Human factor policy testing in the sequencing of manual mixed model assembly lines

In this paper the human resource management in manual mixed model assembly U-lines is considered. The objective is to minimise the total conveyor stoppage time to achieve the full efficiency of the line. A model, that includes effects of the human resource, was developed in order to evaluate human factor policies impact on the optimal solution of this line sequencing problem. Different human resource management policies are introduced to cope with the particular layout of the proposed line. Several examples have been proposed to investigate the effects of line dimensions on the proposed management policies. The examples have been solved through a genetic algorithm. The obtained results confirm the effectiveness of the proposed model on the performance optimisation of the line.Scope and purposeThis paper deals with the sequencing problem of a mixed model U-assembly line; in particular, it considers the minimisation of the total line stoppage time as an objective function. In order to improve the assembly line performances and to empathise the impact of the human resource in production environment, several help policies between workers have been analysed. Moreover, in the proposed model, finite values of the workers walking speed have been taken into account: in this way, their influence on the adopted help policies was evaluated. As the proposed problem was demonstrated to be NP-hard, a proper genetic algorithm was developed for its optimisation. An extensive experiment was carried out to determine a proper choice among the adopted help policies.     

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.     

A study on sequencing method for the mixed-model assembly line in just-in-time production systems

This paper deals with the problem of sequencing products on the mixed-model assembly line in a multi-level JIT production system. We consider both Goal(1) which levels the load at each station on the mixed-model assembly line, and Goal(2) which keeps a constant rate of production of every part in the preceding processes which are used on the mixed-model assembly line, together. We propose a mathematical model which determines the number of every product to be scheduled in each withdrawal position to realize making each withdrawal quantity of all the parts, with a fixed interval, as constant as possible. A sequencing method based on the solution stated above is given. Finally, an example is shown.     

Simulation of a PCB assembly line: A modified JIT approach

In this paper, a simulation model of an existing printed circuit board (PCB) assembly line is developed in an attempt to identify strategies which tend to improve the system performance. The model is based on the concept of the JIT (Just-In-Time) production approach. Parameters investigated includes sequencing rules, lot sizes, number of Kanbans used, and number of PCB types.     

Balancing U-shaped assembly lines with resource dependent task times: A Simulated Annealing approach

The advent of Just-in-Time (JIT) and Group Technology philosophies has popularized U-shaped assembly lines, which help overcome many of the disadvantages, like line inflexibility, job monotony, large inventories, etc., typically associated with straight assembly lines. Although U-shaped layout has demonstrated its supremacy over the traditional straight layout, the problem of U-shaped assembly line balancing (ULB) is much more complex. The extant literature on ULB assumes that each assembly task requires a fixed (or no) equipment and a fixed number of workers. However, it is often desirable to reduce certain task times by assigning more workers or alternative equipments at a given workstation. The problem in such cases is to assign not only the task but also resource alternatives (number of workers and equipment type) to workstations. Research on such resource dependent U-shaped assembly line balancing (RDULB) is scarce. We address the problem of RDULB and propose a Simulated Annealing (SA) based metaheuristic, which gives optimal solution for most of the small-to-medium problem instances. For very large problems, while SA generates a good feasible solution within half an hour to 1.5 h, Cplex is unable to find a single feasible solution even after 10 times the CPU time required by SA.     

Assembly line balancing and group working: A heuristic procedure for workers’ groups operating on the same product and workstation

In this paper, we examine an assembly line balancing problem that differs from the conventional one in the sense that there are multi-manned workstations, where workers’ groups simultaneously perform different assembly works on the same product and workstation. This situation requires that the product is of sufficient size, as for example in the automotive industry, so that the workers do not block each other during the assembly work. The proposed approach here results in shorter physical line length and production space utilization improvement, because the same number of workers can be allocated to fewer workstations. Moreover, the total effectiveness of the assembly line, in terms of idle time and production output rate, remains the same. A heuristic assembly line balancing procedure is thus developed and illustrated. Finally, experimental results of a real-life automobile assembly plant case and well-known problems from the literature indicate the effectiveness and applicability of the proposed approach in practice.     

A systematic modelling and simulation approach for JIT performance optimisation

Robust computer aided simulation and modelling tools help to visualise, analyse and optimise complex production processes with a reasonable amount of time and investment. A review of the literature shows that simulation and modelling have not been extensively applied in just-in-time (JIT) manufacturing environments. Also there remains a lack of a comprehensive mechanism to identify the most significant JIT drivers for the purpose of system process optimisation. The prime objective of this study is to close this gap by applying computer based simulation tools and linear mathematical modelling to identify the impact of selected key JIT parameters on performance in an automotive component-manufacturing environment. Research shows that variables such as inconsistent task distribution, variation on operator performance, misconception of total quality management philosophy and lack of set-up time elimination plans disrupt ideal JIT production. In this study, ProModel simulation and modelling software is used to model and simulate different experimental scenarios in order to understand and quantify the impact of selected input key JIT variables on objective functions (i.e. process time and takt time). The outcome is a robust mathematical model that highlights the significance of JIT drivers in the manually operated mixed-model assembly lines.     

A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.     

Implementing a hybrid simulation model for a Kanban-based material handling system

Plant floor material handling is a loose loop in most assembly plants. Simulation offers a quick, controllable and tunable approach for prototyping complex material handling processes in manufacturing environments. This paper proposes a hybrid simulation approach, using both discrete event and agent-based technologies, to model complex material handling processes in an assembly line. A prototype system is implemented using a commercial multi-paradigm modeling tool. In this prototype, JIT principles are applied to both the production and the material handling processes. The system performance is evaluated and system optimization directions are suggested. The proposed hybrid modeling approach facilitates the implementation of a responsive and adaptive environment in that various “what-if” scenarios can be simulated under different simulation configurations and real-time situations.     

基于遗传算法的焊接生产线工位平衡规划方法

在现代化大规模大批量的流水装配制造业中,数量众多的作用分配和多工位的合理安排使工位平衡问题显得更为突出。针对第一类工位平衡问题,即在给定的生产节拍下最小化工位数,首先分析了该问题并建立了数学模型,进而提出了一种基于改进遗传算法求解工位平衡问题的方法。该算法以焊接任务的操作顺序优先关系为约束前提,在初始种群的生产以及交叉和变异过程中保证了染色体解的可行性,同时在遗传算法的选择过程中考虑了具有相同工位数的最优作业方案的工时标准差,从而提高了算法的搜索效率和解的可靠性。最后通过实例求解验证了该算法的有效性。     

An optimal batch size for a JIT manufacturing system

This paper addresses the problem of a manufacturing system that procures raw materials from suppliers in a lot and processes them to convert to finished products. It proposes an ordering policy for raw materials to meet the requirements of a production facility. In turn, this facility must deliver finished products demanded by outside buyers at fixed interval points in time. In this paper, first we estimate production batch sizes for a JIT delivery system and then we incorporate a JIT raw material supply system. A simple algorithm is developed to compute the batch sizes for both manufacturing and raw material purchasing policies. Computational experiences of the problem are also briefly discussed.     

An integrated MRP and JIT framework

A new production planning and scheduling framework is developed to address the multi-stage, production-inventory system problem by integrating Material Requirements Planning (MRP) and Just-in-Time (JIT) production. The objective is to find detailed shop-floor schedules, which specify the quantity of an operation to be processed, at what time, and by which machine, so as to minimize total production cost. The integrated system gets rid of the major problems existing in MRP and JIT. First of all, the proposed integrated system incorporates both the scheduling and capacity planning aspects, simultaneously. Secondly, the integrated system eliminates the need to specify planned lead time. Thirdly, the integrated system, unlike MRP, provides detailed shop-floor schedules. Lastly, the integrated system does not have to operate in the level schedule case as in JIT, so it can handle a very general production environment.     

Scheduling a single mobile robot for part-feeding tasks of production lines

This study deals with the problem of sequencing feeding tasks of a single mobile robot which is able to provide parts for feeders of machines on production lines. The mobile robot has to be scheduled in order to stoppage from lack of parts in the production line. A method based on the characteristics of feeders and inspired by the ( \(s,Q\) ) inventory system, is thus applied to define time windows for the feeding tasks of the robot. The capacity of the robot is also taken into consideration. The performance criterion is to minimize total traveling time of the robot for a given planning horizon. A genetic algorithm-based heuristics is presented which results in a significant increase in the speed of finding near-optimal solutions. To evaluate the performance of the genetic algorithm-based heuristic, a mixed-integer programming model has been developed for the problem. A case study is implemented at an impeller production line in a real factory and computational experiments are also conducted to demonstrate the effectiveness of the proposed approach.     

求解混流装配线调度问题的蚁群算法

以最小化总的传送中断时间为目标函数的混流装配线调度问题是丰田生产方式中自动化概念的一个重要问题,而新颖的蚁群算法具有通用性、鲁棒性、并行搜索以及易于与其他启发式算法结合的优点,可以解决多种组合优化问题,对其进行了改进,以便更适于求解混流装配线的调度问题。实验表明：改进的蚁群算法解决了混流装配线的调度问题,得到了优于分支定界法、模拟退火法和遗传算法的可行解。     

Just‐in‐time parts feeding policies for paced assembly lines: possible solutions for highly constrained layouts

This paper deals with the dimensioning of a just‐in‐time (JIT) parts feeding system, for a paced assembly line. Specifically, we consider the quite frequent case of an old‐fashioned, push‐oriented warehouse arrangement, characterized by materials and subassemblies that are stocked in different and peripheral areas of the manufacturing plant. In these circumstance, layout changes are almost impossible and warehouses cannot be substituted by supermarkets. Nevertheless, this does not preclude the possibility to rationalize material flow and to reduce work in process (WIP) using a JIT parts feeding system. To this aim, we propose two alternative approaches, differentiating in terms of the order with which warehouses and workstations are visited. In order to dimension both systems and to estimate their expected performances, a mathematical model is also presented. At first, the model is presented in a basic formulation, which is very effective and easy to use. A more sophisticated formulation, based on Bayesian probability, is introduced next; this is not so much for practical purposes, but rather to demonstrate the potential of the proposed JIT system in very constrained operating settings.     

A multi-objective scatter search for a mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems.     

A Simulated Annealing algorithm for a mixed model assembly U-line balancing type-I problem considering human efficiency and Just-In-Time approach

This research deals with balancing a mixed-model U-line in a Just-In-Time (JIT) production system. The research intends to reduce the number of stations via balancing the workload and maximizing the weighted efficiency, which both are considered as the objectives of this research paper.After balancing the line and determining the number of stations, the labor assignment policy should be set. In this study, it was assumed that there are two types of operators: permanent and temporary. Both types can work in regular and overtime periods. Based on their skill levels, workers are classified into four types. The sign at each work station indicates types of workers allowed to work at that station. An alert system using the hybrid kanban systems was also considered. To solve this problem, a Simulated Annealing algorithm was applied in the following three stages. First, the balancing problem was solved and the number of stations was determined. Second, workers were assigned to the workstations in which they are qualified to work. Following that, an alert system based on the kanban system was designed to balance the work in the process inventory. This was achieved by defining control points based on the processing time and making control decisions to minimize the number of kanban cards. In the proposed SA algorithm, two methods for the temperature cooling schedule were considered and two methods were defined for determining the number of neighborhood search. The initial temperature was considered equal to the cost of the initial solution to reach the convergence situation as soon as possible. Five problems were solved in small size using the GAMS software. The results obtained from the GAMS software were compared with those obtained from the SA algorithm to determine the performance difference. The computational results demonstrated that the SA algorithm is more consistent with the answers obtained. Also seven large scale problems were solved. The results showed that the SA algorithm still have better reliability. To show the efficiency of the proposed SA algorithm, an axel assembly company was studied. To satisfy demands and reduce backlogging, a mixed model assembly line was designed for this case study. The results showed that the mixed model assembly line designed using the SA algorithm had good efficiency.     

Integrated Set Parts Supply system in a mixed-model assembly line

The mixed-model assembly line (MMAL) is a type of assembly line in which a variety of product models are assembled on the same line. The use of highly variant parts on the assembly line need to be considered carefully to enable satisfactory material flow control and allow for smooth production. To increase the quality of parts supply and parts assembly in MMAL, Toyota has introduced an innovation system known as Set Parts Supply (SPS). In this paper, we investigate the parts supply issues in SPS implementation using a case study in the automotive industry. The linkage of parts supply strategies with Manufacturing Execution System (MES) is introduced to improve the SPS implementation which are (i) synchronized parts supply, (ii) e-kanban system and (iii) Synchronized Supply Sheet. From the research findings, the integration with MES has contributed to the Just In Time in parts supply at the supermarket area and assembly line.     

蚁群算法求解装配线平衡第一类问题

装配线平衡问题是生产管理中重要且较难解决的问题,其中第一类问题是装配线平衡问题的关键问题。本文通过对装配线平衡问题的分析与建模,提出了利用蚁群算法这种人工智能优化算法求解一般装配线平衡第一类问题的步骤和算法。采用启发式的方法构造分配方案的生成策略,并对信息素的更新采用局部更新与全局更新相结合的规则,从而使得该算法具有较好的目的性,大大提高了获得最优解的效率。通过该蚁群算法能得到装配线平衡第一类问题质量较优的解,且有速度快、鲁棒性、通用性等优势。