Model Assembly Line Balancing Research Articles

A GRA-based approach for optimal selection of SALBP-1 assembly line balancing heuristics

Assembly line balancing is a compulsory approach which enables mass production of similar parts with proper efficacy. Multiple heuristics are proposed in literature to solve single model assembly line balancing problems (SALBP-1). Previous work has focused on developing and evaluating assembly line balancing heuristics and apply them to different fields. Other work investigates the factors affecting the performance of particular assembly line balancing heuristic. However, there is still a need for a guideline for selecting the most suitable heuristic for a particular application with insights into multiple performance measures. This research presents a grey relational analysis (GRA)-based approach to aid process designers in selecting the heuristic generating that yields the best line balancing. Specifically, combinations of factors of cycle time, order strength, number of tasks, and time variability are considered as operational setups of the line balancing process. The solution of each setup is solved by each heuristic, and the performance of the solution is evaluated. Then, GRA is applied to rank the heuristics on basis of the resulting grey rational grades. The proposed approach provides useful insights into the combinations suitable for a particular heuristic. In this study, time variability and cycle time are shown to have the greatest impact on the overall performance of the considered heuristics.

The Effect of Learning on Assembly Line Balancing: A Review

Classical assembly line balancing (ALB) models assume constant cycle times during production. However, this assumption oversimplifies the actual situation, especially in small batch production of up to a few hundred units, since employees can significantly improve their performance thanks to the learning effect, causing task times to decrease. Several researchers have realised the importance of the effect of learning in ALB. However, only a limited number of papers have so far addressed this issue. This is problematic, since ignoring the learning effect in ALB may lead to inaccurate results and by extension misleading conclusions. This study summarises the main contributions in the field of ALB that focus on the learning effect. First, assembly lines (ALs) and ALB problems are characterised. Next, the importance of the learning effect in ALB is highlighted, and the main learning curve (LC) models are introduced. Finally, an exhaustive review of the main contributions in the field of ALB and learning effect is provided. The results highlight that many problems in this area need to be investigated further, in relation to both conceptual model building and the development of algorithms for solving practical size problems.

Comparison of MILP and CP models for balancing partially automated assembly lines

AbstractThe objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the field focused on solving the Simple Assembly Line Balancing problem, a basic simplified version of the general problem. As the production environment became more complex, several new ALB problem types appeared, and almost all ALB problems are NP-hard, meaning that finding a solution requires a lot of time, resources, and computational power. Methods with custom-made algorithms and generic approaches have been developed for solving these problems. While custom-made algorithms are generally more efficient, generic approaches can be more easily extended to cover other variations of the problem. Over the past few decades, automation has played an increasingly important role in various operations, although complete automation is often not possible. As a result, there is a growing need for partially automated assembly line balancing models. In these circumstances, the flexibility of a generic approach is essential. This paper compares two generic approaches: mixed integer linear programming (MILP) and constraint programming (CP), for two types of partially automated assembly line balancing problems. While CP is relatively slower in solving the simpler allocation problems, it is more efficient than MILP when an increased number of constraints is applied to the ALB and an allocation and scheduling problem needs to be solved.

Mixed-model assembly line balancing problem considering learning effect and uncertain demand

An assembly line system is a manufacturing process in which parts are added in sequence from workstation to workstation until the final assembly is produced. In a mixed-model assembly line balancing problem, tasks belonging to different product models, are allocated to workstations according to their processing times and precedence relationships amongst tasks. The research parlayed two features, learning effect and uncertain demand, into the conventional mixed-model assembly line balancing model, which is the main contribution of our paper. Both features can affect the new decision appeared in the stated problem — the level of production. The problem setup as well as the new decisions considered in the problem are novel. The proposed model optimized two objectives, total expected cost and average cycle time. To solve the model, a mixed integer-based heuristic and a customized variable neighborhood search method is proposed. The algorithms are examined for two different system response time requirements. Computational results showed that the mixed integer-based heuristic is more efficient if there is enough response time for the decision making process. On the contrary, the customized variable neighborhood search method can deliver promising results under real-time conditions. The Pareto-optimal set can be generated, which provides the managers with multiple choices for different cost and cycle time combinations.

A bi‐objective ergonomic assembly line balancing model with conic scalarization method

AbstractThe most important factor affecting efficiency and ergonomic risk levels in an assembly line design is the problem of assigning certain tasks to certain stations, namely the assembly line balancing problem. In the literature, assembly line balancing problem has often been studied, but studies that consider ergonomic risks are deficient. Recently, it has been one of the issues that have started to attract great attention with the realization of health problems caused by assembly lines. To this end, in this study, a bi‐objective mathematical model is developed that considers balancing assembly line station time and ergonomic risk levels, simultaneously. It is aimed to minimize both station time and the total deviations of ergonomic risk scores for the stations. Weighted sum and conic scalarization methods are applied to solve the bi‐objective model. To analyze the outcomes of the developed model, an application is proposed and tested on a real industrial case, at a home appliance assembly line. The deployment of the OMAX method is a contribution to the literature since it shows an analysis tool which evaluates the results of assembly line balancing. This method evaluates the performance of the stations based on different criteria such as station time and ergonomic risk. The number of high‐risk stations is obtained as 13 in the single‐objective model aiming to minimize the station time, while it is found to be nine in the bi‐objective model solved with CSM, without an increase in the total number of stations.

An analysis of task assignment and cycle times when robots are added to human-operated assembly lines, using mathematical programming models

Adding robots to a human-operated assembly line influences both the short- and long-term operation of the line. However, the effects of robots on assembly line capacity and on cycle time can only be studied if appropriate task assignment models are available. This paper shows how traditional assembly line balancing models can be changed in order to determine the optimal number of workstations and cycle time when robots with different technological capabilities are able to perform a predetermined set of tasks. The mathematical programming models for the following three cases are presented and analysed: i) only workers are assigned to the workstations; ii) either a worker or a robot is assigned to a workstation; iii) a robot and a worker are also assigned to specific workstations. The data of an assembly line producing power inverters is used to illustrate the proposed calculations. Both the assignment of tasks and the changes of cycle time are analysed within the AIMMS modelling environment. The computational characteristics of the proposed mathematical programming models are also examined and tested using benchmark problems. The models presented in this paper can assist operations management in making decisions relating to assembly line configuration.

Consideration of workforce differences in assembly line balancing and worker assignment problem

Abstract Workforce differences in terms of skill, age, gender and physical measures have a large impact on production systems performance. Moreover, the role of differences between workers can be particularly seen in assembly lines, where a vast range of tasks are performed manually, and workers are involved widely. In this work, we introduce a new assembly line balancing and worker assignment model with consideration of workers’ diversity in terms of both workers’ expertise and perceived physical effort to implement an age-inclusive workforce. Here, a new Worker Task Categorization Matrix is introduced to assure the complete involvement of the individual during the balancing process. Following, a bi-objective linear programming model is proposed and solved by using the ϵ -constraint approach. Finally, we test and validate the model through a real-case application.

Multi-objective optimization of mixed model assembly line balancing in an assemble-to-order industry with stochastic environment

The objective of this research is to propose a methodology for multi-objective optimization of a mixed-model assembly line balancing problem with the stochastic environment. To do this a mathematical model representing the problems at hand is developed with objectives of minimizing cycle time and minimization of the number of workstations (which is of Type-E ALB problem). And two optimization meta-heuristics are considered to solve it, namely, Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) and Multi-Objective Genetic Algorithm (MOGA). To test the performance of the algorithms three different size standard problems in Assemble-to-order types of industry are taken and five demand arrival scenarios are considered to incorporate the stochastic nature of the demand arrival for each model in all problems. Both the algorithms are coded and run using MATLAB® 2013a and are compared based on different performance measures. The results indicated that MOGA outperformed NSGA-II in most of the test problems. Nevertheless, both algorithms have resulted in significant improvements in the performance measures in Assemble-to-order types of industry dataset compared to the existing line configuration.
 Keywords: Assembly Line, Multi-objective optimization, Single model, mixed-model, stochastic environment, Genetic Algorithm

An Optimization Model for Assembly Line Balancing Problem with Uncertain Cycle Time

With the drastic change in the market, the assembly line is susceptible to some uncertainties. This study introduces the uncertain cycle time to the assembly line balancing problem (ALBP) and explores its impact. Firstly, we improve the traditional precedence graph to express the precedence, spatial, and incompatible constraints between assembly tasks, which makes ALBP more realistic. Secondly, we establish the assembly line balancing model under an uncertain cycle time, which is defined as an interval whose size can be adjusted according to the level of uncertainty. The objective of the model was to minimize the number of stations and the cycle time. Thirdly, we integrate the operator’s skill level into the model, and a multipopulation genetic algorithm is used to solve it. The method proposed in this study is verified by several test problems of different sizes. The results show that when the cycle time is uncertain, the proposed method can be used to obtain more reasonable results.

Optimization of a Customized Mixed Model Assembly Using MATLAB/Simulink

The relevance of assembly line balancing has never been more critical as it is now with the advent of Industry 4.0. This paper considers the case study of a Mixed Model Assembly Line in the form of a water bottling plant. The paper initially discusses the design consideration of the water bottling plant and how it was modelled in Simulink. Next the paper chooses an appropriate optimization technique and runs it on the model with real-time inputs. Finally, an analysis is done to test the impact of optimization and if it has improved the production time of the water bottling plant. The results of the study aim to add knowledge to the field of Mixed Model Assembly line balancing.

Comparative analysis and optimization of Mixed Model assembly line using Genetic Algorithm

Line balancing aims at arrangement of different facilities like workstation, machines, tool, and operator in an adequate manner in order to obtain most efficient balancing of assembly line, its capabilities, flow of product and services. Various methods are proposed in the literature to optimize these parameters hence this research effort starts with the review of the various methods developed and to compare main features of few of them for their relative assets and limitation to allow choose the most suitable method for optimization of line balancing. The researchers used the average times of the tasks of the models as the timings of the tasks of the combined assembly line model while designing the assembly line which is an approximate method of dealing with the data. Hence, in this paper, the design of the assembly line of the models is done simultaneously for all the models using their original task times. Such an attempt gives a realistic design of the mixed-model assembly line using genetic algorithm for the balancing of this mixed model assembly line problem. A new methodology of balancing the assembly line has been adopted and the output is compared with conventional methods of line balancing. The optimization of assembly line using cyclic multipoint crossover method of genetic algorithm has been performed to obtained real offspring and the best chromosome is selected to find out the efficiency of assembly line. The superiority of this approach is demonstrated using a numerical example. The multi-objective genetic algorithm with cyclic crossover and with individual task times of the models is a unique contribution. Although the RPW method also gives better results other than any conventional methods of line balancing however when the population size (assembly lines) of the system increase and number of workstation are more than genetic algorithm method is suitable and gives better result and this research paper presents the comparative analysis of mixed model assembly line balancing methods and tries to find out the best possible solution in order to minimize no. of workstation, ideal time, bottleneck and increase the efficiency of plant

Model for Assembly Line Re-Balancing Considering Additional Capacity and Outsourcing to Face Demand Fluctuations

The most critical stage in a garment industry is sewing process, because generally, it consists of a number of operations and a large number of sewing machines for each operation. Therefore, it requires a balancing method that can assign task to work station with balance workloads. Many studies on assembly line balancing assume a new assembly line, but in reality, due to demand fluctuation and demand increased a re-balancing is needed. To cope with those fluctuating demand changes, additional capacity can be carried out by investing in spare sewing machine and paying for sewing service through outsourcing. This study develops an assembly line balancing (ALB) model on existing line to cope with fluctuating demand change. Capacity redesign is decided if the fluctuation demand exceeds the available capacity through a combination of making investment on new machines and outsourcing while considering for minimizing the cost of idle capacity in the future. The objective of the model is to minimize the total cost of the line assembly that consists of operating costs, machine cost, adding capacity cost, losses cost due to idle capacity and outsourcing costs. The model develop is based on an integer programming model. The model is tested for a set of data of one year demand with the existing number of sewing machines of 41 units. The result shows that additional maximum capacity up to 76 units of machine required when there is an increase of 60% of the average demand, at the equal cost parameters..

Maximizing comfort in Assembly Lines with temporal, spatial and ergonomic attributes

We aim at maximizing the comfort of operators in mixed-model assembly lines. To achieve this goal, we evaluate two assembly line balancing models: the first that minimizes the maximum ergonomic risk and the second one that minimizes the average absolute deviations of ergonomic risk. Through a case study we compare the results of the two models by two different resolution procedures: the Mixed Integer Linear Programming (MILP) and Greedy Randomized Adaptive Search Procedures (GRASP). Although linear programming offers best solution, the results given by GRASPs are competitive.

The joint line balancing and material supply problem

In production systems of automobile manufacturers multi-variant products are assembled on paced mixed-model assembly lines, which have to be supplied by a lean in-house logistic with containers of different sizes, alternative supply policies and different number of parts. The assignment of operations to workplaces within the assembly line balancing problem and the assignment of containers to supply policies within the material supply problem determine the operational cost of a work system. Up to now in the literature and real-world both problems are solved successively.We analyze the interdependence of the line balancing and material supply problem in depth and reveal potential productivity gains through simultaneous planning. We set up a practice-oriented assembly line balancing model, which is extended to cover several important logistical constraints of the material supply problem, and solved it with a flexible heuristic on the general assembly line balancing problem, we developed in an earlier paper. In a computational experiment, we determine cost savings through simultaneous planning and show the applicability of our approach within a practice-oriented experiment.

Formulation of workforce skill constraints in assembly line balancing models

The objective of assembly line balancing (ALB) is to minimize the number of workstations organized to perform tasks with precedence constraints. An important element of proper assignment of tasks to workstations is the consideration of skill requirement of tasks, and skill level of workers. Some tasks may require special skills, some workers might not be able to perform complicated tasks, and some workers might be specialized for specific tasks. This paper provides a general framework to model skill requirements and skill conditions for assembly line balancing models. Three types of skill constraints are defined. Low skill constraints determine workstations for workers who are able to perform only some simple tasks. High skill constraints consider tasks which require higher than average skills of workers. Finally, exclusive skill constraints consider situations where a group of workers is specialized in a subset of tasks. The paper summarizes the mathematical description of the different skill constraints, and shows how simple assembly line balancing models can be completed with skill considerations. The effect of skill constraints on the optimal solution is illustrated with a sample problem.

Application of the results of simple assembly line balancing models in practice: the case of a bicycle manufacturer

The objective of assembly line balancing (ALB) is the allocation of tasks to workstations of continuous production lines in order to minimise operating cost or other cycle time-related management criteria. As a consequence of the development of computer technology, mathematical programming models of practical-sized ALB problems can be solved by commercial optimisation software very efficiently. Therefore, the focus of research in ALB can be shifted to practice-driven model formulation and to the investigation of new areas of application. The objective of this paper is to show how cycle-time related operations management decisions can be supported with quick solution of ALB models through the case of a bicycle assembly process. Practical ALB problems related to workforce skill and to production quantity changes are presented and management implications of the results are explained.

Balancing and scheduling of flexible mixed model assembly lines

Mixed model assembly line literature involves two problems: balancing and model sequencing. The general tendency in current studies is to deal with these problems in different time frames. However, in today's competitive market, the mixed model assembly line balancing problem has been turned into an operational problem. In this paper, we propose mixed integer programming (MIP) and constraint programming (CP) models which consider both balancing and model sequencing within the same formulation along with the optimal schedule of tasks at a station. Furthermore, we also compare the proposed exact models with decomposition schemes developed for solving different instances of varying sizes. This is the first paper in the literature which takes into account the network type precedence diagrams and limited buffer capacities between stations. Besides, it is the first study that CP method is applied to balancing and scheduling of mixed model assembly lines. Our empirical study shows that the CP approach outperforms the MIP approach as well as the decomposition schemes.

Formulation of Multi-level Workforce Skill Constraints in Assembly Line Balancing Models

The objective of assembly line balancing (ALB) is to assign tasks to workstations organized to perform tasks with precedence constraints. An important element of proper assignment of tasks to workstations is the consideration of skill requirement of tasks, and skill level of workers. This paper provides a general framework to model skill requirements and skill conditions for assembly line balancing models. Three types of skill constraints are defined. Low skill constraints determine workstations for workers who are able to perform only some simple tasks. High skill constraints consider tasks which require higher than average skills of workers. Finally, exclusive skill constraints consider situations where a group of workers is specialized in a subset of tasks. The paper summarizes the mathematical description of the different skill constraints, and shows how simple assembly line balancing models can be completed with skill considerations.

Teaching Note—Implementing Line Balancing Heuristics in Spreadsheets

Two previous papers in INFORMS Transactions on Education demonstrated an innovative Excel array formula approach that can be used for the handling of precedences in project management and assembly line balancing models. In this paper I combine the array formula approach with a clever but simple precedence coding system to present an efficient spreadsheet for performing assembly line balancing using the heuristic method that is common in operations management textbooks. In addition, I demonstrate to students a perturbation method to handle ties in algorithms in Excel and also demonstrate the application of Excel's Scenario Manager to line balancing.

Fuzzy goal programming approach to multi-objective mixed model assembly line balancing

This study addresses mixed model assembly line balancing problems arising in automotive plants. The main motivation for balancing a mixed model assembly line is to avoid inefficiencies among the stations. There exist many sources for these inefficiencies such as idle workers, extra stations, unbalanced workloads and unnecessary equipments along the line. All these factors can be involved in a multi-objective approach. However, the decision maker cannot determine precise target values for abovementioned objectives. Thus, fuzzy goal constraints are much more suitable for the problem solution. The problem stated above is formulated in this study as a mixed integer fuzzy-goal programming with the objectives of minimising number of workers and stations, minimising total unbalanced workloads and minimising total equipment costs. The problem is then linearised using Min-Max goal programming approach to solve mixed model assembly line balancing with above given objectives. This study includes a case study and determines the priority of each objective based on the judgments of the assembly line manager.