In-plant material buffer sizes for pull system and level-material-shipping environments in the automotive industry

Traditionally automotive assembly plants operate with at least two weeks of frozen vehicle orders enabling them to calculate the amount of material needed for the next two weeks. Implementing a pull system between the plant and its customers, and level-material-shipping between the plant and its suppliers, changes the amount of material buffer needed within the assembly plant. The pull system eliminates the frozen vehicle-order schedule, without which plants cannot calculate the material needed, but must use inventory to buffer the line from random demand. Level-material-shipping, a just-in-time principle that provides suppliers with a constant shipping schedule, forces plants to use material inventory to buffer the line from un-level demand for parts resulting from un-level production or an un-level model mix. We model these effects to derive formulas for the buffer needed to provide a given service level in the face of demand variability, un-level production, and un-level model-mix.     

Analysis of parts requirements variance for a JIT supply chain

Metrics used to assess level parts usage for some JIT supply chains are based on the product-unit by product-unit launch of products on the assembly line. However, many automobile assembly plants require parts suppliers to deliver parts to the assembly line by using a constant order cycle, non-constant order quantity framework. The paper proposes metrics that are suited to this parts delivery framework. Through extensive simulation over a wide range of assembly parameter values, it assesses the ability of supplier plants to match assembly requirements at each supply epoch. Among other things, the study finds that the conventional JIT scheduling framework also works well in general under this parts delivery arrangement, but acute parts stockout conditions could sometimes occur. Therefore, managers need to consider this in determining appropriate inventory levels for each part. This will prevent stockouts and reduce supply chain costs.     

Research of Dynamic Material Handling in an Automobile General Assembly Line Based on Real-time Information

The status of material delivery of an automobile general assembly line is analyzed,and the technique to achieve the real-time tracking of assembly statas information is proposed based on RFID( Radio Frequency Identification) . Thus the consumption of line-side buffer is obtained dynamically,then the type and quantity of needed material are fed back to the subsystem of material handling; the algorithm for determining the best time departure time of delivery driver based on minimizing of total time penalty function is proposed. This approach makes the ma- terial amount of a single delivery trip maximized and improves the efficiency of delivery drivers significantly in the case of does not affect the assembly line normal throughput. Additionally,although this dynamic material handling method is developed for the automobile assembly plant,it should be pointed out that this method is also applicable to other mixed model assembly plants such as electronics,semiconductor and aerospace industry.     

一种新型物料装置在汽车零部件生产中的应用研究

介绍一种由新型材料制作适用于中小型零件加工、装配和搬运的组合式物料装置及其在汽车零部件生产中的应用。用该材料设计制作的组合式物为装置具有轻便、灵活、可重复使用等特点，能够有效地提高生产率和降低工人的劳动强度。     

Local scheduling problems submitted to global FIFO processing constraints

This paper deals with local scheduling problems as part of a global process management policy. The industrial context is an automotive assembly plant. An automobile manufacturing process creates a list that schedules orders so as to optimize production costs. Such a list must respect the vast majority of shop-related constraints, notably those of the assembly shop, which are the hardest to schedule. This list also allows one to provide information to suppliers regarding the parts they are required to deliver to the assembly plant. PSA Peugeot-Citroën has set the ambitious objective of ensuring compliance with all list entries upon arrival at the assembly plant. This objective is equivalent to instituting a global first-in, first-out (FIFO) management policy for all operations upstream of the assembly line (i.e. body shop, paint shop) and in particular it enables one to make significant reductions in component inventories.

However, FIFO-based flows are difficult to achieve. They first require setting up local production scheduling in the plant in order to satisfy constraints specific to each shop (body shop, paint shop, assembly line); this step will be referred to as ‘local scheduling’. Moreover, a number of disturbances intervene to change the initial order.

The aim of our work is to lay out a new policy for managing flows (called ‘reorderable scheduling’). The premise is to define the maximum scheduling level allowed upon input into each shop so as to guarantee restoration of the initial list order (i.e. reordering) at the assembly entrance.

We will start by studying, from a theoretical perspective, a very general base case and then we will characterize the maximum authorized local scheduling disturbance that enables us to fulfil the global management objective (i.e. FIFO). We will also show the implementation of this method in our industrial case within the context of an automotive assembly plant and finally perform the validation step by means of simulation.     

Factors impacting manual picking on assembly lines: an experiment in the automotive industry

Manual picking on assembly lines is an important part of the assembly operator's work. In the automotive industry, alternative material exposure strategies are continually evaluated in order to facilitate the assembler's work and minimise non-value-adding time, resulting in increased use of smaller bins or containers at workstations, instead of large pallets. The assembly operator's retrieval of components from bins, containers or pallets is known as manual picking. Previous work in this area has focused on picking from pallets placed on the floor; therefore, there is a need to include other forms of material exposure. The purpose of this paper is to explain what material exposure factors have an impact on manual picking times on assembly lines. A full factorial experiment, testing seven factors in 128 experiments, was conducted in an automotive assembly line setting. The factors with the greatest impact on manual picking time were packaging type; angle of exposure; height of exposure; and part size. This study contributes by addressing the importance of six factors influencing manual picking times, enhancing knowledge derived from MTM and elsewhere. The results from this study can be useful for designing manual picking operations, not just at assembly lines but also other situations where discrete manual picking occurs such as at workstations or bench assembly. Managerial contributions are evident in the examination of factors important in designing new production systems, including materials supply processes and assembly processes. A further managerial application is the use of the results in managing assembly lines.     

A realistic multi-manned five-sided mixed-model assembly line balancing and scheduling problem with moving workers and limited workspace

The assembly line balancing problem can completely vary from one production line to the other. This paper deals with a realistic assembly line for the automotive industry inspired by Fiat Chrysler Automotive in North America and Parskhodro in Iran (both large-scale automotive companies). This problem includes some specific requirements that have not been studied in the literature. For example, the assembly line is five-sided, and workers can move along these sides. Due to the limited workspace, all the sides cannot work simultaneously at one station. First, a mixed integer linear programming model is proposed for the problem. Then, the model is improved to have a tighter linear relaxation. Moreover, an effective logic-based Benders’ decomposition algorithm is developed. After careful analysis of problem’s structure, three propositions are introduced. The master problem is well restricted by eight valid inequalities. Two different sub-problem types are defined to extract more information from the master problem’s solution. In this case, the algorithm adds effective cuts that reduce the solution space to the extent possible at each iteration. Thus, the number of iterations is significantly cut down. The performance of the model and algorithm, as well as improvement made on both, is evaluated.     

Using distributed search methods for balancing mixed-model assembly lines in the automotive industry

Currently, modern companywide PC networks usually possess significant unused calculation capacity. Since the connected personal computers are mainly used for office applications, considerable off-peak times occur. Consequently, in order to solve planning problems more efficiently, it is promising to apply distributed search procedures that make use of those available off-peak times. This applies in particular to complex problems where insights into the structure of the solution space are lacking. The paper at hand illustrates the application of distributed search methods to automotive assembly line balancing. Modern mass customization programs in the automotive industry frequently comprise more than a billion theoretical variants. Since this causes an oscillating capacity demand at the line, deliberately designing the layout of a mixed-model assembly line is of significant importance. The paper at hand provides a new mixed-model assembly line balancing approach that integrates specific aspects relevant for the automotive industry. However, by integrating several -hard subproblems like a detailed personnel planning or a flexible process planning of each task, the resulting model has significant complexity. Consequently, in order to find appropriate line layouts in reasonable time, specifically designed distributed solution approaches are provided and evaluated. Among these approaches, particularly the use of a specific clustered Tabu Search algorithm attains promising results. By making use of an adaptive dynamic load balancer, substantial improvements of the solution quality can be obtained even under unfavorable circumstances like oscillating background loads in the PC network.     

Design of stochastic assembly lines considering line balancing and part feeding with supermarkets

This article aims to address the assembly line balancing problem (ALBP) and supermarket location problem (SLP) as two long-term interrelated decision problems considering the stochastic nature of the task times and demands. These problems arise in real-world assembly lines during the strategic decision-making phase of configuring new assembly lines from both line balancing and part-feeding (PF) aspects. A hierarchical mathematical programming model is developed, in which the first level resolves the stochastic ALBP by minimizing the workstation numbers and the second level deals with the stochastic SLP while optimizing the PF shipment, inventory and installation costs. The results of case data from an automotive parts manufacturer and a set of standard test problems verified that the proposed model can optimize the configuration of assembly lines considering both ALBP and SLP performance measures. This study also validates the effect of the stochastic ALBP on the resulting SLP solutions.     

Impacts of RFID implementation on cost structure in networked manufacturing

Radio frequency identification (RFID) presents the key innovative solution to automatically capture and track the movement of material items throughout an entire supply chain. In this article, we explore influences of RFID technology implementation on cost and profit efficiencies. For this purpose a mathematical model of supply chain cost structure and functions was created to analyse the impacts. The model, which has also been verified through a case study approach, is aimed to examine the economical impact of RFID implementation at an automotive original equipment manufacturer and its suppliers. To analyse the impact of the traceability system using RFID technology, a cost reduction parameter will be used. Since in automotive supply chains the functional dependence of individual parts is changing in the upstream and downstream supply chain, only certain parts will be calculated with their complete functions and relations.     

带时间窗的汽车总装线物料配送路径规划

分析了现阶段汽车总装线物料配送中存在的问题,建立了适用于汽车总装线物料配送路径规划的混合时间窗模型,提出了解决带时间窗的汽车总装线物料配送路径优化问题的改进遗传算法,使用了一种新的染色体编码方式和与之对应的交叉算子。针对传统轮盘赌随机操作选择误差比较大的弊端,提出改进的轮盘赌选择算子,加大随机数的产生次数并加入排序选择的思想,融合了最佳个体保存选择策略,提高算子的选优性能。实验表明该算法用于求解带时间窗的汽车总装线物料配送路径问题的有效性。     

Workload balancing and manufacturing complexity levelling in mixed-model assembly lines

To effectively react and meet the current ever growing demand for individualised motor vehicles, built to customer specific requirements, automotive industry has accelerated its transition towards mass-customisation. As a result, the number of new model introductions has drastically increased over the past three decades. To cope with this intensified customisation, the current automotive assembly platforms are designed to assemble a wide range of relatively different models, and are turned into mixed-model assembly lines (MMALs). This implies that the set of tasks to be performed on each workstation is no longer stable but varies highly with the model-mix. As a consequence, the manufacturing complexity increases at the workstations and throughout the whole assembly system. This paper proposes a method to monitor manufacturing complexity at each workstation while the MMAL is being balanced. An entropy-based quantitative measure of complexity, which incorporates the variability of each task duration, is developed. This measure is used to monitor the manufacturing complexity level at each workstation. An integrated mixed-line balancing and complexity monitoring heuristic is proposed, to determine workload balance solutions, in which manufacturing complexity is levelled throughout the workstations composing the line. This procedure is tested on a real data-set provided by an automotive manufacturer. The results are reported and thoroughly discussed.     

A modified particle swarm optimisation algorithm to solve the part feeding problem at assembly lines

The Assembly Line Part Feeding Problem (ALPFP) is a complex combinatorial optimisation problem concerned with the delivery of the required parts to the assembly workstations in the right quantities at the right time. Solving the ALPFP includes simultaneously solving two sub-problems, namely tour scheduling and tow-train loading. In this article, we first define the problem and formulate it as a multi-objective mixed-integer linear programming model. Then, we carry out a complexity analysis, proving the ALPFP to be NP-complete. A modified particle swarm optimisation (MPSO) algorithm incorporating mutation as part of the position updating scheme is subsequently proposed. The MPSO is capable of finding very good solutions with small time requirements. Computational results are reported, demonstrating the efficiency and effectiveness of the proposed MPSO.     

Product quality and plant build complexity

This paper assesses the hypothesis that plant build complexity impacts quality, suggests mechanisms to explain how complexity impacts quality, and offers countermeasures to mitigate this impact. We analyse automotive assembly data that supports the proposition that plant build complexity negatively impacts quality. In addition, this paper provides actual automotive assembly line work instructions and shows how they can be represented by the Gilbreths’ fundamental work elements and plant build complexity impacts these work elements. To highlight complexity's adverse impact on quality, we compare the actual work instructions at two automotive assembly line stations–one with very little complexity and one with extreme complexity. We list common automotive assembly quality lapses and describe how they can be caused or exacerbated by complexity. Finally the paper presents practical countermeasures to mitigate complexity's impact on quality. While based on the automotive industry, the lessons apply to all complex assembly lines.     

Sequence alteration and restoration related to sequenced parts delivery on an automobile mixed-model assembly line with multiple departments

A mixed-model assembly line is commonly used in the automobile industry. When there are multiple departments on an assembly line, there are usually different sequencing considerations from various departments. Intentional sequence alteration to accommodate a different sequencing consideration can be needed for a downstream department. Unintentional sequence alteration may also take place due to rework or equipment breakdowns. There is also an increasingly common practice in automobile assembly to have parts sequenced before delivering to the final assembly line. To achieve sequenced parts delivery, the sequence needs to be known in advance. Thus, addressing sequence alteration and restoration becomes more relevant for an automobile mixed-model assembly line. A number of sequence alteration methods to accommodate a downstream department's sequencing considerations are presented. One method easily supports sequence restoration of the sequence altered by the method. Two sequence restoration methods for restoring the sequence altered by unintentional reasons are discussed; the proper sizing of the two restoration methods are also addressed. These sequence alteration and restoration approaches mainly address the design and control aspects of the mixed-model assembly line. A case study based on an automobile assembly plant is presented to demonstrate the use of these methods.     

Monolithic versus hierarchical approach to integrated scheduling in a supply chain

The two approaches, monolithic and hierarchical, with a set of mixed integer programming formulations are proposed and compared for multi-objective integrated scheduling in a customer driven supply chain. The supply chain consists of multiple manufacturers (suppliers) of parts, a single producer of finished products and a set of customers who generate final demand for the products. Each supplier has a number of identical production lines in parallel for the manufacture of parts, and the producer has a flexible assembly line for assembly of products. Given a set of orders, the problem objective is to determine which orders are to be provided with parts by each supplier, find a schedule for the manufacture of parts by each supplier and for the delivery parts from each supplier to the producer, and find a schedule for the assembly of products for each order by the producer, such that a certain performance measure of the supply chain is optimised. The selection of the parts supplier for each order is combined with due date setting for some orders, subject to the suppliers and the producer available capacity. Different objective functions are considered that take into account both customer service level and total manufacturing, delivery and production cost. Numerical examples are presented that are modelled by real-world integrated scheduling in a customer driven supply chain of high-tech products, and some computational results are reported to compare the two approaches.     

Determination of optimal part delivery dates in a stochastic assembly line

We consider the problem of determining the optimal part delivery dates of a low volume, small lot assembly line which manufactures large and costly products. Parts are assembled at each stage to a subassembly. The arrival time of an assembly part from a vendor is assumed to be a normally distributed random variable. A dynamic programming algorithm is developed to minimize work-in-process inventory of parts and subassemblies. It is implemented on the single job problem. Its further extensions to the multi-job case and to incorporate the cost of setting delivery dates are also discussed.     

DFT的装配车间生产物流策略仿真与研究

针对某汽车部件的实际装配生产,运用离散事件仿真的方法对基于需求流动技术(DFT)的装配生产物流策略进行了研究.在分析该汽车部件装配生产流程的基础上,确定eM-Plant仿真软件中的建模对象与装配生产系统中实体对象的对应关系,使用面向对象的方法建立了相应的离散事件仿真模型.在模型中分别实施基于推式生产方式的物流策略和基于DFT的物流策略,以该汽车部件的平均日产量为绩效指标,比较了两种物流策略对装配生产的影响.     

Capacitated lot sizing with alternative routings and overtime decisions

Material requirements planning (MRP) is a basic tool for performing detailed material planning function in the manufacture of component parts and their assembly into finished items. MRP's managerial objective is to provide ‘the right part at the right time’ to meet the schedules for completed products. However satisfying end customer demands faster with lower inventories implies smarter scheduling which must simultaneously reflect actual capacity conditions. Therefore, the need is to schedule both capacity and materials simultaneously. Since MRP does not consider the availability of capacity resources to schedule production, consequently the schedules so developed are usually capacity infeasible. This paper proposes a three-step procedure to develop capacity feasible material and production schedules in a finite capacity environment. In the first step, an LP model produces capacity feasible but lot size relaxed planned order releases for all end products and assembly components which are then fed into a MRP processor, where a bill of material (BOM) explosion process generates material plans. Finally, these material plans are introduced to another LP model which assures that capacity feasibility is again restored. The mathematical models developed consider restrictions on lot sizes as well as alternative production routings and overtime decisions. A numerical example also is provided and some future research directions are outlined.     

Balancing mixed-model assembly lines: a computational evaluation of objectives to smoothen workload

Mixed-model assembly lines are widely used in a range of production settings, such as the final assembly of the automotive and electronics industries, where they are applied to mass-produce standardised commodities. One of the greatest challenges when installing and reconfiguring these lines is the vast product variety modern mixed-model assembly lines have to cope with. Traditionally, product variety is bypassed during mid-term assembly line balancing by applying a joint precedence graph, which represents an (artificial) average model and serves as the input data for a single model assembly line balancing procedure. However, this procedure might lead to considerable variations in the station times, so that serious sequencing problems emerge and work overload threatens. To avoid these difficulties, different extensions of assembly line balancing for workload smoothing, i.e. horizontal balancing, have been introduced in the literature. This paper presents a multitude of known and yet unknown objectives for workload smoothing and systematically tests these measures in a comprehensive computational study. The results suggest that workload smoothing is an essential task in mixed-model assembly lines and that some (of the newly introduced) objectives are superior to others.