Line Balancing Model Research Articles

An efficient multi-objective adaptive large neighborhood search algorithm for solving a disassembly line balancing model considering idle rate, smoothness, labor cost, and energy consumption

The concept of green manufacturing emphasizes the importance of product disassembly in achieving energy-efficient recycling and remanufacturing operations. Disassembly line balancing (DLB) is a critical component of the product disassembly process, wherein a set of tasks must be allocated to workstations for disassembly. This study proposes a multi-objective DLB model that aims to minimize multiple conflicting objectives simultaneously including idle rate, smoothness, labor cost, and energy consumption. A key innovation of this study involves the creation of a tailored adaptive large neighborhood search (ALNS) algorithm which is one of the first studies in the literature on product disassembly algorithms. The developed ALNS employs efficient construction and destruction heuristics to solve the proposed multi-objective DLB problem. The ALNS algorithm aims to destroy and repair solutions effectively, while a local search procedure helps it escape from local optimum solutions. The provided DLB model is effectively solved by applying the proposed ALNS algorithm to the disassembly process of a turbine reducer. The obtained results from this application serve as a compelling demonstration of the efficiency and effectiveness of the proposed approach. Furthermore, comparisons conducted with various state-of-the-art algorithms using small and large instance sets consistently highlight the superiority of the proposed ALNS approach.

Linear Disassembly Line Balancing Problem with Tool Deterioration and Solution by Discrete Migratory Bird Optimizer

In recent years, the global resource shortage has become a serious issue. Recycling end-of-life (EOL) products is conducive to resource reuse and circular economy and can mitigate the resource shortage issue. The disassembly of EOL products is the first step for resource reuse. Disassembly activities need tools, and tool deterioration occurs inevitably during the disassembly process. This work studies the influence of tool deterioration on disassembly efficiency. A disassembly line balancing model with the goal of maximizing disassembly profits is established, in which tool selection and assignment is a critical part. A modified discrete migratory bird optimizer is proposed to solve optimization problems. The well-known IBM CPLEX optimizer is used to verify the correctness of the model. Six real-world products are used for disassembly experiments. The popular fruit fly optimization algorithm, whale optimization algorithm and salp swarm algorithm are used for search performance comparison. The results show that the discrete migratory bird optimizer outperforms all three other algorithms in all disassembly instances.

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

The 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.

Assessment of Man and Method Toward the Assembly Line Improvement in Automotive Manufacturing

This research deal with root cause analysis of using Lean Manufacturing principle for assembly line at X manufacturing automotive company in Malaysia. This research aims to improve the process and manpower allocation in the T-Model assembly line and to increase the productivity efficiency of Man and Method toward production output. Movement waste and waiting waste were subjects that were studied in this research. Data was collected through the interview process, observation, photos and calculating the current total production process time related to the assembly process. Process flow in the actual layout, process setup and simulation of the existing design using Witness Software were used to determine the section or method with the highest idle percentage or setup time. The witness Software was also used to simulate the improved layout, and the improvement then is suggested. In conclusion, the entire production process is presented, which initially offered very low productivity. With the line balancing and discrete event simulation model combination, productivity is increased from 3 units per shift initially to 6 units per shift manufactured product at the end. 40% increase in the production output. The proposed layout and practice are done, and the improvement was highlighted at an assembly line.

Multi-Objective Discrete Brainstorming Optimizer to Solve the Stochastic Multiple-Product Robotic Disassembly Line Balancing Problem Subject to Disassembly Failures

Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) product disassembly efficiency. Most of the current research on disassembly line balancing problems focuses on decomposing one product. More than one product can be disassembled concurrently, which can further improve the efficiency. Moreover, uncertainty such as the depreciation of EOL products, may result in disassembly failure. In this research, a stochastic multi-product robotic disassembly line balancing model is established using an AND/OR graph. It takes the precedence relationship, cycle constraint, and disassembly failure into consideration to maximize the profit and minimize the energy consumption for disassembling multiple products. A Pareto-improved multi-objective brainstorming optimization algorithm combined with stochastic simulation is proposed to solve the problem. Furthermore, by conducting experiments on some real cases and comparing with four state-of-the-art multi-objective optimization algorithms, i.e., the multi-objective discrete gray wolf optimizer, artificial bee colony algorithm, nondominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition, this paper validates its excellent performance in solving the concerned problem.

Bi-objective optimization-based multi-criteria decision-making framework for disassembly line balancing and employee assignment problem

PurposeAlthough disassembly balancing lines has been studied for over two decades, there is a gap in the robotic disassembly. Moreover, combination of problem with heterogeneous employee assignment is also lacking. The hazard related with the tasks performed on disassembly lines on workers can be reduced by the use of robots or collaborative robots (cobots) instead of workers. This situation causes an increase in costs. The purpose of the study is to propose a novel version of the problem and to solve this bi-objective (minimizing cost and minimizing hazard simultaneously) problem.Design/methodology/approachThe epsilon constraint method was used to solve the bi-objective model. Entropy-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization methods for Enrichment Evaluation (PROMETHEE) methods were used to support the decision-maker. In addition, a new criterion called automation rate was proposed. The effects of factors were investigated with full factor experiment design.FindingsThe effects of all factors were found statistically significant on the solution time. The combined effect of the number of tasks and number of workers was also found to be statistically significant.Originality/valueIn this study, for the first time in the literature, a disassembly line balancing and employee assignment model was proposed in the presence of heterogeneous workers, robots and cobots to simultaneously minimize the hazard to the worker and cost.

Discrete Bat Optimizer for Disassembly Line Balancing Problem

The recycling of end-of-life (EOL) products is the primary link in the remanufacturing process. EOL products rely on disassembly lines to retain valuable parts for remanufacturing. In this work, a disassembly line balancing model is established based on an AND/OR graph. It takes precedence relation, cycle time restriction, failure risk, and time uncertainty into consideration and aims to maximize the dismantling profit and minimize the energy consumption. Then, a multi-objective discrete bat optimizer based on Pareto rules is designed according to the problem model, and a precedence preserving crossover operator, a single point mutation operator and a 2-optimization operator are used to simulate the flight strategy of bats to satisfy the search of feasible solutions. To speed up the convergence, we propose an elite strategy to maintain the non-dominate solutions in the external files. By decomposing products of different sizes and analyzing the experimental results, the proposed algorithm is evaluated with the existing multi-objective discrete gray wolf optimizer, artificial bee colony optimizer, non-dominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition. The effectiveness of the proposed algorithm in solving this problem is verified.

Discrete Bat Optimizer for Disassembly Line Balancing Problem

The recycling of end-of-life (EOL) products is the primary link in the remanufacturing process. EOL products rely on disassembly lines to retain valuable parts for remanufacturing. In this work, a disassembly line balancing model is established based on an AND/OR graph. It takes precedence relation, cycle time restriction, failure risk, and time uncertainty into consideration and aims to maximize the dismantling profit and minimize the energy consumption. Then, a multi-objective discrete bat optimizer based on Pareto rules is designed according to the problem model, and a precedence preserving crossover operator, a single point mutation operator and a 2-optimization operator are used to simulate the flight strategy of bats to satisfy the search of feasible solutions. To speed up the convergence, we propose an elite strategy to maintain the non-dominate solutions in the external files. By decomposing products of different sizes and analyzing the experimental results, the proposed algorithm is evaluated with the existing multi-objective discrete gray wolf optimizer, artificial bee colony optimizer, non-dominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition. The effectiveness of the proposed algorithm in solving this problem is verified.

Robotic disassembly line balancing and sequencing problem considering energy-saving and high-profit for waste household appliances

Waste household appliances contain hazardous substances and valuable components. The timely disposal of these waste products can avoid environmental pollution and improve the recycling rate of resources. Robotic disassembly can realize efficient and precise disassembly to achieve sustainable and clean production. Reasonable allocation of robots and disassembly tasks is the key to achieving efficient production. Reducing energy consumption and improving profits are the focus of the government and enterprises. The tool replacement of the robot needs to be considered in the actual disassembly process, which increases the difficulty of the problem. This study establishes a multi-objective robotic disassembly line balancing and sequencing model to quantify the problem. The model aims to shorten the disassembly completion time and perform disassembly operations with energy-saving and high-profit. An improved genetic simulated annealing algorithm based on problem characteristics is designed. The superiority of the proposed algorithm is verified by solving 21 benchmarks with the number of tasks ranging from 7 to 148 and comparing with five algorithms. In a disassembly case of waste household appliances represented by a microwave oven, 66 disassembly schemes are obtained for decision makers to choose from different emphases. The results show that these schemes can not only achieve efficient and green disassembling but also obtain higher disassembly profits.

Modeling and Balancing for Disassembly Lines Considering Workers With Different Efficiencies.

To achieve sustainable manufacturing of large-scale end-of-life products, disassembly for recycling and remanufacturing has been widely adopted by industries. Disassembly line balancing becomes an important and challenging issue. The disassembly efficiencies of workers are different in the actual disassembly line due to some factors, including disassembly environment, skill level, work enthusiasm, etc. However, efficiency differences are often ignored in previous studies, which ultimately lead to unbalanced workloads among stations. Therefore, this article establishes a disassembly line balancing model that considers workers with different efficiencies and introduces the bucket brigade model into the disassembly line. Its optimization objectives include workload smoothness, cost of workers, disassembly risk, and disassembly demand. To obtain high-quality solutions, a discrete flower pollination algorithm based on problem characteristics is proposed. The performance of the proposed algorithm is verified by comparing it with 11 algorithms. Finally, the proposed model and algorithm are applied to an actual television disassembly case considering workers with different efficiencies, and provide decision makers with multiple disassembly schemes.

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.

A risk-averse two-stage stochastic programming model for a joint multi-item capacitated line balancing and lot-sizing problem

In this paper, a comprehensive production planning problem under uncertain demand is investigated. The problem intertwines two NP-hard optimization problems: an assembly line balancing problem and a capacitated lot-sizing problem. The problem is modelled as a two-stage stochastic program assuming a risk-averse decision maker. Efficient solution procedures are proposed for tackling the problem. A case study related to mask production is presented. Several insights are provided stemming from the COVID-19 pandemic. Finally, the results of a series of computational tests are reported.

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.

C-ALB (Collaborative Assembly Line Balancing): a new approach in cobot solutions

Collaborative robots can be a proper solution to improve the throughput of manual systems without reducing their flexibility. To effectively use cobots in productive systems, it is fundamental to develop a suitable task allocation model that considers collaboration. Hence, we present a model for collaborative assembly line balancing (C-ALB) which considers paralleling tasks and collaboration in the balancing resolution. Indexes that take into account both the product and process characteristics are defined to evaluate the quality of the proposed task allocation model and comparing it to others. The results confirm the influence of the product characteristics on the system performance, leading to the definition of a new paradigm for product design.

A genetic simulated annealing algorithm for parallel partial disassembly line balancing problem

The timely recovery and disassembly of waste electrical and electronic equipment (WEEE) can not only obtain a higher economic benefit but also can reduce the impact of hazardous substances on the environment. The parallel disassembly line can disassemble different kinds of WEEE synchronously and improve disassembly efficiency. Therefore, a parallel partial disassembly line balancing model with stochastic disassembly time is established in this paper. The evaluation indexes of the disassembly line include the number of workstations, workload smoothness, and disassembly profits. A new genetic simulated annealing algorithm is proposed to optimize the model. The encoding and decoding strategies are constructed according to the characteristics of partial disassembly and parallel layout. Two-point mapping crossover and single-point insertion mutation operations are designed to ensure that the disassembly sequence meets the precedence constraints and disassembly constraints. The simulated annealing operation is applied to the results of the genetic operation. The proposed algorithm obtains better solutions than the tabu search algorithm in stochastic parallel assembly line balancing problems, and the proposed algorithm has better performance than the CPLEX solver, genetic algorithm, and simulated annealing in parallel disassembly line balancing problems. Finally, a parallel partial disassembly line for waste televisions and refrigerators is constructed, and the performance of the proposed multi-objective algorithm is superior to those of five classical multi-objective algorithms. The results show that the proposed model has a better practical application ability and that the proposed algorithm can improve the performance of disassembly lines.

Optimisation of mixed-model assembly line balancing problem under uncertain demand

Assembly lines play a crucial role in determining the profitability of a company. Market conditions have increased the importance of mixed-model assembly lines. Variations in the demand are frequent in real industrial environments and often leads to failure of the mixed-model assembly line balancing scheme. Decision makers have to take into account this uncertainty. In an assembly line balancing problem, there is a massive amount of research in the literature assuming deterministic environment, and many other works consider uncertain task times. This research utilises the uncertainty theory to model uncertain demand and introduces complexity theory to measure the uncertainty of assembly lines. Scenario probability and triangular fuzzy number are used to describe the uncertain demand. The station complexity was measured based on information entropy and fuzzy entropy to assist in balancing systems with robust performances, considering the influence of multi-model products in the station on the assembly line. Taking minimum station complexity, minimum workload difference within station, maximum productivity as objective functions, a new optimization model for mixed-model assembly line balancing under uncertain demand was established. Then an improved genetic algorithm was applied to solve the model. Finally, the effectiveness of the model was verified by several instances of mixed-model assembly line for automobile engine.

Provision model for line balancing in multi-echelon multi-product continuous cement production

This study investigated model for line balancing in multi eschelon and multi product. A cement company which have 4 integrated plant consist of multi-eschelon 4 rawmill, 4 kiln, 9 finish mill with multi-product OPC and PCC. This paper proposed production cement plant mathematical model using mixed integer linear programming (MILP). The main objective of the model is minimize cost by changing decision variable production rate (ton/hour) and product allocation route. Constraint is demand fulfillment of multi product, inventory and production balancing, inventory and production min/max production. Furthermore, the improvement recommendation also will be declared in order to reduce production cost.The implication practice of proposed model of line balancing in multi-eschelon multi product continuous production is production manager could develop production planning and manage production rate in order to reduce production cost during multi period.

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.