Seru Production System Research Articles

An integrated simulation-data envelopment analysis approach for impact of line-seru conversion

This study aims to design an innovative method to evaluate the effects of line-seru conversion in terms of various environmental and economic performance criteria. For this purpose, an integrated approach using simulation and data envelopment analysis (DEA) assisted with various performance criteria is proposed for the first time. The simulation studies involve diverse scenarios for the production of a circuit breaker on assembly lines balanced with RPW and COMSOAL heuristic algorithms, and seru production systems. The simulation results indicate a significant increase in labour effectiveness and station/yatai utilization ratios, complete removal of WIP inventories, which led to a growth in productivity up to 43.29%. The number of workers, carbon-dioxide emission, required work-space, training costs of workers and equipment cost data were incorporated in the results to evaluate the applicability and relative efficiency of the developed scenarios by use of the CCR and BCC models of DEA. The calculated scale efficiency shows that the scenarios for assembly lines balanced with COMSOAL and the seru-based scenarios were both fully effective. The seru production system scenario with 6 yatais was the most effective scenario due to reduced task times after conversion. Also, line-seru conversion proved to be advantageous despite the high training costs.

Order sequencing for a bucket brigade seru in a mass customization environment

In a mass customization environment, customer demands may change dramatically, which challenges conventional production systems. Seru production system (SPS) is a key to deal with changing varieties and fluctuated volumes. A bucket brigade seru is a divisional seru, in which partially cross-trained workers are organized as a bucket brigade. This paper studies an order sequencing problem for a bucket brigade seru with the objective of minimizing makespan. Dynamic behavior of a bucket brigade seru is modeled. Properties of the problem are presented that alternating the processing sequence of some products does not affect makespan. These properties provide factory managers with flexibility to adjust products processing sequence in case of changes in customer demands or lacks in materials. Six local search algorithms are developed using these properties, which combine three policies of selecting products in the operations of swap and insert. A bucket brigade local-search-based differential evolution (BLaDE) algorithm is proposed for the addressed problem, which may incorporate one of the six local search algorithms according to various scenarios. More than 14,000 instances are generated to evaluate the algorithms in terms of effectiveness, convergence, and stability. Experimental results demonstrate the superiority of BLaDE in volatile markets.

A nested partitioning-based solution method for seru scheduling problem with resource allocation

This paper investigates the production scheduling problems of allocating resources and sequencing jobs in the seru production system (SPS). As a new-type manufacturing mode arising from Japanese production practices, seru production can achieve efficiency, flexibility, and responsiveness simultaneously. The production environment in which a set of jobs must be scheduled over a set of serus according to due date and different execution modes is considered, and a combination optimization model is provided. Motivated by the problem complexity and the characteristics of the proposed seru scheduling model, a nested partitioning method (NPM) is designed as the solution approach. Finally, computational studies are conducted, and the practicability of the proposed seru scheduling model is proven. Moreover, the efficiency of the nested partitioning solution method is demonstrated by the computational results obtained from different scenarios, and the good scalability of the proposed approach is proven via comparative analysis.

Flexible matching of multi-skilled workers and operation units in the hybrid rotating seru production system: An optimization model-based method

Flexible matching of multi-skilled workers and operation units in the hybrid rotating seru production system: An optimization model-based method

An effective two phase heuristic for synchronized seru production scheduling and 3PL transportation problems

This paper concerns with synchronized seru production scheduling and transportation problems, in which seru is a new production mode deriving from Japanese electronic assembly industry. In order to realize fast response in seru production system (SPS) according to the just-in-time principle, the decision of seru production scheduling and transportation is considered synchronously. Without loss of generality, the transportation works are assumed to be in charge by a third-party logistics (3PL) company. The intractability of synchronized seru production scheduling and 3PL transportation problem is confirmed by analyzing the properties, and the upper and lower bounds of makespan are provided along with developing the linear program compress model. Accordingly, an effective two phase heuristic algorithm is designed, in which the first phase uses three moves and two swaps to obtain a locally optimal permutation, and the second phase employs the shortest path algorithm based on 4-opt strategy to form the optimal synchronized schedule. Finally, computational experiments are conducted to demonstrate the efficiency of proposed two phase heuristic algorithm, and empirical observations are also reported.

Lot streaming in workforce scheduling problem for seru production system under Shojinka philosophy

Drawing upon efficiency, flexibility, and responsiveness, this study attempts to explore the impact of lot streaming and worker assignment strategies in the workforce scheduling problem for the seru production system (SPS), which has emerged as an alternative to traditional assembly lines. The addressed problem involves decisions related to employee timetabling, lot splitting, and sublot scheduling. While scheduling problems in SPS have been extensively researched in recent years, the workforce scheduling problem has not received much attention in the literature. Moreover, to our best knowledge, it has not been investigated with the sublot division methodologies within the context of SPS so far. To bridge this gap, a generic novel optimization model is developed with the objective of minimizing average flow time (AFT) by integrating lot streaming and the Shojinka philosophy through modules within the model. Additionally, the structural properties of the problem are examined, and lower and upper bound formulations are developed based on these properties. Given the NP-hard nature of the problem, customized approaches based on the genetic algorithm (GA) are proposed for solving large-sized problems, considering the Shojinka and division methodologies. The computational results clarify that achieving Shojinka with variable sublots division methodology significantly reduces the AFT objective. Furthermore, the findings confirm that adapting variable sublots in operational scenarios results in a drastic improvement in system performance, regardless of the implemented worker assignment strategy.

Coordinated seru scheduling and distribution operation problems with DeJong’s learning effects

Inspired by the advantage of just-in-time philosophy, zero-inventory is common in many industries, especially those need fast response with a short lifespan. Accordingly, this paper focuses on coordinated production scheduling and distribution operation problems considering workers’ DeJong’s learning effects in seru production system (SPS), in which seru is a relatively new-type manufacturing mode originating from Japan and can achieve fast response in practice. Two variants of coordinated seru scheduling and distribution operation problems are studied, and the corresponding 0–1 integer programming model is formulated. By analyzing the mathematical property, the polynomial computation time of the former is able to be determined, and an intractability and NP-hardness proof is provided for the latter. The dynamic programming-based exact algorithm and the heuristic ant colony optimization algorithm are developed respectively. Computational experiments are conducted finally, and a series of experimental results indicate that the Dejong’s learning effect has a significant influence on coordinated seru scheduling and distribution operation problems, meanwhile a remarkable benefit (the average improvement is 16.97%) can be achieved by the coordinated production scheduling and distribution operation in SPS.

Bi-objective energy-efficient scheduling in a seru production system considering reconfiguration of serus

Owing to movable workstations, light equipment and multi-skilled workers, serus can be constructed, modified and dismantled rapidly. Such an advantage enables serus to be reconfigured in response to frequent changes in product types. As different configurations lead to different processing time and energy consumption, an important issue is how to arrange a best configuration of seru with the tasks assigned to it. To this end, we focus on solving the task dispatching, product sequencing and seru reconfiguration problems simultaneously considering economic and environmental performance. A bi-objective mathematical model with objectives of minimizing the total earliness and tardiness and the total carbon dioxide emission is formulated. The problem is proved to be strongly NP-hard, and a non-dominated sorting genetic algorithm-II (NSGA-II) is developed. The algorithm is tested by a medium-size numerical example and ninety sets of large-size problems. Based on the computational results, the impact of configuration flexibility and the relation between processing time and energy consumption on the value of objective functions is analyzed.

A cooperative coevolutionary algorithm with problem-specific knowledge for energy-efficient scheduling in seru system

With the increasingly prominent issues about the global environment, green manufacturing has become a research hotspot. To cope with the turbulent market environment, seru production system (SPS) as an innovative manufacturing mode has attracted much attention, yet energy consumption is still rarely considered in SPS. This paper addresses an energy-efficient scheduling problem in seru system (EESPSS) with the minimization of energy consumption and makespan simultaneously. It contains three coupled subproblems, i.e., worker-seru assignment, batch-seru assignment, and worker-task assignment. To tackle the problem effectively, we build a mathematical model and design a cooperative coevolutionary algorithm (CCA) with three phases: hybrid initialization, multi-population cooperation exploration with feedback, and knowledge-guided greedy search. Firstly, a knowledge-based rule is designed to produce high-quality initial solutions. Secondly, multi-population cooperation is proposed to achieve subproblem co-optimization. Specifically, two populations are constructed for adjusting worker-seru assignment and batch-seru assignment and an elite population is applied for information sharing by individual migration. Moreover, to enhance algorithm search capability, a feedback strategy is designed for operator selection and population size adjustment. Thirdly, to exploit the nondominated solutions effectively, several properties are derived as the problem-specific knowledge to design a greedy search for the batch-seru assignment and worker-task assignment. Numerical tests and statistical comparisons are carried out, which demonstrate the effectiveness of the specific designs of the CCA and its superiority to the existing algorithms in solving the EESPSS.

A bi-level nested heuristic algorithm for divisional seru order acceptance and scheduling problems

This paper focuses on the order acceptance and scheduling problem with worker–operation assignment considering the precedence constraint in the divisional seru production system, which is a new production type derived from Japan and can achieve responsiveness, flexibility, and efficiency simultaneously. With limited production capacity and workers with different skill sets and skill levels, the problem considered in this paper includes three sub-problems: (i) assigning workers to each seru, (ii) making the order acceptance and scheduling plan, and (iii) allocating workers to operations for each order. Subsequently, a nonlinear integer programming model is established for improving the total net revenue, and a bi-level nested heuristic algorithm is designed due to its intractable computation. Computational experiments are made finally, and results show that the objective value obtained by the bi-level nested heuristic algorithm is 3% better than the bi-level genetic algorithm, and the running time is shortened by 99.23%. That is, the proposed bi-level nested heuristic algorithm can achieve better results and higher efficiency for divisional seru order acceptance and scheduling problems.

To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights

The maximum tardiness reflects the worst level of service associated with customer needs; thus, the principle that seru production reduces the maximum tardiness is investigated, and a model to minimize the maximum tardiness of the seru production system is established. In order to obtain the exact solution, the non-linear seru production model with minimizing the maximum tardiness is split into a seru formation model and a linear seru scheduling model. We propose an efficient cooperative algorithm using a genetic algorithm and an innovative reinforcement learning algorithm (CAGARL) for large-scale problems. Specifically, the GA is designed for the seru formation problem. Moreover, the QL-seru algorithm (QLSA) is designed for the seru scheduling problem by combining the features of meta-heuristics and reinforcement learning. In the QLSA, we design an innovative QL-seru table and two state trimming rules to save computational time. After extensive experiments, compared with the previous algorithm, CAGARL improved by an average of 56.6%. Finally, several managerial insights on reducing maximum tardiness are proposed.

Configuring a seru production system to match supply with volatile demand

Configuring a seru production system to match supply with volatile demand

An exact quadratic programming approach based on convex reformulation for seru scheduling problems

AbstractMotivated by a practical production scheduling problem at a factory, this article studies scheduling problems in seru production system (SPS). Seru is a relatively new‐type production mode originating in Japan and has brought inspiring benefits to production practice. Following the just‐in‐time philosophy of SPS, the objective of seru scheduling problem is to minimize the sum of earliness and tardiness penalties. Two common due date types of job are considered, and the seru scheduling problem is formulated as a 0–1 quadratic programming model with linear constraints that is then reformulated using convex reformulation methods to ensure convexity. Computational experiments are implemented. Experimental results indicate that the proposed exact solution method can obtain approximate optimal solutions efficiently and effectively for seru scheduling problems.

Smart seru production system for Industry 4.0: a conceptual model based on deep learning for real-time monitoring and controlling

ABSTRACT Seru production system is an innovative assembly system and combines the flexibility of job shop production and high efficiency of assembly lines. This production system easily adapts to product replacements, reduces product wastages by eliminating work-in-process inventories and delay time, and provides enterprises with a competitive advantage through reducing the operating costs, required workforce, and space. Besides these advantages, the disadvantage of seru type production is that, in assembly lines, specific tasks are completed in specified stations, whereas in seru production, the tasks required for the assembly of a product are completed in a yatai by a cross-trained worker. This, in turn, results in a higher risk of production errors. Accordingly in this research, a conceptual model is proposed to monitor and control multiple factors of the production process like worker, environment, assembly tools, ergonomics, storage and inventory and issue warning for prevention of process and quality errors in seru production by use of advanced analytics based on deep learning. In addition to providing support to the worker, the proposed Smart Seru Production System Model will assist system participants in obtaining and understanding data about the production processes and reacting quickly based on this information.

An effective heuristic based on 3-opt strategy for seru scheduling problems with learning effect

This paper is concerned with the scheduling problem in a new-type seru production system by consideration of DeJong's learning effect to minimise the total weighted completion time, so as to achieve efficiency, flexibility, and fast responsiveness to cope with the current volatile market. A combinatorial optimisation model is constructed and then reformulated to a binary quadratic assignment program. Accordingly, after presenting the necessary and sufficient condition for the locally optimal solution, a tabu search with strategic oscillation based on 3-opt as a diversification strategy is designed as the solution approach. A set of test problems are generated, and computational experiments with large-scale cases are made finally. The results indicate that the proposed heuristic algorithm is promising in solving seru scheduling problems and has a good performance in term of solution quality, efficiency, and scalability.

Multi-skilled worker assignment in seru production system for the trade-off between production efficiency and workload fairness

PurposeThe paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS).Design/methodology/approachThree approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem.FindingsNumerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers.Practical implicationsSPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS.Originality/valueThe study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed.

A mathematical model for line-seru conversion and scheduling problem in seru production system

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.

Scheduling problem in seru production system considering DeJong’s learning effect and job splitting

Scheduling problem in seru production system considering DeJong’s learning effect and job splitting

Order acceptance and scheduling problem with outsourcing in seru production system considering lot-spitting

Order acceptance and scheduling problem with outsourcing in seru production system considering lot-spitting

Production planning method for seru production systems under demand uncertainty

The seru production system is a new-type manufacturing system arising from Japanese production practices that can simultaneously achieve high efficiency, flexibility, and responsiveness. In this paper, a worker and production quantity allocation optimization method for multi-product seru production systems under demand uncertainty is proposed. To handle such demand uncertainty, the Monte Carlo technique is employed and the optimization problem is formulated as a bi-level mixed-integer linear programming problem. At the first step, a worker allocation problem is solved before demand realization is observed. As the second optimization step, the production quantity allocation is then conducted based on the observed demand realization. The proposed method is applied to several numerical problems to illustrate its effectiveness.