research Articles

Research on the optimization of an angle grinder assembly line based on lean thinking

ABSTRACT Aim to solve all kinds of problem existing in the angle grinder assembly line of the enterprise, such as the unreasonable distribution of work procedures, serious work in progress (WIP) inventory, chaotic production line layout and low assembly line balancing. Value Stream Map (VSM) is used to describe the layout and process diagram of the assembly line and a future value flow chart is established to optimize the angle grinder assembly line in this paper. IE method is utilized to improve the layout of the assembly line and bottleneck location, and the assembly line balance problem in advanced industrial manufacturing is combined with the genetic algorithm (GA). The assembly line improved effectively shortens the product assembly cycle, reduces the inventory and material handling distance, and improves the production line balance.

Balancing stochastic type-II assembly lines: chance-constrained mixed integer and constraint programming models

ABSTRACT In the literature, line balancing is mostly investigated in deterministic environments. But production systems inevitably contain stochastic situations. In this study, the stochastic type-II assembly line balancing problem (ALBP) is considered. Firstly, a chance-constrained nonlinear mixed integer programming (MIP) formulation is developed from the well-known deterministic form. Then, a new linearized stochastic model is proposed by using some transformation approaches to reduce model complexity, and the model is solved. Finally, constraint programming (CP) models for deterministic ALBPs, nonlinear chance-constrained stochastic ALBPs and linearized chance-constrained stochastic ALBPs are developed, respectively. Problems from the literature are utilized to test the effectiveness of the proposed models and the results are compared with a bidirectional heuristic algorithm. The numerical results show that the CP models are more effective and successful for solving the stochastic ALBP. Some managerial implications are also suggested for industrial environments that consistently face ALBPs.

Decomposition-based bi-objective optimization for sustainable robotic assembly line balancing problems

Due to the increasing greenhouse gas emissions and the energy crisis, the manufacturing industry which is one of the most energy intensive sector is paying close attention to the improvement of environmental performance efficiency. Therefore, in this paper the automated assembly line is balanced in a sustainable way which aims to optimize a green manufacturing objective (the total energy consumption) and a productivity-related objective (similar working load) simultaneously. A comprehensive total energy consumption of each processing stage was analyzed and modeled. To make the model more practical, a sequence-based changeover time and robots with different efficiencies and energy consuming rates are considered and optimized. To properly solve the problem, the proposed novel optimal solution takes the well-known MOEA/D as a base and incorporates a well-designed coding scheme and a problem-specific local search mechanism. Computational experiments are conducted to evaluated each improving strategies of the algorithm and its superiority over two other high-performing multi-objective optimization methods. The model allows decision makers to select more sustainable assembly operations based on their decision impacts in both productivity and energy-saving.

Solving Assembly Production Line Balancing Problem Using Greedy Heuristic Method

This paper focuses on assembly line balancing (ALB) problem which the objective is to maximize the assembly line efficiency. The problem solved using a greedy heuristic method. MATLAB Software is used to perform the proposed greedy heuristic method. Then, the proposed method is applied to a previous real life case problem that is found in literature for the cookers assembly line in the Light Industrial Company in Iraq. The results of the proposed method is compared with the performance of the company without applying any assembly line balancing method. The result of this paper is also compared with the results obtained from a previous work in the literature which is based on Shortest Operation Time (SOT) using the software named Quantitative Methods, Production and Operations Management (POM-QM). The outcomes of the research have shown that the greedy heuristic method is more efficient, where the efficiency increased from 78.24% before applying assembly line balancing and 81.64% for the previous work based on SOT method to 85.53% when applying the greedy heuristic method. The research has recommended that the decision maker in the company should follow the most appropriate method to achieve a high efficiency operations.

PROBLEM SOLVING IN CROSS OVER LINE BALANCING USING HHO

The assembly line is a series of workflow manufacturing structures that are still common in large scale manufacturing of sustainable items. In the unbalanced tax system, more idle time is required to complete the task; A similar hanging problem arises when the tasks are not properly planned. This article proposes a technique to the line balance problem with crossover workstations. Different techniques were developed to resolve assembly line balance troubles. Existing methods for solving the assembly line balance problem can’t be implemented to included type issues. Using the Harris Hawks optimization, improve the performance of a network-based line balancing problem. The assembly line optimization is performed using the Harris Hawks optimization cycle multipoint crossover method to acquire actual posterity and the high-quality chromosome is chosen to determine the performance of the sequential construction system.

An Improved Lexicographical Whale Optimization Algorithm for the Type-II Assembly Line Balancing Problem Considering Preventive Maintenance Scenarios

In the traditional assembly line balancing, all the workstations are assumed available and hence the unavailability of any workstation brings about the stoppage of the whole line and the waste of the production capacity in the rest workstations. Considering the planning characteristic of preventive maintenance, this paper proposes a novel methodology of integrating the preventive maintenance scenarios into assembly line balancing problems to bypass the unavailable workstation. A lexicographic model is formulated to generate multiple task assignment plans that ensure primarily the high productivity under regular operation scenario and guarantee secondarily the production continuity under preventive maintenance scenarios. And, an improved whale optimization algorithm (IWOA) with three modifications is proposed to solve this problem. Specifically, a combined crossover operator enhances better combination in exploration; three best search agents promote the exploitation; partial regeneration avoids being trapped in local optima. More than five thousand experiments demonstrate that the joint of three modifications endows the IWOA significant superiority over six variants and other six well-known algorithms. Moreover, integrating preventive maintenance scenarios into the assembly line balancing problem increases the production efficiency by 1% at the cost of small production adjustment.

Heuristics based on Slope Indices for Simple Type I Assembly Line Balancing Problems and Analyzing for a Few Performance Measures

In any mechanical manufacturing industry, raw materials’ cost is the major cost component which constitutes up to 40% of the total cost. Other major cost is the processing cost to convert the raw materials in to a finished product that takes place in an assembly line. Optimizing the available resources including the number of work benches and operators is important to meet the timely delivery of finished goods with optimum cost. In an assembly line, the manufacturing processes on any material are sub-divided into many tasks which may include sub-assemblies and machining. These tasks are carried out in several work stations which are responsible for a single or a set of operations carried out on the materials. Assembly lines need to be balanced to have even distribution of work for both men and machines. Type 1 simple assembly line balancing problems (SALBP-1) refer to minimization of number of work stations by keeping the cycle time constant. If the number of work stations is fixed, minimizing the cycle time falls under Type II problems. To solve these types of NP-hard problems, heuristics are used by researchers. The heuristics use different parameters to assign a particular task to a work station. This paper proposes and analyses a few heuristics that can be used to solve SALBP-1. They use slope indices to order the jobs and assign them to different work stations. The slope indices are the ratios of any two parameters taken suitably

A consecutive heuristic algorithm for balancing a mixed - model assembly line type II using a (W-TAWH) model developed for straight and U-shaped layouts

The development of assembly lines in manufacturing systems has evolved due to consumer expectations and a growing and increasingly competitive global market. The assembly line balancing problem (ALBP) is well recognised in manufacturing and may arise when tasks are assigned to the workstation. In this paper, a problem statement is structured by assigning a task to a worker in a suitable workstation, subject to some constraints, in order to reduce the cycle time and improve performance. This is a new approach, based on worker and task assignment and is thus a worker – task assigned to workstation heuristic model (W-TAWH). It has been developed for straight and U-shaped models. The methodology is described as two stages, the input related data stage and assignment stage. Firstly, the focus is on combining precedence diagram and generating the sequence vector procedure. The second stage comprises development of comprehensive mathematical formulae for the consecutive algorithm to address the straight and U – shaped balancing problem, with minimization or maximation of relevant criteria. This generates performance evaluation criteria according to the number of tasks and workers assigned to the workstation, and these criteria are integrated into a single score using a desirability function approach. Finally, the performance of the developed algorithm is validated through a numerical example. We tested 24 alternative solutions and the fourth solution (1-4-1) achieved the highest score (0.726), which represented the following assignment variables: straight layout, minimum total number of successor tasks rule, and four workers. The performance evaluation is represented by the lower bound of cycle time and upper bound of efficiency.

Assembly line balancing for personalized production

Abstract Assembly line balancing problems aim to an efficient and effective assignment of all the required tasks to workstations in a flow oriented production system. Nowadays, assembly lines have to face the manufacturing of extremely personalized products (e.g. cars) as requested by an increasingly higher portion of the market demand. Several literature contributions focus on different balancing problems affected by the wide variety of the final product, e.g. mixed and multi model assembly lines. However, no contribution seems to tackle the personalized production of goods. Such products require to assemble a certain number of tasks whatever the final product personalization is, and a variable number of optional of different type determined by the specifications of every single costumer. This paper faces the generalized assembly of personalized goods proposing an innovative two step methodology to optimize the workload balancing between the assembly line stations, considering traditional tasks and the optional required by the product personalization, which could occur with different frequencies and pairings. The first phase of the developed methodology executes a clustering of product options required by the costumers based on a similarity index. This phase leads to the definition of several sets of optional typically requested together by the customer and with similar mounting time. The methodology second phase leverages the defined clusters of optional. Indeed, optional of the same cluster shouldn’t be assigned to the same workstation to reduce the overload or underload of the assembly operators. An integer programming model is proposed to assign both traditional tasks and optional to stations, to maximize the assembly line balancing considering the order frequency and assembly time of the clusterized optional. An industrial case study is adopted to test and validate the proposed two steps methodology. The obtained results highlight a consistent time balancing between assembly line workstations and a significant limitation of the operator overloads.

Robust optimization for U-shaped assembly line worker assignment and balancing problem with uncertain task times

Awareness of the importance of U-shaped assembly line balancing problems is all on the rise. In the U-shaped assembly line, balancing is affected by the uncertainty associated with the assembly task times. Therefore, it is crucial to develop an approach to respond to the uncertainty caused by the task times. When the great majority of existing literature related to uncertainty in the assembly line is considered, it is observed that the U-shaped assembly line balancing problem under uncertainty is scarcely investigated. That being the case, we aim to fill this research gap by proposing a robust counterpart formulation for the addressed problem. In this study, a robust optimization model is developed for the U-shaped assembly line worker assignment and balancing problem (UALWABP) to cope with the task time uncertainty characterized by a combined interval and polyhedral uncertainty set. A real case study is conducted through data from a company producing water meters.

A Case Study in Line Balancing and Simulation

Assembly line balancing (ALB) allocates individual tasks to work stations while respecting the physical, safety, and quality constraints. Two-sided assembly lines are generally used in the production of medium or large-sized products (e.g. automotive, household appliance). We considered several characteristics including zoning constraints, the task to task relationships, tooling and station dependent constraints to offer the real-world environment. The most common two objectives for the ALB are minimizing the number of workers (type-1) and minimizing the cycle time (type-2). This article presents an integer programming formulation for both type-1 and type-2 ALB problems and metaheuristics to solve this complex problem. Even if ALB gives better results than the current line balance that our industry partner applied, it cannot be guaranteed that the amount of daily production will increase due to randomness in the line. We simulate the proposed line balances to provide a testing platform for line balancing results and to help identify inefficiencies and bottlenecks in the system.

A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing

Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.

A systematic review of research themes and hot topics in assembly line balancing through the web of science within 1990–2017

Assembly line balancing (ALB) deals with how tasks are assigned in an assembly line to reach the required production rate and to optimize goals. Tasks in assembly line are assigned in an ordered sequence of workstations, where constraints such as precedence relations of tasks are satisfied and performance indicators are optimized. Implementing ALB could bring about optimal or semi-optimal responses that reduce initiation costs and operational costs in production systems. The purpose of this study was to analyze the contents of research papers addressing ALB and to predict the direction of future studies concerned with the ALB problems and those of U-type ALB problems. As such, this study drew on a systematic approach to reviewing the papers dealing with ALB published within 1990–2017. The papers under investigation were selected from the Web of Science database and were analyzed with aid of related software based on their content, collaboration network, and citation analysis. Through this process, the top productive countries, institutions and authors were explored. Furthermore, cooperative content analysis, keyword frequency analysis, and co-occurrence (co-word) analysis were applied to explore the current hot topics, and predict future trend of studies. This study could considerably contribute to researchers’ perspective of the research topics in the field and of the gaps that demand further investigation.

A review on mixed assembly line balancing, sequencing and scheduling in an industry

Assembly line in a production unit is a line of workers and machines. In a manufacturing unit the production of components are done on assembly line. The product moves along this line while it is being processed. The assembly lines changed with time from single models straight lines to the mixed, multi -model lines in a flexible manufacturing system and also lines with parallel work stations, U-shaped lines. The main objective of assembly line design for a system is balancing the assembly line according to the desired production volume per shift and minimizing the number of workstations. In this paper, different assembly line balancing problems (ALBP) have been reviewed and discussed. For solving the assembly line balancing and sequencing problems, the development of mathematical model may be quite helpful for the researchers.

An effective artificial fish swarm optimization algorithm for two-sided assembly line balancing problems

Two-sided assembly lines are often used in assembly of large-sized products, such as automobiles, buses and trucks. Compared to the traditional one-sided assembly line, two-sided assembly line has advantages of shorter line and higher utilization of fixture. However, normal balancing method is not applicable to solve the two-sided assembly line balancing problem since the constraint conditions become more complicated. On the other hand, artificial fish swarm algorithm is a relatively new member of swarm intelligence based on swarm behaviors that were inspired from social behaviors of fish swarm in nature. As a typical application of behaviorism in artificial intelligence, artificial fish swarm algorithm can search for the global optimum. So it is a good candidate for developing new search algorithm for solving optimization problems in operational research. In this research, an effective discrete artificial fish swarm algorithm is developed to solve the cost-oriented assembly line balancing problems which aims to minimize the construction cost and at the same time minimize the number of mate-station. Through extensive computational experiments, the performance of the proposed artificial fish swarm algorithm is examined. The experimental results validate the effectiveness and efficiency of the proposed method.

Simple assembly line balancing problem of Type 1 with grey demand and grey task durations

PurposeThe purpose of this paper is to present a mixed integer programming model for simple assembly line balancing problems (SALBP) with Type 1 when the annual demand and task durations are uncertain and encoded with grey numbers.Design/methodology/approachGrey theory and grey numbers are used for illustrating the uncertainty of parameters in an SALBP, where the objective is to minimize the total number of workstations. The paper proposes a 0-1 mathematical model for SALBP of Type 1 with grey demand and grey task durations.FindingsThe uncertainty of the demand and task durations are encoded with grey numbers and a well-known 0-1 mathematical model for SALBP of Type 1 is modified to find the minimum number of workstations in order to meet both the lower and upper bounds of the uncertain demand. The results obtained from the proposed mathematical model show a task-workstation assignment that does not distribute precedence relations among tasks and workstations and the sum of task durations in each single workstation is less than or equal to the grey cycle time.Originality/valueThe grey theory and grey numbers have not been previously used to identify uncertainties in assembly line balancing problems. Therefore, this study provides an important contribution to the literature.

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

PurposeThis 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/approachThe 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).FindingsAlgorithm 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/valueThe 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.

An Effective Grouping Evolution Strategy Algorithm Enhanced with Heuristic Methods for Assembly Line Balancing Problem

Assembly line balancing problem (ALBP) is an allocation of given tasks to the workstations in a way that the number of workstations or the idle times of workstations get minimized. ALBP is a well-known problem in mass-production systems with high production volume and low diversity. In division and grouping allocation problems like ALBP, metaheuristic algorithms based on group structure, i.e. grouping genetic algorithm, are more efficient. The aim of this study is developing a new solution procedure to minimize the number of workstations for a given cycle time based on grouping evolution strategy. A modification of the ranked positional weight method is proposed to construct the initial solution, and a new heuristic method based on a modified version of the COMSOAL method is provided to tighten the solution after performing the mutation operator. Different strategies based on line efficiency and line smoothness indexes are considered to select the best result and transfer it to the next generation. Moreover, to evaluate the performance of proposed algorithms, some well-known standard test problems are utilized. Computational results indicate that the proposed solution algorithm performs efficiently and can obtain the optimal global solution in most of the high dimensional problems.

Model and metaheuristics for robotic two-sided assembly line balancing problems with setup times

Two-sided robotic assembly lines are employed to assemble large-sized high-volume products, where robots are allocated to the workstations to perform the tasks and human workers are replaced for achieving lower cost and greater flexibility in production. In the two-sided robotic assembly lines, setup times are unavoidable and it has been ignored in most of the reported works. There has been limited attention on this till date. This paper focusses on the robotic two-sided assembly line with consideration of sequence-dependent setup times and robot setup times. A new mixed integer linear programming model is developed with the objective of optimizing the cycle time. Due to the NP-hard nature of the considered problem, this paper proposes a set of metaheuristics to solve this considered problem, where two main scenarios with low and high setup time’s variability are considered. Computational results verify that this new model is capable to achieve the optimal solutions for small-size instances whereas the simple adoption of the published mathematical model might produce wrong solutions for the considered problem. A comprehensive study with 13 algorithms demonstrates that the two variants of artificial bee colony algorithm and migrating bird optimization algorithm are capable to achieve the optimality for small-size instances and to obtain promising results for large-size instances.

Constraint programming model for resource-constrained assembly line balancing problem

The literature studies assume that resources used to be perform the tasks are certain and homogenous in any assembly line. However, tasks may need to be processed by general resource requirements in real life. These general resources could be classified by usage of resources such as simple or multiple, alternative and concurrent. The problem which is related to assignment of the task to any workstation and assignment of resources needed by the task simultaneously is defined as resource-constrained assembly line balancing problems (RCALBPs). In this study, a multiobjective model with minimization of cycle time and resource usage for a given number of stations is modeled to solve the RCALBP for the first time. Alternative and general resource types for tasks and using more than two resource type requirements are also considered. A constraint programming model is developed and solved to find the optimal solutions of these problems. The proposed models are tested with sample scenarios to show the effectiveness of the model.