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The increasing market turbulence has impelled leading manufacturers to search the development of alternative production systems supposed to enhance their responsiveness to the market. The purpose of this paper is to discuss the turnaround currently observed in some manufacturing industries in Japan, which are migrating to more human-centred product...
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... among the many possible alternatives, the classic U-shaped line format is a popular cell layout alternative frequently adopted in the implementation of cell production systems. Figure 4 presents three basic patterns of worker allocation for the operation of a U-shaped line comprising nine work stations. As for the size of work cells, Shinohara (1995; compiled data concerning the way operators have been assigned to work under the cell production approach in a sample of 12 plants, in which a total of near 294 work cells had been organized, and reported that a team assigned to such cells is typically comprised of 1 to 12 members. ...
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... In this situation, production modes, such as assembly production line and Toyota production system, cannot quickly respond to the market environment which has the short product life cycle, diversified product varieties and fluctuant demand, because they are mainly oriented to a stable market environment (Yu and Tang 2019). In response to these changes, in 1992, several short conveyor lines were separated from a multi-product conveyor line in a Sony Corporation factory producing 8 mm CCD-TR55 cameras, and a short line, called seru, is responsible for assembling a particular product (Miyake 2006), (Yu et al. 2014). Because seru production system (SPS) could be built, dismantled, rebuilt, and optimized quickly and frequently, so this newtype production mode can not only achieve the high efficiency like the assembly production line, but also meet the specific needs of many varieties and small batches (Liu et al. 2010b). ...
This paper focuses on seru scheduling problems considering the sequence-dependent setup time in seru production system, which is a new-type manufacturing system originated in Japanese production practice recently that can better adapt to the fluctuate market demand. A mixed integer programming (MIP) model with the objective of minimizing the sum of the makespan and the total weighted tardiness is constructed for the seru scheduling problem. The branch-and-bound (B&B) algorithm with two main steps is designed subsequently, where the first step solves the assignment of products to serus, while the second step solves the scheduling optimization in each seru. Finally, the computational experiments and comparative analysis with CPLEX 12.8 are made, and the report of results verifies that the effectiveness and practicability of the proposed MIP and B&B algorithm.
... Due to each AC is highly independent and autonomous, there is no need to consider line balance issues and the overall system performance is not restricted by the weak link. Therefore, this innovative assembly system has shown many benefits in industrial practice, such as increasing productivity, shortening production lead time, reducing work-in-process (WIP) inventory, improving system flexibility and responsiveness (Miyake, 2006;Yin et al., 2018). ...
An assembly cell line (ACL) is one type of cell production practice, derived from the Toyota Production System in the electronics industry and rapidly spread to other fields. In this mode, the conveyor line is divided into assembly cells (ACs) where various parts and tools are placed closer to the workers, enabling them to perform multiple tasks throughout an entire product assembly from start to finish. In this way, ACL allows manufacturers to rapidly configure an appropriate heterogeneous capacity to match heterogeneous demands with diversified customer orders in the high-mix, low-volume (HMLV) environment, which is the spread of the Just-In-Time (JIT) philosophy from the material level to the organization level. However, due to the lack of real-time information sharing in the ACL workshop, especially the up-to-date individual capacity and asynchronous production processes within and between ACs, it is hard to coordinate the heterogeneous capacities of ACs to meet the HMLV demands in a complex manufacturing environment with uncertainties. In this context, this paper proposes a heterogeneous demand–capacity synchronization (HDCS) for smart ACL by using artificial intelligence-enabled IIoT (AIoT) technologies, in which computer vision (CV) is applied for up-to-date capacity analysis of ACs. Based on these, an AIoT-enabled Graduation Intelligent Manufacturing System (GiMS) with feedback loops is developed to support real-time information sharing for the synchronous coordination of the ACL operation, which also provides the basis for the implementation of the HDCS mechanism through a rolling scheduling approach. Finally, a real-life industrial case is carried out by a proof-of-concept prototype to verify the proposed approach, and the results show that the measures on shipment punctuality and production efficiency are both significantly improved.
... It is worth noting that some Japanese leading electronics companies have been combining the strengths from Toyota production system and Sony's one-person production organization to creatively break divide conveyor lines into assembly cell systems, named cell production (or Seru Seisan in Japanese), for better flexibility, responsiveness and costefficiency to cope with the volatile business environments [13][14][15]. Different with the machine-oriented cellular manufacturing based on group technology, the human-centered cell production is derived from the lean production philosophy and has high reconfigurability to support the firms to implement the "Assembly-to-Order" strategy to cope with increasing demand variation [9,16,17]. Due to the unique advantages and successful applications in the electronics industry, cell production has quickly diffused to other industries and countries in various layouts. ...
... (11) and (12) define each possible assignment of the job task to each available AC, and optional means the interval variable may exist or not, and its size is equal to the processing time of the job task if it exists. Eq. (13) defines a sequence variable that represents the possible sequence of job tasks at each available AC. Eq. (14) ensures that only one job task can be performed at an available AC at the same time without overlap, and once starts, it must be finished. Eq. (15) ensures that a setup task with the setup time S v is required when switching to the production of a distinct family v. ...
Manufacturing systems are constantly required to leverage emerging technologies to increase flexibility and responsiveness to meet the diversified demands in an ever-changing market cost-efficiently. Assembly cell line (ACL), derived from the Toyota production system, is firstly sprouted in some leading electronics companies. In this unique layout, the conveyor line is divided into human-centric assembly cells (ACs) and each AC stores a certain number of parts that allow the multi-skilled worker to easily access and complete an entire product assembly on his or her own pace. Therefore, the proper production capacity of ACL can be achieved through frequent changeover and reconfiguration to meet the changing demands, which is an extension of the just-in-time strategy for organization systems (JIT-OS). However, due to the lack of real-time data on the up-to-date capacity of ACs and an efficient coordination mechanism of ACL based on real-time information feedback in dynamic and stochastic manufacturing environments, it is difficult to successfully implement JIT-OS to rapidly respond to the changing demands. This research proposes a real-time data-driven synchronous reconfiguration of smart ACL workshops (Sync-RAS) under the Graduation Intelligent Manufacturing System (GiMS) by leveraging Industry 4.0 (I4.0) enabling technologies. Computer vision (CV) is employed to monitor and analyze the real-time operation process within the human-centric AC. In addition, an appropriate information-sharing architecture with scalability and reconfigurability is developed to coordinate the smooth reconfiguration of the smart ACL workshop in rapid response to the changing demands. Based on these, GiMS is introduced to achieve simple and feasible operational tactics through real-time data-driven information visibility, traceability, and sharing, while a Sync-RAS mechanism is proposed to optimize the reconfiguration of ACL. Finally, a real-life case has been performed to verify and show the benefits of the proposed approach.
... Because the equipment in SERU is compact and movable, SERU reconfiguration can be performed with few auxiliaries in a short time. SERU layout is usually stuck to U-shape to achieve majime (i.e., making the layout of workers, equipment, and tools in a seru as compact as possible), leading to reduced workspace, decreased work-in-process inventories, and less setup time (Stecke et al. 2012;Miyake 2006). ...
... As a result, the same product can be manufactured synchronously in multiple serus, making it possible to provide more volume flexibility (Sakazume 2006;Yin et al. 2017). Furthermore, the capacity of each seru can be easily adjusted by adding or removing stations and workers in accordance with either increasing or decreasing production volume (Miyake 2006). By implementing SERU reconfiguration, the ability to quickly respond to volatile volume is enhanced. ...
... Multi-skilled workers in SERU are cross-trained to cope with broadened tasks and thus enable rapid reallocation of workforce among multiple serus. Therefore, they provide an additional capacity buffer to respond to production volume fluctuation (Miyake 2006). In addition, research has found that setup time reduction can raise firms' capability to cope with small lots production and is regarded as an important source for volume flexibility (Hallgren and Olhager 2009). ...
SERU production system (SERU) demonstrates more manufacturing flexibility in response to volatile market demands. Although many firms have implemented SERU successfully, little research on manufacturing flexibility in SERU exists, especially empirical evidence in the context of Chinese firms. Manufacturing flexibility considers two major dimensions in SERU, namely, product mix flexibility and volume flexibility. We propose a contingency-based framework to explore the source factors of manufacturing flexibility and their impact on firm performance. We examine the moderating effects of both multi-skilled worker turnover and industry type. Using a total of 357 samples from China, we test the hypotheses with structural equation modeling. Our results reveal that both SERU reconfiguration and multi-skilled worker involvement are important sources of product mix flexibility and volume flexibility, and the two kinds of flexibility subsequently improve firm performance. Multi-skilled worker involvement has a stronger impact than SERU reconfiguration on improving manufacturing flexibility. Both multi-skilled worker turnover and industry type are important moderators. We also conduct tests indicating that the mediation effects of manufacturing flexibility are moderated by multi-skilled worker turnover and industry type. Overall, the acquisition of manufacturing flexibility and its impact on performance in SERU are situation dependent.
... The seru production system (SPS), a Japanese cellular manufacturing technique, has gained popularity in Japanese manufacturing companies owing to its high efficiency and flexibil- (Miyake 2006;Stecke et al. 2012;Roth et al. 2016). The SPS involves converting from an assembly line with several workers to one with few workers who can assemble a component/product from start to finish. ...
Owing to its high efficiency and flexibility, the seru production system (SPS), which originated in Japan, has attracted greater attention in management and academic studies. This research focuses on optimizing the configuration for implementing the SPS under an uncertain demand. The study is aimed at formulating a robust production system capable of effectively responding to stochastic demands. The primary issues are determining the amount of skill training required and matching workers with their corresponding skills. A stochastic optimization model is developed to minimize the total expected cost of the system, while considering the costs associated with training, staff shortage, and staff surplus. A heuristic algorithm is developed to solve this problem. Experimental results indicate that, compared to the full-skilled training strategy, appropriate partial skill training (such as the long chain skill training strategy) can yield greater benefits. The total cost and amount of skill training increase with growing differences in the product mix compositions, demand fluctuations, and number of product types. Moreover, the skill level of workers increases with a decrease in training cost and an increase in staff shortage and surplus costs.
... Liu et al. [3] utilized the minimum completion time to solve the flexible job-shop scheduling with loading/ unloading robots, established a mathematical model of the problem, and searched for the scheduling results with a tabu search and greedy algorithm. Miyake [4] used the minimum production cycle to solve the multi-process job-shop scheduling with transport time, constructed a nonlinear mathematical model, and solved the model with improved genetic algorithm, realizing batch scheduling. Du et al. [5] explored the reconstruction and scheduling of multi-stage variable-batch production lines in labor-intensive enterprises, adopted different algorithms for production lines of different scales, and compared the applicable scopes of two joint optimization algorithms with examples. ...
Cloud manufacturing is a new service-oriented efficient and low-consumption agile manufacturing mode integrating information, manufacturing, Internet of Things, and other technologies. One of the key decisions of production enterprises is production task allocation based on cloud robot cell-line, which determines the efficiency and flexibility of the production system and affects various production links, such as job-shop logistics, production planning, and production scheduling. This paper explores the production task allocation, from the angle of the optimal combination of cloud manufacturing resources. First, a mathematical model was established, based on the transport cost of different sub-tasks and the tardiness cost of product delivery, and solved by quantum firefly algorithm (QFA). Next, QFA was proved superior to traditional firefly algorithm (FA), improved FA, and the FA optimized by cat swarm optimization (CSO-FA), in terms of time complexity and spatial complexity. The research enriches the theory and methodology of allocating operation-level cloud manufacturing resources based on cloud robot cell-line and provides decision support to manufacturers, which want to implement operation-level allocation of cloud manufacturing resources based on cloud robot cell-line.
... Each operator in a seru must have the competency to work in different serus. Miyake (2006) and Sakazume (2005) conducted a comparative study between the traditional cellular physical arrangement and seru. However, Kaku (2016) showed that the comparison was not valid both the systems used similar technologies. ...
... Ayrıca, seru sisteminin sadece elektronik sektörde başarı elde edemeyeceğini, aynı zamanda belirlenen koşullar belli bir seviyeye kadar yerine getirildiği taktirde diğer sektörlerde de başarılı bir şekilde uygulanabileceğini belirtmiştir. Miyake [21] çalışmasında, seru üretim sistemini malzeme akış kontrolü, üretim kapasitesi ayarlaması, işçi becerilerinin geliştirilmesi, çalışma ekibinin yetkilendirilmesi, üretim artışı ve ürün özelleştirmesi yönlerinden araştırmıştır. ...
Günümüz rekabet ortamında işletmeler rekabet gücünü artırmak için müşterilerin çeşitli ve kişiye özel taleplerine hızlı ve yüksek verimlilik ile yanıt vermeleri gerekmektedir. Üretim sistemlerinde verimliliği ve esnekliği artırmak amacıyla Amerikan Ford şirketi tarafından ortaya çıkan seri üretim (montaj hattı), Japonya’daki Toyota firması tarafından geliştirilen Toyota Üretim Sistemi (TÜS) ve grup teknolojisine dayalı Hücresel Üretim Sistemi (HÜS) gibi farklı üretim sistemleri geliştirilmiştir. Ancak, dinamik talepler, ürünlerin kısa yaşam döngüsü ve yüksek çeşitlilik nedeniyle montaj hattı ve TÜS’de verimlilik düşmektedir. Sony ve Canon gibi Japon şirketleri, pazar taleplerini karşılamak için bir Japon hücresel üretim sistemi geliştirmişlerdir ve geleneksel HÜS'den ayırmak için bu üretim sistemine seru üretim sistemi adı vermişlerdir. Literatürde seru üretim sistemi ile ilgili az sayıda çalışma yapılmıştır. Özellikle Türkçe literatürde, seru üretim sistemlerini inceleyen sadece iki çalışmaya rastlanmıştır. Bu yönüyle çalışmanın literatürdeki bu boşluğu doldurması hedeflenmektedir. Bu çalışmada seru üretim sistemi ele alınarak bu konuda kapsamlı literatür taramasının yanı sıra montaj hattının seru üretim sistemine dönüştürme (hat-seru dönüşümü) ve siparişlerin toplam gecikmelerini minimize eden çizelgeleme problemleri aynı anda matematiksel bir model olarak önerilmiştir. Modelin çözümü için GAMS paket programı kullanılmıştır ve sonuçlar analiz edilmiştir.
... uniyet ve başarı anlayışı olarak belirlemiştir. Çalışmanın sonucunda ise elde ettiği bulgular; üretim hacmine uyarlanabilir değişikliklerinin ortaya konmasına ek olarak sık model değişiklikleri, çok parçalı küçük boyutlu ürünler, artan üretim, üretim maliyetinde düşüş, ürün kalitesinde iyileşme ve kısaltılmış teslim süresi olarak tespit edilmiştir.Miyake (2006) yaptığı çalışmada, amacı iş takımlarının güçlendirilmesi, çoklu görev ve düşük maliyetli otomasyon kavramlarını ön planda tutarak örnek bir üretim hattını Seru sistemi ile U şeklinde yeniden düzenlemiştir. Bu çalışmanın uygulanmasında belirlenen performans endeksleri; maliyet, yatırım, WIP envanteri, kurulum zaman, organizasyon yeniliği ...
Bu çalışma, 90’lı yılların ortalarından itibaren montaj temelli çalışan işletmelere uygulanmaya başlanan yeni bir üretim sistemi olan seru sistemini tanıtmaktadır. Uluslararası literatürde çok fazla yaygın olmayan bir konu olan seru, ulusal literatüre de ilk kez bu çalışma ile girmektedir. Çalışmada seru üretim sisteminin doğuşu, anlamı, türleri, yararları ve matematiksel modellemesi tanımlanmaktadır. Geleneksel montaj hattının sökülüp nasıl seru sistemine dönüştürüldüğü de detaylı olarak anlatılmıştır. Bu araştırma, hali hazırda geleneksel montaj hattını kullanan işletmelerde uygulanabilmesi için bulundurulması gereken temel özellikleri anlatarak, bundan sonra yapılacak uygulamalı çalışmalara da ön ayak olacağı düşünülmektedir.
... In random allocation, the individuals to trace the target are selected randomly, according to the previously determined number of individuals. In sequential allocation, all individuals are ranked in ascending order of fitness, and the top individuals are selected for the tracing task [19][20][21][22][23][24][25]. In the CSO-FA algorithm, the firefly distribution area is divided into the concentrated area and the dispersed area. ...
This paper attempts to minimize the makespan and cost and balance the load rate of the process scheduling of cloud manufacturing resources. For this purpose, a multiobjective scheduling model was established to achieve the minimal makespan, minimal cost and balanced load rate. Next, the cat swarm optimization (CSO) and the firefly algorithm (FA) were combined into a hybrid multi-objective scheduling algorithm. Finally, the hybrid algorithm was verified through CloudSim simulation. The simulation results show that the algorithm output the optimal scheduling plan in a short time. This research not only provides an effective way to find the global optimal solution, within the shortest possible time, to the process scheduling problem of cloud manufacturing resources with multiple objectives, but also promotes the application of swarm intelligence algorithms in job-shop scheduling problems.
