An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem

The consideration of this paper is given to address the straight and U-shaped assembly line balancing problem. Although many attempts in the literature have been made to develop deterministic version of the assembly line model, the attention is not considerably given to those in uncertain environment. In this paper, a novel bi-objective fuzzy mixed-integer linear programming model (BOFMILP) is developed so that triangular fuzzy numbers (TFNs) are employed in order to represent uncertainty and vagueness associated with the task processing times in the real production systems. In this proposed model, two conflicting objectives (minimizing the number of stations as well as cycle time) are considered simultaneously with respect to set of constraints. For this purpose, an appropriate strategy in which new two-phase interactive fuzzy programming approach is proposed as a solution method to find an efficient compromise solution. Finally, validity of the proposed model as well as its solution approach are evaluated though numerical examples. In addition, a comparison study is conducted over some test problems in order to assess the performance of the proposed solution approach. The results demonstrate that our proposed interactive fuzzy approach not only can be applied in ALBPs but also is capable to handle any practical MOLP models. Moreover, in light of these results, the proposed model may constitute a framework aiming to assist the decision maker (DM) to deal with uncertainty in assembly line problem.     

Smart control of the assembly process with a fuzzy control system in the context of Industry 4.0

Assembly line balancing is important for the efficiency of the assembly process, however, a wide range of disruptions can break the current workload balance. Some researchers explored the task assignment plan for the assembly line balancing problem with the assumption that the assembly process is smooth with no disruption. Other researchers considered the impacts of disruptions, but they only explored the task re-assignment solutions for the assembly line re-balancing problem with the assumption that the re-balancing decision has been made already. There is limited literature exploring on-line adjustment solutions (layout adjustment and production rate adjustment) for an assembly line in a dynamic environment. This is because real-time monitoring of an assembly process was impossible in the past, and it is difficult to incorporate uncertainty factors into the balancing process because of the randomness and non-linearity of these factors. However, Industry 4.0 breaks the information barriers between different parts of an assembly line, since smart, connected products, which are enabled by advanced information and communication technology, can intelligently interact and communicate with each other and collect, process and produce information. Smart control of an assembly line becomes possible with the large amounts of real-time production data in the era of Industry 4.0, but there is little literature considering this new context. In this study, a fuzzy control system is developed to analyze the real-time information of an assembly line, with two types of fuzzy controllers in the fuzzy system. Type 1 fuzzy controller is used to determine whether the assembly line should be re-balanced to satisfy the demand, and type 2 fuzzy controller is used to adjust the production rate of each workstation in time to eliminate blockage and starvation, and increase the utilization of machines. Compared with three assembly lines without the proposed fuzzy control system, the assembly line with the fuzzy control system performs better, in terms of blockage ratio, starvation ratio and buffer level. Additionally, with the improvement of information transparency, the performance of an assembly line will be better. The research findings shed light on the smart control of the assembly process, and provide insights into the impacts of Industry 4.0 on assembly line balancing.     

Multi-objective fuzzy assembly line balancing using genetic algorithms

This paper presents a fuzzy extension of the simple assembly line balancing problem of type 2 (SALBP-2) with fuzzy job processing times since uncertainty, variability, and imprecision are often occurred in real-world production systems. The jobs processing times are formulated by triangular fuzzy membership functions. The total fuzzy cost function is formulated as the weighted-sum of two bi-criteria fuzzy objectives: (a) Minimizing the fuzzy cycle time and the fuzzy smoothness index of the workload of the line. (b) Minimizing the fuzzy cycle time of the line and the fuzzy balance delay time of the workstations. A new multi-objective genetic algorithm is applied to solve the problem whose performance is studied and discussed over known test problems taken from the open literature.     

Comparative Corrigendum note on papers “Fuzzy adaptive GA for multi-objective assembly line balancing” continued “Modified GA for different types of assembly line balancing with fuzzy processing times”: differences and similarities [Appl. Soft Comput. 34 (September 2015) 655–677]

In the very few recently published paper by Alavidoost et al. [1], they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2], as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption.     

Assembly line balancing under uncertainty: Robust optimization models and exact solution method

This research deals with line balancing under uncertainty and presents two robust optimization models. Interval uncertainty for operation times was assumed. The methods proposed generate line designs that are protected against this type of disruptions. A decomposition based algorithm was developed and combined with enhancement strategies to solve optimally large scale instances. The efficiency of this algorithm was tested and the experimental results were presented. The theoretical contribution of this paper lies in the novel models proposed and the decomposition based exact algorithm developed. Moreover, it is of practical interest since the production rate of the assembly lines designed with our algorithm will be more reliable as uncertainty is incorporated. Furthermore, this is a pioneering work on robust assembly line balancing and should serve as the basis for a decision support system on this subject.     

Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models

Two-sided assembly lines are especially used at the assembly of large-sized products, such as trucks and buses. In this type of a production line, both sides of the line are used in parallel. In practice, it may be necessary to optimize more than one conflicting objectives simultaneously to obtain effective and realistic solutions. This paper presents a mathematical model, a pre-emptive goal programming model for precise goals and a fuzzy goal programming model for imprecise goals for two-sided assembly line balancing. The mathematical model minimizes the number of mated-stations as the primary objective and it minimizes the number of stations as a secondary objective for a given cycle time. The zoning constraints are also considered in this model, and a set of test problems taken from literature is solved. The proposed goal programming models are the first multiple-criteria decision-making approaches for two-sided assembly line balancing problem with multiple objectives. The number of mated-stations, cycle time and the number of tasks assigned per station are considered as goals. An example problem is solved and a computational study is conducted to illustrate the flexibility and the efficiency of the proposed goal programming models. Based on the decision maker's preferences, the proposed models are capable of improving the value of goals.     

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.     

A meta-heuristic algorithm for the fuzzy assembly line balancing type-E problem

Consideration is given to a single-model assembly line balancing problem with fuzzy task processing times. The problem referred to herein as f-SALBP-E consists of finding a combination of the number of workstations and the cycle time as well as a respective line balance such that the efficiency of the line is maximized. f-SALBP-E is an extension of the classical SALBP-E under fuzziness. First, a formulation of the problem is given with the tasks processing times presented by triangular fuzzy membership functions. Then, since the problem is known to be NP-hard, a meta-heuristic based on a Genetic Algorithm (GA) is developed for its solution. The performance of the proposed solution approach is studied and discussed over multiple benchmarks test problems taken from the open literature. The results demonstrate very satisfactory performance for the developed approach in terms of both solution time and quality.     

Multi-rule Multi-objective Simulated Annealing Algorithm for Straight and U Type Assembly Line Balancing Problems

The task of balancing of assembly lines is of considerable industrial importance. It consists of assigning operations to workstations in a production line in such a way that (1) no assembly precedence constraint is violated, (2) no workstations in the line takes longer than a predefined cycle time to perform all tasks assigned to it, and (3) as few workstations as possible are needed to perform all the tasks in the set. This paper presents a new multiple objective simulated annealing (SA) algorithm for simple (line) and U type assembly line balancing problems with the aim of maximizing “smoothness index” and maximizing the “line performance” (or minimizing the number of workstations). The proposed algorithm makes use of task assignment rules in constructing feasible solutions. The proposed algorithm is tested and compared with literature test problems. The proposed algorithm found the optimal solutions for each problem in short computational times. A detailed performance analysis of the selected task assignment rules is also given in the paper.     

Mixed-model assembly line balancing using a multi-objective ant colony optimization approach

Mixed-model assembly lines are production systems at which two or more models are assembled sequentially at the same line. For optimal productivity and efficiency, during the design of these lines, the work to be done at stations must be well balanced satisfying the constraints such as time, space and location. This paper deals with the mixed-model assembly line balancing problem (MALBP). The most common objective for this problem is to minimize the number of stations for a given cycle time. However, the problem of capacity utilization and the discrepancies among station times due to operation time variations are of design concerns together with the number of stations, the line efficiency and the smooth production. A multi-objective ant colony optimization (MOACO) algorithm is proposed here to solve this problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved. The results show that the MOACO algorithm is an efficient and effective algorithm which gives better results than other methods compared.     

Accumulated risk of body postures in assembly line balancing problem and modeling through a multi-criteria fuzzy-genetic algorithm

Monotonous body postures during repetitive jobs negatively affect assembly-line workers with the developing of Work-related Musculoskeletal Disorders (WMSDs). Ergonomics specialists have offered auxiliary posture diversity to deal with the lack of varieties, especially for high-risk ones. Meanwhile, Assembly Line Balancing (ALB) problem has been recognized as a prior thinking to (re)configure assembly lines via the balancing of their tasks among their workstations. Some conventional criteria, cycle time and overall workload are often considered during the balancing. This paper presents a novel model of ALB problem that incorporates assembly worker postures into the balancing. In addition to the conventional ALB criteria, a new criterion of posture diversity is defined and contributes to enhance the model. The proposed model suggests configurations of assembly lines via the balancing; so that the assigned workers encounter the opportunities of changing their body postures, regularly. To address uncertainties in the conventional criteria, a fuzzy goal programming is used, and an appropriate genetic algorithm is developed to deal with the model. Various computational tests are performed on the different models made with combinations of the three criteria mentioned above. Comparing the pay-offs among the combinations, results show that well balanced task allocation can be obtained through the proposed model.     

A Coevolutionary Algorithm for Balancing and Sequencing in Mixed Model Assembly Lines

A mixed model assembly line is a production line where a variety of product models are produced. Line balancing and model sequencing problems are important for an efficient use of such lines. Although the two problems are tightly interrelated with each other, prior researches have considered them separately or sequentially. This paper presents a new method using a coevolutionary algorithm that can solve the two problems at the same time. In the algorithm, it is important to promote population diversity and search efficiency. We adopt a localized interaction within and between populations, and develop methods of selecting symbiotic partners and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. Also presented are the experimental results that demonstrate the proposed algorithm is superior to existing approaches.     

A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem

To better reflect the uncertainty existing in the actual disassembly environment, the multi-objective disassembly line balancing problem with fuzzy disassembly times is investigated in this paper. First, a mathematical model of the multi-objective fuzzy disassembly line balancing problem (MFDLBP) is presented, in which task disassembly times are assumed as triangular fuzzy numbers (TFNs). Then a Pareto improved artificial fish swarm algorithm (IAFSA) is proposed to solve the problem. The proposed algorithm is inspired from the food searching behaviors of fish including prey, swarm and follow behaviors. An order crossover operator of the traditional genetic algorithm is employed in the prey stage. The Pareto optimal solutions filter mechanism is adopted to filter non-inferior solutions. The proposed model after the defuzzification is validated by the LINGO solver. And the validity and the superiority of the proposed algorithm are proved by comparing with a kind of hybrid discrete artificial bee colony (HDABC) algorithm using two test problems. Finally, the proposed algorithm is applied to a printer disassembly instance including 55 disassembly tasks, for which the computational results containing 12 non-inferior solutions further confirm the practicality of the proposed Pareto IAFSA in solving the MFDLBP.     

Mixed model U-line balancing type-1 problem: A new approach

Avoiding work overload (imbalance) in mixed model U-line production systems entails an investigation into both balancing and sequencing problems at the same time and that is why some authors have considered both planning problems simultaneously. However because of the existing differences between planning horizons of balancing and sequencing problems (the former is a long to mid-term planning problem whereas the latter has a short term planning horizon) this simultaneous approach is only practical under very special conditions. It is also known that installation of an assembly line usually needs considerable capital investments and consequently it is necessary to design and balance such a system so that it works as efficiently as possible. To do so, in this paper, we develop a new approach to balance a mixed model U-shaped production system independent of what product sequences may be. This new approach is based on minimization of crossover workstations. Due to utilization of crossover workstations, balancing mixed model assembly lines in U-shaped line layouts is more complicated than that of straight lines. Some kind of issues including the ‘model mixes’ appearing in such workstations and the time taken for an operator to move from one side of the line to another increase the complexity of mixed model U-line balancing problems (MMULBP). Therefore it seems reasonable to develop a model in which minimizing the number of crossover workstations and maximizing the line efficiency are considered at the same time. Such a model is presented in this paper. In the proposed model, minimizing the variation of workload is also considered and taking into account operator's travel times, an extra time is assigned to workload of crossover workstations. Furthermore a genetic algorithm (GA) is proposed and a number of well-known test problems are solved by the GA and the related results are illustrated. Finally, the conclusion is presented.     

Intelligent workload balance control of the assembly process considering condition-based maintenance

Balancing the workloads of workstations is key to the efficiency of an assembly line. However, the initial balance can be broken by the changing processing abilities of machines because of machine degradation, and at some point, re-balancing of the line is inevitable. Nevertheless, the impacts of unexpected events on assembly line re-balancing are always ignored. With the advanced sensor technologies and Internet of Things, the machine degradation process can be monitored continuously, and condition-based maintenance can be implemented to improve the health state of each machine. With the technology of robotic process automation, workflows of the assembly process can be smoothed and workstations can work autonomously together. A higher level of process automation can be achieved. Real-time information of the processing abilities of machines will bring new opportunities for automated workload balance via adaptive decision-making. In this study, a fuzzy control system is developed to make real-time decisions to balance the workloads based on the processing abilities of workstations, given the policy of condition-based maintenance. Fuzzy controllers are used to decide whether to re-balance the assembly line and how to adjust the production rate of each workstation. The numerical experiments show that the buffer level of the assembly line with the proposed fuzzy control system is lower than that of the assembly line without any control system and the buffer level of the assembly line with another control system is the lowest. The demand can always be satisfied by assembly lines except the one with another control system since there is too much production loss sacrificed for the low buffer level. The sensitivity analysis of the control performance to the parameter settings is also conducted. Thus, the effectiveness of the proposed fuzzy control system is demonstrated, and intelligent automation can improve the performance of the assembly process by the fuzzy control system since real-time information of the assembly line can be used for adaptive decision-making.     

Fuzzy cost-profit tradeoff model for locating a vehicle inspection station considering regional constraints

Facility location allocation （FLA） is one of the important issues in the logistics and transportation fields. In practice, since customer demands, allocations, and even locations of customers and facilities are usually changing, the FLA problem features uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy profit and cost issues of FLA. However, a decision-maker needs to reach a specific profit, minimizing the cost to target customers. To handle this issue it is essential to propose an effective fuzzy cost-profit tradeoff approach of FLA. Moreover, some regional constraints can greatly influence FLA. By taking a vehicle inspection station as a typical automotive service enterprise example, and combined with the credibility measure of fuzzy set theory, this work presents new fuzzy cost-profit tradeoff FLA models with regional constraints. A hybrid algorithm integrating fuzzy simulation and genetic algorithms （GA） is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm.     

A hybrid algorithm for allocating tasks,operators, and workstations in multi-manned assembly lines

In a car, there are approximately 30,000 parts produced by many different industries. This is due to the complexity and enormity of the automotive industry chain. The vehicle assembly process comprises welding, painting, prefabrication, and final entire-vehicle assembly. The assembly line has the largest labor force, which should be arranged and balanced to increase production efficiency and reduce labor force requirements. Unlike traditional studies on assembly line balancing problems (ALBPs), this study considers the characteristics of the automotive industry, such as multi-manned workstations, minimization in terms of the numbers of operators and workstations for streamlined production, budget constraints, the optimization of both task and operator allocation among workstations, and the determination of the start/end processing time of each task at different workstations. To address these NP-hard problems, a hybrid heuristic approach that combines the procedure of building feasible balancing solutions and the simulated annealing algorithm is proposed to map out an optimal line balancing plan for multi-manned workstations and to reduce the required workspace for shop operations. Based on the design and analysis of experiments, the effects of the maximum number of allowed operators per workstation and those of the cycle time on ALBP solutions are explored. The optimal combination of algorithm parameters is also determined. The results of this study can serve as a practical reference in planning the allocation of tasks, workstations, and operators in the industry.     

A Lagrangian relaxation technique for the general assembly line balancing problem

This paper addresses the problem of balancing assembly or fabrication lines. In order to achieve a given production rate or to optimize the use of workstations, one has to tackle the problem of balancing the production lines. It is well known that this problem belongs to the class of NP-hard problems. In this paper the polyhedron of the feasible solutions of the assembly line balancing problem is first studied. Then a Lagrangian relaxation algorithm that incorporates the set of cycle constraints in the objective function is proposed. These constraints are the complicating restrictions in the model. The relaxed problem has the interesting property that its linear programming relaxation always has integer optimal solutions. The subgradient algorithm is then used to maximize the Lagrangian dual. A heuristic is also used to find primal feasible solutions for the original line balancing integer program. These two bounds are then used to reduce the size of the branch-and-bound tree.     

Modeling and solving assembly line design problems by considering human factors with a real‐life application

Ergonomics has been playing an important role in assembly system design (ASD) that contains not only the main assembly line balancing problem but also the subassembly line balancing and assembly layout problem. The ergonomics in ASD has an impact both on productivity and on workers’ health, especially when frequent changes in the product mix occur. In this study, we propose a systematic approach in order to handle ASD, which consists of three subproblems, while considering ergonomic risk factors. The first two subproblems are solved simultaneously using the proposed rule‐based constructive search algorithm, where ergonomic risks are evaluated by OCRA method. Later, layout problem is solved under transportation constraints using local search methods with various neighborhood structures. To provide the applicability and evaluate the performance of the proposed systematic approach, a real‐life case study in a harness manufacturing company is solved and prototype productions are performed.