The global spread of the novel coronavirus infection (hereafter referred to as COVID-19) has caused supply chain disruptions. As a result, many global companies are reviewing vertical and horizontal relationships in their supply chains and emphasizing effective supply chain resilience measures, such as strengthening their business structures, diversifying their production sites and suppliers, and enhancing mutual aid systems with other companies.

This research assesses that the impact that the differences of procurement networks, inventory strategies and components have on carmakers when the external factors in the supply chain, such as the spread of COVID-19, become evident. The objective of the research is to evaluate the different effects of supply chain resilience measures and enable carmakers to supply components stably. For this purpose, a simple simulation model of a supply chain in the automotive industry was constructed and simulated, evaluated and analyzed based on scenarios reflecting the differences of the external factors in the supply chain.

製鉄所内のスラブヤードは動的な環境下でクレーンオペレータによって制卸される．スラブヤード制御の意思決定プロセスのパフォーマンスはオペレータのスキルに依存する．また，オペレータに与える情報に依っても制卸の効率は変わると考えられる，オペレータを効果的に支援するためには，問題の捉え方を表す特徴量を定義・評価し，良い特徴量を提示することが重要である，本論文では，納期に関する情報についての複数の提示方法を提案し，制御タスクのシリアスゲームモデルを用いて被験者実験を行うことで，有効な納期情報の提示方法を明らかにする．

The objective of this study is to use the open-source robot control software ROS, which can reuse code among multiple types of robots, to automatically generate the motions that substitute the robot arm for stocking works in convenience stores, etc. . Currently, it is common to use a single planner when generating actions, however, by switching between planners at waypoints provided by mapping the surrounding environment, we confirm that it is possible to reduce the time to complete actions. The experiment has been conducted with VS-060 from DENSO to verify the generated motions.

Manufacturing simulation is useful for the efficient operation of custom manufacturing line (SPS line), but at this point, methods have not yet been systematized for the construction of human-centered simulation models.

The current ergonomics has not yet developed standard in considering worker well-beings-oriented manufacturing, thus this research aims to support human-centered approach in production systems. In addition, with the advancement of digital twin technology, there is a growing demand for digitization and modeling of humans. Therefore, this study proposes the concept of modeling and simulation of workers in the SPS line work environment based on these ideas.

A lean manufacturing tool called Kanban is used in the communication structure which comprehends the signal and response setup. Industry 4.0 has seen an assertive evolution in recent years as human productivity has increased. The progress, however, makes technology adaption more difficult and convoluted, particularly for complex production lines which lack a real-time notification system, an effective communication system, and appropriate data collection methods which might raise the likelihood of failure. Both the lean methodology itself and the understanding of Industry 4.0 should be incorporated into the communication structure. Therefore, the purpose of this study is to develop Kanban Apps for the Decision Support System (DSS), which is specifically utilised by manufacturing students, professionals in the industry, and practitioners of lean. In-depth discussion is conducted regarding both the primary development process and the verification. The results demonstrate that Kanban Apps can be easily integrated into digital manufacturers, propelling the response time with real-time operational information. The functionality of the Kanban Apps is evaluated by comparing manual calculation and the calculation made using the Kanban Apps. These connections between data analytics from various machines integrated with contemporary gadgets like sensors inducing the cyber-physical system will have a significant impact on boosting productivity through improved decision-making.

Among industry players, the success rate with the adoption of Lean Manufacturing (LM) has been growing significantly year-over-year, by leveraging the Industrial Revolution of 4.0. The boom in Industry 4.0 has resulted in exponential data growth in all fields. This has been possible due to the big data exchange system in real-time, which enables engineers to gain complete control of the system to deal with any forthcoming situation, including data collection and machine control. This scenario also results in competition encouraging the manufacturing industry to grow, thereby increasing the demand pool to cater to the market requirements. However, in real industry, engineers face issue with time, with regards to shortening the notification time when a mistake occurs, which is critical for decision making. Thus, in this review, researchers have tried to find a solution. Simulation can be employed to exploit a new concept of the solution to address complex data-based problem, and concentrate on the decision support system. This research tries to discern and diagnose the gap between the merging of both simulation as well as implementation of LM.

Many quality inspection tasks have been mechanized and automated in the quality control departments of manufacturing companies. On the other hand, manual inspection is essential for products that cannot be automated. For example, the tips of rotary tools used in dental treatment are covered with fine diamond particles by electrodeposition, so no one has the same shape. We have researched inspection support tools for rotary tools using machine learning. However, it has become clear that it is difficult to discriminate samples that are difficult to judge visually with high accuracy, even with machine learning. In this study, we aim to improve the discrimination system by applying filter-based preprocessing to image data.

Production system by advance demand information has the advantages of enabling manufacturers of finished products to respond to diversifying customer needs and to secure a stable and large quantity of various parts with short delivery times, and enabling parts suppliers to provide a stable supply without having to hold huge product inventories. On the other hand, the upstream companies in the production process need to produce and ship products more quickly to meet the delivery date of the final product, which increases uncertainty. Therefore, analyzing effects of variable factors, such as demand fluctuations and the gap between advance demand information and firm-order information, is necessary. In our previous study, we formulated the flow of products and information in a supply chain using advance demand information and constructed a simulator based on these formulations. In this study, we aimed to evaluate and investigate the effects of improving the whole supply chain by adjusting production and inventory quantities in the supply chain using advance demand information. Overtime costs and inventory costs are introduced into the simulator to evaluate the impact. Results of the simulation confirms that front-loaded production is a very effective measure as long as it can be handled, and that overtime work is also effective depending on the condition of late delivery penalties.

需要が大きく変動する工作機械部品について、内示理論を使ったレジリエントな在庫管理法による、手配業務の標準化を進めている。その中で今般、代表的な部品について内示の時系列分析を行い、「内示のブレが定常性をもつ」ことを実証した。長納期部品では、調達数量を早い段階で確定しなければならないために、確定後にも内示の前提が大きく変化するなどの複雑な不確実性を有している。そこで、新たに期別在庫管理目標設定法等を考案し、手配業務の標準手順に織り込んだ。実務に適用し、在庫回転日数を17%削減する効果を得た。

This study deals with the no-buffer job-shop scheduling problem to avoid head-on collisions on bidirectional path for AGV transport schedules in manufacturing plants. The no-buffer job-shop scheduling problem allows schedules in which the resources processed by two products are swapped, but such schedules cause head-on collisions in the bidirectional path in the AGV transport schedule. The situation of head-on collision is analyzed mathematically, and constraint conditions are clarified to the collisions. The results of numerical simulations are examined to show that the no-buffer job-shop scheduling problem with the proposed constraints can give appropriate schedules for AGVs.

Production scheduling problem is one of the typical optimization problems and it has been utilized as a practical problem to be solved in the manufacturing industry. We consider a simultaneous optimization of product input sequence and workforce scheduling for multi-stage, multi-item cell production systems. The optimization problem is formulated as a Resource Constrained Project Scheduling Problem (RCPSP). RCPSP has been studied as a project scheduling problem considering resources such as workers. However, the traveling time of workers to their work stations and the travel time of parts to each work station have not been taken into account in the conventional study. We propose a RCPSP formulation for the scheduling problem for multi-stage, multi-product cell production lines with workforce scheduling considering the travel time of operations and parts. The exact solution of the RCPSP is based on Gurobi, a mathematical programming solver. The effectiveness of the derived solution is confirmed via simulation software (plant simulation).

In this paper, a generalized version of the minimal switching graph (MSG) problem is revisited. Problem MSG is a combinatorial optimization model of via minimization in double-sided circuit boards, and it is defined on a directed bipartite graph with a finite set of items and a finite set of switches. An item and a switch correspond to a via candidate connecting the two faces of circuit board in the initial design and to a wiring cluster, respectively. A turned-on switch means the move of every wire segment of the wiring cluster from its initial face to the other face. In the generalized version, each item has two non-negative profits, and each switch has a positive cost which is paid when the switch is turned on. A solution is a set of turned-on switches, and it is feasible if the total cost of turned-on switches does not exceed a given budget. When a switch is turned on, it changes the direction of each arc incident with the switch from the initial to the opposite. If a feasible solution makes the directions of all the arcs incident with an item identical, then the solution can collect the item and get either profit according to the resulting direction of the arcs. The objective is to maximize the total profit of collected items (which intends to maximize the total weighted number of via candidates to be removed from the initial circuit board design). In this paper, numerical experiments are conducted to demonstrate the solutions based on a recently proposed integer programming formulation of the generalized version, and the results are reported.

In the labor-intensive cell manufacturing system, operator skills play a significant role in productivity, so it is crucial to train workers effectively. Although there are many studies on operator allocation and scheduling using mathematical models and on operator analysis and improvement analysis in actual workplaces, few studies have satisfied both of these requirements simultaneously. In this study, we propose an operator allocation method that considers fatigue and verifies the method's effectiveness by conducting assembly experiments using the allocation results.