Capacity-based order review/release strategies to improve manufacturing performance

Given the increased emphasis by manufacturing organizations to reduce work-in-progress inventories thereby shortening their manufacturing lead times, more attention is being given to the control of the materials released to the shop floor. The control of input into the shop is one of the activities associated with order review/release (ORR). A common assumption of the published literature on ORR is that all orders received by the shop will be accepted, regardless of shop conditions. In this paper, this assumption is relaxed such that in times of high shop congestion, it may be better to reject an order to allow the customer to seek another supplier than to accept the order and deliver it to the customer late. When the shop is highly congested, accepting all orders will jeopardize the ability of the shop to meet customer due dates. In an industry that demands fast and reliable turnaround of customer orders, not meeting due dates may result in customers placing their orders with other more reliable suppliers. Of course, rejecting the order outright will also have some negative effects. The best alternative would depend on the preferences by managers and the tradeoffs involved between rejecting the order or accepting the order and then not meeting the customer due date. Three methods to determine whether or not to accept the order were tested in this research. Results suggest the consideration of shop loads is better than a random rejection in determining whether an order should be accepted or rejected by the shop. Also, an analysis of the load on the orders routeing is more effective than considering the overall load in the shop.     

A methodology for planning and controlling workload in a job-shop: a four-way decision-making problem

There has been extensive research on workload and input–output control with the objective of improving manufacturing operations in job-shops. In this paper, a multiple decision-making scheme is proposed to plan and control operations in a general job-shop, and to improve delivery and workload related performance measures. The job-shop characteristics reinforce the need for designing a global system that controls both the jobs entering (order acceptance, due date setting and job release) and the work-in-process (dispatching), leading to an improvement of operational measures. Previous research has concentrated on scheduling a set of orders through the shop floor, according to some decision mechanism, in order to optimise some measure of performance (usually total lead time). This means that, since only a part of the decision-making system is being optimised, the resulting decision may be sub-optimal. In this paper it is shown that the performance of the different decision rules changes when they are considered simultaneously. Hence, a higher level approach, where the four decisions (order acceptance, due date setting, job release and dispatching) are considered at the same time, should be adopted to improve job-shop operational performance.     

Event-driven multi-agent ubiquitous manufacturing execution platform for shop floor work-in-progress management

It has been reported that radio frequency identification (RFID) technology is applied to manufacturing shop floors for capturing and collecting real-time field data. Real-time information visibility and traceability allows decision makers to make better-informed shop floor decisions. It has been a great challenge to process a huge amount of RFID data into useful information for managerial uses. This paper presents an event-driven shop floor work-in-progress (WIP) management platform for creating a ubiquitous manufacturing (UM) environment. The platform aims to monitor and control dynamic production and material handling through RFID-enabled traceability and visibility of shop floor manufacturing processes. The platform provides facilities to process shop floor real-time RFID events and to aggregate actionable and meaningful operational information to support decision-making activities. An information processing mechanism based on a critical event model is proposed to organise real-time field data in various abstract levels for enterprise decisions. A case study at an air conditioner manufacturing company is used to demonstrate how the proposed platform can benefit its shop floor WIP management by showing how production and logistic operators and their supervisors accomplish their tasks.     

MTO运营模式的订单交货期决策模型

承诺交货期过晚,没有时间优势,容易丢失订单;承诺交货期过早,企业超负荷生产,增加生产成本,可能出现亏损。针对基于订单式生产(MTO)的中小制造企业的客户订单交货期预报问题,综合考虑价格、订单需求量、提前完工和延期交货损失、加班、外协和损耗率因素等对任务完工期的影响,以收益最大化为目标,建立了订单任务交货期决策模型,并运用Matlab编程和计算工具对实例进行分析,验证了模型的有效性。模型对MTO生产企业交货期决策具有一定的参考意义。     

MTO生产模式的完工期预测及承诺交货期决策

在订单式生产的中小制造企业中,订单的完成过程受诸多不确定因素的影响。考虑影响完工期的确定因素及随机因素,如生产时间、插单的等待时间、机械故障等待时间、误工时间、损耗率及返工率等,根据数据统计分析假定各因素服从一定的随机分布,建立了完工期区间预测模型,通过多次仿真得到了完工期区间范围。以相关原则为依据,通过比较预测完工期及客户要求交货期,做出承诺交货期及价格决策。最后进行灵敏度分析,通过仿真得到完工期均值与各随机变量对应参数的变化关系,在此基础上提出了控制完工期影响因素的措施。     

Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors

Physical Internet (PI, π) has been widely used for transforming and upgrading the logistics and supply chain management worldwide. This study extends the PI concept into manufacturing shop floors where typical logistics resources are converted into smart manufacturing objects (SMOs) using Internet of Things (IoT) and wireless technologies to create a RFID-enabled intelligent shop floor environment. In such PI-based environment, enormous RFID data could be captured and collected. This study introduces a Big Data Analytics for RFID logistics data by defining different behaviours of SMOs. Several findings are significant. It is observed that task weight is primarily considered in the logistics decision-making in this case. Additionally, the highest residence time occurs in a buffer with the value of 12.17 (unit of time) which is 40.57% of the total delivery time. That implies the high work-in-progress inventory level in this buffer. Key findings and observations are generated into managerial implications, which are useful for various users to make logistics decisions under PI-enabled intelligent shop floors.     

Multi-agent hybrid shop floor control system

A manufacturing system is composed of many activities that can be monitored and controlled at different levels of abstraction. A shop floor can be considered an important level at which to develop a Computer Integrated Manufacturing system (CIM). However, a shop floor is the dynamic environment where unexpected events continuously occur, and impose changes to planned activities. To deal with this problem, the shop floor should adopt an appropriate control system that is responsible for the coordination and control of the manufacturing physical flow and information flow. In this paper, a hybrid control system is described with a shop floor activity methodology called Multi-Layered Task Initiation Diagram (MTD). The architecture of the control model identifies three levels; i.e. the shop floor controller (SFC), the intelligent agent controller (IAC) and the equipment controller (EC). The methodology behind the development of the control system is an intelligent multi-agent paradigm that enables the shop floor control system to be an independent, autonomous, and distributed system, and to achieve an adaptability to change the manufacturing environment.     

Virtual cell formation

The concept of a virtual cellular manufacturing system (VCMS), which operates very much like a traditional cellular manufacturing system (CMS), has attracted considerable attention in recent years. Unlike traditional cellular manufacturing systems, virtual cellular manufacturing systems are most suitable in production environments that experience frequent product mix changes. Whereas, in traditional CMS, the shop floor configuration is fixed, in VCMS the shop floor configuration changes in response to changes in product mix over time. Therefore, the life of a given shop floor configuration continues as long as the product mix remains relatively unchanged. Furthermore, whereas in traditional cellular manufacturing systems, a machine cell occupies a contiguous region of the shop floor, the same is not necessarily true with virtual cells. Virtual manufacturing cells are simply logical cells in which the machines belonging to the same cell need not occupy the same contiguous area. Because cell members are logical instead of physical, machines in a cell can be at any location on the floor. In this paper, we present a methodology for designing virtual manufacturing cells.     

Effectivity date analysis and scheduling

The impact of design on logistics cannot be ignored, and design for logistics is a new concept similar to design for manufacturing or design for assembly. Engineering change is one of the scenarios that would require logistics support. Change control of a product data management (PDM) system is one of the major approaches for handling engineering changes today. According to principles of configuration management, during the change control workflow, there are three different dates: release date, effective date, and effectivity date utilised for controlling and managing change planning and scheduling. Effective date is the exact date that a released change takes effect to the shop floor workshop. Effectivity date is the expected date that decision makers plan for the change to take effect. In normal situations, multiple disciplines, such as design and development, purchasing, shop floor workshop, quality control, and so on, are involved in making a change decision on when a change is to become effective. In this paper, a linear programming effectivity decision model is proposed to concurrently support changes of design scheduling, and production planning and scheduling when an engineering change occurs. The proposed model succeeded in solving an integration problem of design scheduling, production planning and shop floor scheduling.     

Extended structured adaptive supervisory control model of shop floor controls for an e-Manufacturing system

The high cost and long development cycle of shop floor control systems and the lack of true system integration capabilities are identified as one of the most challenging obstacles in deploying e-Manufacturing systems. Overcoming these obstacles is essential for manufacturers to execute a make-to-order business model in order to stay competitive and remain profitable in the future. We propose a formal method to generate the desired control trajectories and provide true integration mechanisms for shop floor control systems. Using the proposed architecture can result in the development of an e-Manufacturing system capable of achieving a substantial reduction of both the high cost and long development cycle currently required to engineer shop floor control systems. By taking advantage of both the linear growth of the complexity function in a structured adaptive supervisory control model and the information-centric characteristics of a virtual production line in a manufacturing execution system, a formal model, which we call an extended structured adaptive supervisory control, for a discrete manufacturing system is introduced. A shop floor control system based on the extended structured adaptive supervisory control model is built for an industrial testbed system. The shop floor control system is fully tested and evaluated.     

The impact of priority rule combinations on lateness and tardiness

Several recent studies have explored the concurrent deployment of different priority rules at different processing stages of a manufacturing system. This study investigates the same issue by combining three popular simple priority rules with a combinatorial rule. In a three-stage flow shop, these rules are combined into 64 combination schemes and their performance compared under two shop load levels with two due date setting methods. The performance criteria considered are: mean lateness; mean tardiness; maximum tardiness; and per cent of tardy jobs. The results indicate that the rule combinations are a better strategy than their pure forms when various performance measures are jointly evaluated. Particularly, selected combinations of the Modified Shortest Processing Time, Shortest Processing Time, and the Earliest Due Date rules appear to be very effective. While the extent of shop load level shows little impact on the relative performance of the schemes, the endogenous method of due date setting consistently yields better results than the exogenous method.     

An order release mechanism for a flexible flow system

Order release is where orders are released to the shop floor for processing. An Order Release Mechanism (ORM) can control order release, such that orders are selectively released so as to improve shop management and performance. This paper reports on the development of an ORM for a specific Flexible Flow System (FFS), where the primary criterion in running the system is flow time. The ORM determines what order to release when, and makes the routeing decisions for each order. Using a simulation model, the performance of the FFS with the ORM was compared to present practice without an ORM and an ‘off-the-shelf’ ORM from the literature, namely CONWIP. It is found that the ORM not only improves the mean time that orders spend in the shop under all load conditions, but also reduces the variance of this measure. It would be expected that the total time that orders spend in the system increases with the ORM, at least compared to immediate release, but at high load levels it is found that this measure also decreases. An analysis of variance suggests that the choice of ORM is more important than the choice of the dispatching rule used on the shop floor.     

Lead time prediction in unbalanced production systems

Whether job due dates are set internally or externally, it is critical for the shop floor controller to be able to accurately predict job lead times. Previous research has shown that utilizing information on the congestion levels along a job's route is more valuable than overall shop congestion levels when predicting job lead times. While this information is easily attained in a simulation model, in industrial applications the task may be considerably more difficult, especially when lot splitting is used to accelerate material flow. We examine the effectiveness of three lead time estimation procedures which utilize different shop information in bottleneck-constrained production systems where lot splitting is practiced under a variety of experimental conditions. The results indicate that accurate lead time estimates can be obtained using information pertaining solely to the bottleneck work centre when the bottleneck is at an entry work centre. This offers operations managers a substantial ease in implementation over previously reported methods. The results also show that the operations managers interested in accurately estimating job lead times are well advised to take advantage of the excess capacity at non-bottleneck work centres by performing additional setups, and take measures that reduce bottleneck shiftiness.     

Alternative order release mechanisms: a comparison by simulation

Releasing mechanisms are the set of rules which determine when matured orders i.e. those confirmed orders for which materials and tools are available, are dispatched to the shop floor so that processing can commence. This paper presents a simulation in which three such mechanisms are compared with a scenario in which matured orders are automatically released. Two of the mechanisms assume that the set of jobs to be released is given and that capacity cannot be adjusted. In contrast, the third rule allows capacity to be adjusted both as new jobs enter the system and as they are released to the shop floor. The simulation study shows that the latter rule is the best releasing rule under delivery performance and workload measures, but is slightly worse under the workload balance measure. This high performance is achieved at a relatively low cost in terms of increased capacity. In comparison with a scenario in which mature orders are automatically released to the shop, the latter rule performs better under all criteria except the proportion of tardy orders. It is concluded that further research is needed to show whether order release mechanisms can improve all delivery criteria as well as workload criteria.     

工期不确定的模具车间柔性Flow-shop调度问题

由于模具制造属于非重复性单件订货生产,模具加工的任务工期具有较强的不确定性,导致生产调度混乱。为制定合理可行的生产调度方案,建立了任务工期离散概率模型,以最大完工时间的期望值最小为目标,建立不确定工期柔性Flow-shop调度模型;在遗传算法交叉、变异等操作中融入模拟退火操作,将遗传算法的全局搜索能力与模拟退火算法的良好局部搜索能力相结合,设计了不确定工期的柔性Flow-shop调度问题混合遗传模拟退火算法。利用混合遗传模拟退火算法对调度模型进行求解,通过仿真实验表明,该研究对于解决工期不确定的模具车间柔性Flow-shop调度问题是行之有效的。     

EGS环境下的订单分配优化分析

将在线与店内顾客购物冲突引起的通道拥塞问题简化为排队等待时间优化问题,以订单在店内的最大等待时间最小化为目标,利用M|M|1排队系统建立了排队论优化模型.算例分析显示:在最优解中,该模型试图令所有的商店均达到工作载荷均衡,从而在配送预算范围内的任意时刻拥塞程度均可达到最小化.在敏感度分析中,最优等待时间与成本预算和总利用率两个变量密切相关,即当配送预算和总利用率达到某个特定值后,再次下降时,等待时间将分别发生显著增加和减少,这将对系统的绩效产生负面影响.     

Impact of sequence-dependent setup time on job shop scheduling performance

In a production system using multi-purpose and flexible machines, reducing setup time is an important task for better shop performance. Numerous cases were reported about successful reduction of setup times by standardization of setup procedures. However, setup times have not been eliminated and remain an important element of real production problems for production systems such as commercial printing, plastics manufacturing, metal processing, etc. It is especially critical when the setup time is sequence dependent. In this situation, shop performance cannot be effectively improved without the aid of an appropriate scheduling procedure. Review of the past studies shows that there has not been a significant amount of research done on the scheduling procedure for a dynamic job shop with sequence dependent setup times. This paper investigates the job shop scheduling problems that are complicated by sequence-dependent setup times. The study classifies and tests scheduling rules by considering whether setup time and/or due date information is employed. These scheduling rules are evaluated in dynamic scheduling environments defined by due date tightness, setup times and cost structure. A simulation model of a nine machine job shop is used for the experiment. A hypothetical, asymmetric, setup time matrix is applied to the nine machines.     

Integrating load-based order release and priority dispatching

Workload control (WLC) is a well-established production control concept for job shops that put primary emphasis on load-based order release. Recent advances in load-based order release research have led to an improved delivery performance at reduced shop floor workloads. But although order release is the primary focus of WLC research, it must be coupled with priority dispatching on the shop floor if order progress is to be regulated. Prior simulation research suggests that load-based order release methods should only be coupled with simple dispatching rules because other, more powerful rules can conflict with the functioning of the release method. Yet, recent empirical research suggests that powerful priority dispatching rules – such as due date-oriented dispatching rules – are in fact needed for a high level of delivery performance to be obtained in practice. This paper focuses on overcoming the conflict between order release and dispatching, so load-based order release can be combined with due date-oriented dispatching. Preliminary analysis reveals that part of the conflict is because existing due date-oriented dispatching rules overcompensate for schedule deviations that occur when orders are either released earlier or later than planned. Two alternative new dispatching rules based on an improved method of determining operation due dates are then developed to better account for schedule deviations and overcome the conflict with load-based order release. Further improvements in delivery performance are obtained, while the large workload reductions achieved by recently developed load-based order release methods are retained.     

Layout design in fractal organizations

Today's complex, unpredictable and unstable marketplace requires flexible manufacturing systems capable of cost-effective high variety–low volume production in frequently changing product demand and mix. In fractal organizations, system flexibility and responsiveness are achieved by allocating all manufacturing resources into multifunctional cells that are capable of processing a wide variety of products. In this paper, various fractal cell configuration methods for different system design objectives and constraints are proposed. These parameters determine the level of interaction between the cells, the distribution of different product types among the cells and the similarity of cell capabilities. A tabu-search-based method is proposed to optimize the product distribution to the cells and the arrangement of machines and cells on the shop floor. This optimization is performed for different fractal cell configuration methods and cell quantities. The quality of the resulting shop floor layouts is measured in terms of resource requirements and material movements. The results indicate that in fractal layouts, a trade-off is required between machine quantities and material travelling distance. It was generally possible to reduce travelling distances by increasing the degree of optimization on machine layout and product distribution for a specific product demand and mix.