Order sequencing and capacity balancing in synchronous manufacturing

Synchronous manufacturing aims at achieving the benefits of intermittent production lines in production situations that operate without lines. Benefits such as short and constant throughput times and predictable capacity loading can be acquired through an appropriate design of the synchronous manufacturing system and its control system. The order release mechanism is an essential part of this control system. It determines the sequence in which orders are released to the shop floor. As orders may differ in the amount and distribution of their capacity requirements over subsequent production stages, total capacity load may vary over time. If the available capacity per period is not flexible, capacity balancing becomes an issue in the order release decision. In practice, heuristics or rules of thumb are used to solve this problem, but their effectiveness is questioned. This paper examines the effectiveness of some new heuristics that are based on insights from assembly system design and work load control, and compare their performance with an optimal solution approach. The approaches are evaluated in a rolling schedule environment, and under different levels of capacity fluctuations and problem sizes. The results show that the performance of the heuristic solutions deteriorates if capacity fluctuations between the stages increase. If we measure both the amount and frequency of shortages over a long period of time in a rolling schedule environment, a quite simple rule that only takes the available capacity during the first stage into account outperforms more intelligent rules.     

The economic lot and delivery scheduling problem: common cycle, rework, and variable production rate

The economic lot and delivery scheduling problem is to simultaneously determine the production sequence of several assembly components at a supplier and the delivery interval of those components to the customer. The customer, an assembly facility, is assumed to use the components at a constant rate. The objective is to find the production sequence and delivery interval that minimize the holding, setup, and transportation cost for the supply chain. Previous solutions to the problem assume a constant production rate for each component and that all components are of acceptable quality. These assumptions ignore volume flexibility and quality cost. Volume flexibility permits a system to adjust the production rate upwards or downwards within wide limits. Also, component quality may deteriorate with larger lot sizes and decreased unit production times. In this paper, we develop an algorithm for solving the economic lot and delivery scheduling problem for a supplier using a volume flexible production system where component quality depends on both lot sizes and unit production times. We test the performance of the algorithm and illustrate the models with numerical examples.     

Integrated production planning and quality control for linear production systems under uncertainties of cycle time and finished product quality

This work considers serial production systems with several process steps and a possible quality control at final step. It deals with the problem of optimising planned lead time when the real lead time for each process is stochastic and the finished product quality is uncertain unless it is inspected. Three analytical models are proposed aiming to minimise the expected total cost, which is composed of the inventory and backlogging costs for the finished product and quality costs associated with inspection and non-conformities. These models correspond to three quality control policies: (i) without quality control, (ii) with quality control but without taking into account the inspection duration when optimising the planned lead time and (iii) with quality control and with considering the inspection duration when optimising the planned lead time. Based on the results, it can be highlighted the economic advantage of integrating quality control at the early stage of supply and production planning decisions for some cost parameters conditions. The robustness of the proposed models is also analysed regarding the variance of the probability distributions of the lead times.     

基于机器视觉的电视机背板检测及自适应抓取研究

机器视觉广泛应用于装配、制造和加工等工业领域,用来保证产品质量,控制生产流程。将图像处理技术应用于自动化装配生产线,对电视机背板质量进行在线检测。为了实现对不同型号和尺寸的电视机背板的抓取,使用图像模板匹配方法确定当前背板的型号和检测当前背板的品质,并开发基于采集图像的视觉伺服自适应控制系统,该控制系统以西门子 PLC（programmable logic controller,可编程逻辑控制器）作为控制核心,通过串口通信实现PLC与Halcon软件的数据对接。使用Halcon中的工具箱对电视机背板采集图像进行运算以抽取目标的特征,并分析出抓取过程的坐标值,进而根据判别的位置坐标误差结果来控制步进电机调节偏移量以实现自适应抓取过程。针对图像处理后产生的电视机背板位姿累计误差,设计了一种标准模板中心参数值与增量误差值求差的方法,对背板位姿视觉定位误差进行补偿。通过抓取实验验证了视觉伺服自适应控制系统的可靠性较高,能满足自动化装配的工业生产需求。在生产实际中,电视机背板自动检测及抓取平台的应用能极大地提高电视机生产效率、节约人工成本、降低劳动强度和提高生产精度,该平台具有很好的应用前景。     

Channel coordination in an assembly system facing uncertain demand with synchronized processing time and delivery quantity

Supply chains need to be synchronized to increase system efficiency. With increasing globalization, an assembly or a manufacturing firm may receive supplies from all over the world. Synchronization of such a system becomes difficult for two reasons: first, each supplier has certain capacity and can supply materials with a fixed but different order processing time; second, each supplier has a different cost structure and, therefore, production quantities differ due to incentives conflicts, which leads to local optimization of profits by the suppliers. In the existing literature, this problem is generally tackled by focusing either on the synchronization of order processing time or on order quantity determination. However, when system coordination is of the essence, simultaneous consideration of both becomes imperative. A model to synchronize the assembly process is proposed and the optimal order quantities in a two-echelon assembly system with one assembler (facing a stochastic demand) and multiple suppliers with deterministic order processing times is determined. A numerical study conducted shows that risk sharing and proper safety stock placement lead to better system coordination and improve system performance.     

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.     

Plant-level maintenance decision support system for throughput improvement

In the current global scenario of extreme competition, factors such as productivity, availability, quality and cost of operations play a vital role in the success of a company. A critical component relating to all of the above is maintenance. The conventional maintenance decision support systems have primarily focused on maximising the gains of a single machine system. However, a real life application usually consists of multiple machines, and the operational level decisions are more complex. In this paper, an on line plant-level maintenance decision support system (PMDSS) is developed by combining the short term and long term decision making process to improve the overall system performance while continuously attempting to maximise immediate profits in the short term. The PMDSS works towards two basic aims: (1) unplanned downtime reduction by predicting the remaining useful life of the machines, and (2) efficient utilisation of the finite maintenance and production resources through identifying the throughput-critical machines. The benefits of this approach are presented by considering an industrial case study of an automotive assembly line. The results obtained using this PMDSS approach shows a big throughput improvement as compared to the conventional maintenance policies.     

Value of information in remanufacturing complex products

Remanufacturing facilities usually face a trade-off between limited information about remanufacturing yields and potentially long supplier lead times. To improve production performance, these firms may attempt to acquire more timely and accurate information about remanufacturing yields or alternatively, to reduce the lead times of purchased parts. We develop four decision-making models to evaluate the impact of yield information and supplier lead time on manufacturing costs. We identify the operating conditions under which these capabilities are valuable, along with their relative impact on facility performance. Each model is formulated as an infinite horizon, stochastic dynamic program (Markov decision process). Our results indicate that the yield information is generally quite valuable, while investments in supplier responsiveness provide trivial returns to products with few parts. However, as product complexity increases with large number of target parts, the value of short lead times increases.     

Evaluation of performance measures for multi-part,single-product kanban controlled assembly systems with stochastic acquisition and production lead times

We consider the following kanban-controlled, multi-stage production assembly system. A number of raw parts are acquired from various suppliers and assembled into a single product. The raw-part acquisition lead times, the production lead time and demand arrival are all random variables. The raw-part acquisition order is made when the inventory level of the common part buffer, consisting of a number of sets of raw parts where a set of raw parts forms a single product, depletes to a reorder point. A production stage consists of the input queue, the output buffer, and the kanban board. The finished product can be backordered with a given allowable quantity. The problem is to evaluate the various system performance measures for a given set of design parameters: the raw-part batch order size, the common buffer size, the reorder point, the number of kanbans circulating in each production stage. A system is decomposed into a number of semi-autonomous Markov processes. A mathematical model is formulated and an iterative algorithm is proposed to evaluate system performance measures. Extensive numerical experiments with simulation analyses show that the computational time is very short and the model proposed is reasonably accurate. Thus, resorting to a genetic algorithm we can find an optimal set of system design parameters.     

Using the when/where rules in dual resource constrained systems for a hybrid push-pull control

This paper proposes a production control system, DRC-HPP, which uses the when/where rules in dual resource constrained (DRC) systems for a hybrid push-pull (HPP) control, to overcome some difficulties in modelling/implementing DRC/Kanban systems. These rules and the novel ‘process-or-transport’ and ‘whereto’ rules are embedded in some policies workers use to decide when to process (transport) parts, and where (whereto). Unlike most control systems, in which a group of workers is always responsible for transporting and another group is always responsible for processing parts, workers in DRC-HPP are responsible for both transporting and processing parts, as in the Toyota Sewn Products Management System (TSS). Yet, unlike TSS, DRC-HPP can be applied in any layout type. Workers transport parts when they are idle in part processing to enhance their utilisations and synchronise transportation. Since the transportation does not require special worker skills, the cost of training workers is not incurred. DRC-HPP is compared with different benchmarks through simulation experiments to evaluate its performance. It performs well under relatively short transportation times with respect to processing times. If they are relatively longer, the issue becomes to determine the number of workers to achieve a performance level. DRC-HPP also facilitates bottleneck management.     

Scheduling manufacturing systems for delayed product differentiation in agile manufacturing

Production of customized products to respond to changing markets in a short time and at a low cost for agile manufacturing can be implemented with delayed product differentiation in a manufacturing system. The successful implementation of delayed product differentiation lies in efficient scheduling of the manufacturing system. Scheduling problems in implementing delayed product differentiation in a general flexible manufacturing system are defined, formulated and solved here. The manufacturing system consists of two stages: machining and assembly. At the machining stage, a single machine is used to produce standard component parts for assembly products. These parts are then assembled at the assembly stage by multiple identical assembly stations to form customized products. The products to be produced in the system are characterized by their assembly sequences represented by digraphs. The scheduling problem is to determine the sequence of products to be produced in the system so that the maximum completion time (makespan) is minimized for any given number of assembly stations at the assembly stage. Based on the representation of assembly sequence of the products, three production modes are defined: production of a single product with a simple assembly sequence ; production of a single product with a complex assembly sequence ; and production of N products . According to the three defined production modes, the associated scheduling problems are defined as G s scheduling problems, G c scheduling problems and N-product scheduling problems, respectively. Optimal and heuristic methods for solving the scheduling problems are developed. The computational experiment shows that the heuristics provide good solutions to the scheduling problems.     

Optimal order release dates for two-level assembly systems with stochastic lead times at each level

In this paper, we examine an optimisation problem for component replenishment in two-level assembly systems under stochastic lead times. The Assembly-to-Order principle is applied. The demand for a finished product and its planned due date are known. The capacity of the assembly system at each level is considered infinite. At each level, the assembly process starts when all the required components or semi-finished items are available. At the second level, the components are ordered from external suppliers and order release dates are decision variables of the problem. A backlogging cost is incurred if the finished product demand is satisfied after the planned due date. If the finished product, a given component or a semi-finished product is available before the corresponding assembly date, an inventory holding cost is considered. Genetic algorithms (GA) reinforced with different techniques are developed to find order release dates that minimise the total expected cost. A Branch and Bound method is also developed to assess the effectiveness of the hybrid GA. Regardless of the number of components and the variability of the costs related to the finished product, the experimental results indicate that the proposed GA are highly efficient.     

Iterated beam search for the combined car sequencing and level scheduling problem

The level scheduling problem is concerned with the final stage of a multi-stage just-in-time production system so that different models of a product are evenly distributed in a discrete production sequence, thereby making the problem practically an unconstrained optimisation problem. The car sequencing problem, on the other hand, is a constraint satisfaction problem based on a number of options constricting the final assembly schedule. The combined car sequencing and level scheduling problem aims to find the optimal production schedule that evenly distributes different models over the planning horizon and satisfies all option constraints. This paper proposes a parametric iterated beam search algorithm for the combined problem that can be used either as a heuristic or as an exact optimisation method. The paper includes a computational study based on a 54-instance test bed that proves the effectiveness of the proposed algorithm.     

需求和提前期不确定下的两级供应链系统研究

由一个部件提供商和一个装配商组成的两级组装供应链系统,部件提供商提供两种不同的部件给装配商进行装配;分析在单销售周期内,系统上同时存在部件的生产提前期和装配提前期的不确定,以及需求的不确定时,系统的特性.采用博弈论方法,装配商确定装配计划提前期,以及向部件提供商提交的订购量;而部件提供商确定部件生产计划提前期,以及部件批发价格.得出,首先,不确定提前期导致供应链系统订购量减小;其次,在分散控制的组装供应链系统中,系统的总生产、装配计划提前期,要大于集中控制系统中的,并且装配计划提前期与部件生产计划提前期无关;再者,过大的提前期不确定性对组装供应链系统的影响要比需求不确定对组装供应链的影响大.     

Base-stock control for single-product tandem make-to-stock systems

In this paper, we consider a multiple-stage tandem production/inventory system producing a single product. Processing time at each stage is assumed to have a general stationary processing time distribution. The cost of holding work-in-process (WIP) inventory is different at each stage. Therefore, decisions on when to release work to the system as well as when to transfer WIP from one stage to another need to be made. We formulate this problem of release/production control as a Markov decision process. However, the optimal policy is rather complex, making its implementation impracticable in practice. We therefore investigate the performance of simple base stock policies. Our approach aggregates several stages into one and uses a simple approximation to compute 'approximately optimal' base stock levels. We present the results of a simulation study that tests the performance of our approximation in estimating the best base stock levels, and the performance of base stock policies as compared with the optimal policy.     

Synchronized manufacturing at final assembly and feeder shops

Many manufacturing environments consist of a final assembly shop that receives components from various feeder shops. Suppose the final assembly shop assembles a mix of products, each requiring varying amounts of different components. Thus, the sequence in which products are assembled imposes time-dependent demands on the feeder shops. Such problems are often found in the assembly of high-tech telecommunications systems and in the assembly of mixed models of automobiles. We develop a variety of models that link the work-in-process inventory in the feeder shop and the finished component inventory to the component production interval and the final assembly sequence. The manufacturing disciplines considered include push, look-ahead and kanban. Feeder shops are assumed to manufacture components continuously, periodically, or at a fixed lot size. Simple performance characteristics of the feeder shop are derived to provide insight into synchronized manufacturing issues and to help evaluate the effect of different final assembly sequences on feeder shops.     

Buffering unbalanced assembly systems

Many assembly systems are required to hold minimal work-in-process inventories because of space or capital limitations. Unfortunately, in the presence of variable processing times, output from an assembly line may be severely restricted if no work-in-process inventory is held. Thus, assembly lines often should be designed to have minimal, but not zero, buffer capacity. In this paper we address the issue of how one should optimally deploy limited buffer capacity in unbalanced assembly systems, and we find that the answer is sometimes counterintuitive. We study simple asynchronous assembly systems with random processing times and develop simple heuristic rules that can be used to improve existing operations and to support line designers who are faced with increasingly rapid cycles of new product introduction. We apply these heuristics to several larger systems and discover that they perform quite well.     

Buffering unbalanced assembly systems

Many assembly systems are required to hold minimal work-in-process inventories because of space or capital limitations. Unfortunately, in the presence of variable processing times, output from an assembly line may be severely restricted if no work-in-process inventory is held. Thus, assembly lines often should be designed to have minimal, but not zero, buffer capacity. In this paper we address the issue of how one should optimally deploy limited buffer capacity in unbalanced assembly systems, and we find that the answer is sometimes counterintuitive. We study simple asynchronous assembly systems with random processing times and develop simple heuristic rules that can be used to improve existing operations and to support line designers who are faced with increasingly rapid cycles of new product introduction. We apply these heuristics to several larger systems and discover that they perform quite well.     

基于XGBoost的车身尺寸装配质量智能预测模型

针对汽车多级制造系统中传统机器学习方法处理多元数据样本时间久、精度低等问题,提出一种基于XGboost的车身尺寸装配质量智能预测模型,解决多级制造系统的车身装配精准预测控制问题。首先,通过对车身多级装配过程的分析,对数据样本进行预处理,建立基于Spearman系数的不同特征要素的绝对相关性矩阵;其次,对生产流程的相关数据实时采集、清洗及挖掘分析,提出数据分析流程与数据处理框架,建立基于XGBoost的车身尺寸装配质量智能预测模型,并通过对模型性能的有效评估实现对车身尺寸装配的精准控制;最后,仿真实例对比分析表明,基于XGboost的质量智能预测模型能精准地解决多级制造系统中的车身装配质量控制问题。     

Safety stock optimisation in two-echelon assembly systems: normal approximation models

This paper tackles the problem of optimising safety stocks in a two-echelon assembly system. It presents and discusses several approximation models for the assembly lead-time under the assumption of normality of the assembly demand and normality of components’ nominal lead times. These approximation models are subsequently used to optimise safety stocks throughout a two-echelon assembly system. They are then tested on a particular two-echelon N-identical component assembly system. The obtained results are compared with the results of a discrete event simulation. Finally, it is shown that lead-times and safety stock results already obtained for a two-echelon distribution system can also be derived without difficulty from those of two-echelon assembly systems.