The threshold value of a quality index for formation of cellular manufacturing systems

The superiority of cellular manufacturing to job shop manufacturing has been questioned by a number of simulation studies. The initial structure of the machine-part matrix seems to play an important role in the failure of cellular manufacturing systems in these studies. In this paper a grouping measure called ‘quality index— QI’ will be used to evaluate the relationship between the quality of a machine-part matrix and the performance of the corresponding cellular manufacturing system. A simulation study will be conducted to demonstrate how the procedure can be used to determine the threshold value for QI beyond which the cellular manufacturing system outperforms the corresponding job shop manufacturing system.     

A comparison of focused cellular manufacturing to cellular manufacturing and job shop

The purpose of this study is to compare a focused cellular manufacturing environment with traditional cellular manufacturing, and job shop environments. We define focused cellular manufacturing as a configuration scheme that groups components by end-items and forms cells of machines to fabricate and assemble end-items. In addition, this research includes three levels of batch sizes and two levels of set-up times in its performance criteria which few researchers in this area have done. The results indicate that the focused cellular manufacturing scheme has a batching advantage. This advantage dominated the balanced machine utilization benefit of the job shop configuration scheme and out weighed the set-up time reduction advantage of the cellular manufacturing scheme for average end-item completion times and average work-in-process inventory levels. The cellular manufacturing and job shop schemes overcame the batch size advantage only when batch sizes were small or set-up times were large.     

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform. The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors. The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors: early delivery date and fulfillment of processing as many orders as possible. The genetic algorithm (GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem (JSSP) by comparing with the real-world data from a textile weaving factory in South Korea. The proposed platform will provide producers with an optimal production schedule, introduce new producers to buyers, and eventually foster relationships and mutual economic interests.     

Recent development of cellular manufacturing systems

Cellular manufacturing system has been proved a vital approach for batch and job shop production systems. Group technology has been an essential tool for developing a cellular manufacturing system. The paper aims to discuss various cell formation techniques and highlights the significant research work done in past over the years and attempts to points out the gap in research.     

Capability-based distributed layout approach for virtual manufacturing cells

It is shown in the literature that in highly volatile manufacturing environments functional job shops and classical cellular manufacturing systems do not perform well. Classical cellular manufacturing systems are very sensitive to changing production requirements due to their limited flexibility. In order to adapt cellular manufacturing systems to volatile manufacturing environments, the virtual cellular manufacturing concept was proposed in the 1980s by the National Bureau of Standards in USA. This concept is similar to group technology where job families are processed in manufacturing cells. The main difference between a virtual cell and the classic cell is in the dynamic nature of the virtual manufacturing cell; whereas the physical location and identity of classic cell is fixed, the virtual cell is not fixed and will vary with changing production requirements. The virtual manufacturing cell concept allows the flexible reconfiguration of shop floors in response to changing requirements. In the literature, the formation and scheduling process of virtual cells are clearly explained and researched in detail. However, the layout issue is not addressed entirely. Virtual cells are generally formed over functionally divided job shops. Forming virtual cells over a functional layout may adversely affect the performance of a virtual cellular manufacturing system. There is a need to search for different layout strategies in order to enhance the performance. The distributed layout approach may be a better alternative for virtual cellular manufacturing applications. In this research paper, a novel capability-based approach is proposed for the design of distributed layouts. A simulated annealing based heuristic algorithm is developed from the distributed layout. The proposed approach is tested with a problem with real data. An example is also shown in order to give an idea about the superiority of a capability-based distributed layout over the functional layouts in forming virtual manufacturing cells.     

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.     

Investigating the factors influencing the shop performance in a job shop environment with kanban-based production control

In this paper, we intended to explore three research questions related to applying just-in-time (JIT) manufacturing techniques in a job shop environment with the kanban-based production control. Simulation experiments were performed and the results were analysed using a statistical method, planned comparison. We found that the adoption of cellular layout in a job shop with the kanban system should only be considered if the amount of setup time reduction achievable through cellular manufacturing is medium to large; otherwise, functional layout should be adopted. In addition, moving parts in batches within cells was found to be more advantageous than moving parts one piece at a time. Only minor effects of material handling speed on the shop performance were identified.     

Real-time deadlock-free control strategy for single multi-load automated guided vehicle on a job shop manufacturing system

An unmanned automated job shop manufacturing system with a single multi-load automated guided vehicle, which traverses around a single-loop guidepath, is considered in this work. This type of shop design is often used as an independent sector of some complex AGV layouts, such as tandem, segmented bi-directional single-loop and divided configurations. The type of multi-load vehicle is a good alternative against using more single-load vehicles to serve a higher transportation demand. To an unmanned automated manufacturing system, the management of finite system resources, e.g. finite input/output queuing space and transporting carriers, plays a vital role in avoiding system deadlocks and machine blockages. The proposed control strategy for a single multi-load vehicle uses global shop real-time information to achieve the objectives: avoid shop deadlocks caused by inappropriate job movement as well as satisfy the system transport requirement. The efficiency of the proposed vehicle control strategy and the other two expanded strategies under various parameter designs are verified by computer simulation.     

Incorporating human resources in group technology/cellular manufacturing

In today's fiercely competitive marketplace, firms are looking for ways to improve their profitability. Computer simulation studies comparing group technology/cellular manufacturing (GT/CM) to job-shop layouts addressed this topic. However, each study ignored some of the most basic operating conditions said to give GT/ CM its advantages: this simulation study incorporates some of those operating conditions like human resource issues, such as learning and labour constraints, in a comprehensive setting. The results indicate that GT/CM significantly outperforms the job shop layout in almost every environmental setting.     

On the performance of hybrid multi-cell flexible manufacturing systems

Industrial experience has shown that it is virtually impossible to implement a large-scale flexible manufacturing system (FMS) without using the group technology manufacturing concept. However, grouping machines into product cells can limit the FMS flexibility. Thus when the production cells are not completely disjoint, problems under multi-cell flexible manufacturing systems (MCFMS) can be caused by changes in job mix and demand which lead to a workload imbalance both between cells and between machine centres within the same cell. The problems can be mitigated and shop performance improved by transferring workloads from a congested machine centre in one cell to an alternative, less congested machine centre in another cell. Such inter-cell workload transfer results in a hybrid MCFMS which is a cross between a parts similarity-based MCFMS and a process similarity-based MCFMS. Results of a simulation study carried out by the author show that inter-cell workload transfer is very effective in improving shop performance. This paper briefly describes the simulation study and discusses the implications of its results for the design and operation of FMSs. The operational viability, and economic feasibility of hybrid MCFMSs are also discussed in the paper.     

Job shop control

The paper defines batch or job shop production and reviews the functions of production planning and production control in a job shop manufacturing situation. It argues that the separation of planning and control has resulted in the artificial isolation of the sequencing problem in job shop research. It attempts to redefine the production control function for a job shop, now called job shop control, and discuss the activities it involves. The major decision-making problems associated with job shop control are highlighted and an objective function of costs to aid in management decision making is evolved.     

Analysis of group technology scheduling heuristics

The need for increased productivity in small batch manufacturing has recently brought focus to the topics and concepts of group technology. The results of a simulation analysis of the use of three job shop simulation scheduling rules which focus on inducing efficiency in the shop is presented. Two of these rules show significant gains in efficiency and, unexpectedly, they also show significant gains in effectiveness. These rules, in effect, induce the efficiency gains expected with group technology implementation.     

基于仿真的单元化制造系统层次建模技术研究

参考ISO和NBS的企业自动化系统的层次模型,自顶向下将单元化制造系统依次划分为车间层、单元层和设备层,降低了模型的复杂性.建立了基于Petri网的层次模型,将其转化为eM-Plant仿真模型,提高了模型的重用性.对某生产车间,建立了层次化的仿真模型,使用不同的调度策略进行仿真,试验证明了模型的高效性和合理性.     

A set partitioning based heuristic procedure for incremental cell formation with routing flexibility

One of the important issues regarding the implementation of cellular manufacturing relates to deciding whether to convert an existing job shop into a cellular manufacturing system comprehensively in a single go, or to convert in stages incrementally wherein the cells are formed one after the other taking the advantage of experiences of implementation. In this paper, a heuristic method based on iterative set partitioning is proposed for incremental cell formation where part operations can be processed on alternative machines. The objective is to minimize cycle time for a given number of workstations. The proposed method is numerically compared with the existing branch and bound technique and another heuristic algorithm based on multistage programming. It is found that the proposed method requires significantly less computational efforts to yield the optimal solution.     

A simulation comparison of group technology with traditional job shop manufacturing

A simulation model of an actual job shop was used to compare group technology with traditional job shop manufacturing. The experiment compared shops which had four different layouts, designed to emphasize different features of traditional job shops and group technology shops, and four distributions of demand for end items. The group technology shops exhibited superior performance in terms of average move time and average set-up time. The traditional job shops had superior performance in queue related variables (average queue length, average waiting time, work-in-process inventory, etc.). This was caused by group technology's dedication of machines. The effects of the queue related variables outweighed the effects of average move time and average set-up time: the average flow time was shorter in the traditional job shop than in the group technology shops.     

Manufacturing systems with forbidden early shipment: Implications for setting manufacturing lead times

The authors compare two well-known methods for setting manufacturing lead times (flow allowances) in a general job shop when early shipment of completed jobs is forbidden. One of the methods for setting a job's allowance is to make it proportional to the total processing time for the job. This method, referred to as TWK., is compared to a second method PPW. With the PPW method, a job's allowance is obtained by adding to the total job-processing time an allowance for waiting that is proportional to the number of operations that the job requires. The two allowance methods are compared using a computer simulation of an 8-machine job shop. The model is unique in that jobs are not permitted to leave the shop early. This feature of forbidding early shipment (FES) complicates the comparison between allowance methods because it draws the issue of finished-order inventory management into the analysis. Results of computer simulations over a wide range of average due-date difficulty suggest that TWK is the dominant procedure by virtue of providing both tower tardiness and lower inventory.     

Comparing functional and cellular layouts: A simulation study based on standardization

In the last decade, over two dozen simulation studies have focused on comparing cellular and functional layouts. The results reported by these studies vary widely, however. This remains true even when the key performance measure is flow time. These variations reflect the disparate manufacturing and operating environments, as well as differences in parts demands, set-up economies, overall loads and other factors. This work attempts to reduce the sources of variation due to different operating assumptions while retaining the variability associated with differences in part mix and demand characteristics. Instead of focusing on a single data source, this study uses a test bed of six problems extracted from the literature and ensures they share the same operational rules. The simulation results show that conversion to CMS can reduce flow times (relative to the job shop configuration) consistently across all data sets, provided the same operating rules and ranges for key parameter are used. We investigate the reduction in flow time while controlling for the key factors of set-up reduction, overall load on the system and batch size. We also assess the benefits of using transfer batches as a further factor in reducing flow time. Our overall conclusion is that set-up reductions in cells can overcome pooling losses, even under the conservative assumptions where batch size remain unchanged and the material transport times in the job shop are assumed to be negligible.     

Coordinating batching decisions in manufacturing networks

Family-based dispatching heuristics aim for improving job flow times by reducing time spent on set-ups. They realise set-up efficiencies by batching similar types of jobs. By their intuitiveness and the simplicity of their decision logic, they may contribute to an easy to implement and viable strategy in many practical settings. Similar to common dispatching rules most existing family-based dispatching heuristics are myopic, i.e. their decision scope is restricted to a single manufacturing stage. Hence, they neglect opportunities for improving shop performance by coordinating batching decisions with other manufacturing stages. Case examples from industry underpin the need for exploring these opportunities. We do so by studying a simple two-stage flow shop, entailing a serial and a batch stage. To facilitate shop coordination we propose extensions to existing family-based dispatching heuristics. Extended heuristics seek to further increase set-up efficiencies by allowing for upstream job re-sequencing, and pro-active set-ups, i.e. set-ups that may be initiated prior to the arrival of a job. Outcomes of an extensive simulation study indicate significant performance gains for extended heuristics vs. existing heuristics. Performance gains are largest for moderate and high set-up to run-time ratios.     

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.     

An efficient priority rule for scheduling job shops to minimize mean tardiness

The ability to respond quickly to customer demands is a key factor in successfully competing in today's globally competitive markets. Thus, meeting due dates could be the most important goal of scheduling in many manufacturing and service industries. Meeting due dates can naturally be translated into minimizing job tardiness. In this paper we present a priority rule for dynamic job shop scheduling that minimizes mean job tardiness. The rule is developed based on the characteristics of existing dispatching rules. With job due dates set by the generalized total work content rule, the computational results of simulation experiments show that the proposed dispatching rule consistently, and often, significantly outperforms a number of well-known priority rules in the literature. The proposed rule is also robust being the best or near-best rule for reducing the mean flowtime, for all the shop load levels and due date tightness factors tested.