エキスパートシステムによる生産計画システム

Aiming at the design and development of integrated production planning and scheduling system(PPSS) of rare metal manufacturing, the goals and main functions of the integrated production planning and scheduling system were analyzed and designed based on the analysis of rare metal manufacturing enterprise’s main characteristics and management status. Through analysis of the present situation and trend of production planning and scheduling, an integrated system architecture was proposed. Key technologies of system integration and system developing were delivered, and some key functions of production planning and scheduling system which include annual plans management, monthly plans management, MRP management, production scheduling management and production Kanban management were developed based on JSP technology and WEB platform. The practical applications in some rare metal manufacturing enterprises were proved that the integrated production planning and scheduling system could enhance the manufacturing management level effectively.

Thin‐film transistor liquid‐crystal displays (TFT‐LCDs) have gradually replaced cathode‐ray tubes because of their compact size and efficient use of power. Since product lifecycles are shortening, consumers require instant order delivery, which affects fluctuations in supply and demand. As various product types in the LCD industry exist, manufacturing time has to be reduced to satisfy consumer demand. This study investigates the dispatching of parallel machines for TFT‐LCD manufacturing. Since customers’ orders and suppliers’ deliveries vary significantly and the manual adjustment of production planning is time consuming, an appropriate production planning and scheduling system is necessary. Consequently, this study develops a genetic algorithm to solve the scheduling problem of unrelated parallel machines, taking account of multiple performance measurement indicators. We also adopt LINGO to solve the problem and compare the obtained solution quality and computation time of the two approaches to justify the effectiveness of the proposed genetic algorithm.

The following investigation shows the problems in “Industria Granero del Centro EIRL”, it is a company that is dedicated to the production of grains such as split bean, split wheat, american morón and corn flour. The goal is to improve the production planning and scheduling system, and eliminate overproduction and economic losses. VSM (Value Stream Mapping) was used to identify the improvement in the production process. The root causes of the problem were then determined using the Ishikawa diagram. The activities to be carried out are: identification of times of each process of the evaluated product, carry out the implementation of the master production plan, application of Kanban and application of Heijunka for load balancing [1]. The results of this implementation will be visualized through a simulation making use of efficiency, effectiveness and productivity indicators to meet the objective.

The garment industry is a global industry that requires high agility in response to changing market demands that are quickly changing. Short product cycles with unpredictable demand often make the industry unable to meet consumer needs. In increasing the agility of production to deliver products to customers as fast as possible, the production scheduling system must be designed optimally. Recently algorithm hybridization is used because the combination of more than one algorithm is more optimal. Genetic Algorithm (GA) is a metaheuristic algorithm is applied in various production scheduling and its power can be improved by combining it with the Tabu Search (TS). The GA is the best metaheuristic algorithm to output the optimal scheduling with less execution time but has the disadvantage –easily trapped in local optimum (early convergence is faster). The TS algorithm works as a local search algorithm with a faster computation time than GA. This study aims to minimize the total time to complete the work (minimizing makespan) by combining TS into GA in conducting local searches to increase industrial agility. The results obtained are GA-TS hybridization can provide a more optimal solution for the production scheduling in the garment so that agility can increase.

Smart factory evolves by adding new functions or upgrading existing functions to meet the needs of manufacturers in the use stage of the life cycle. To reduce complexity of smart factory, these functions must be carefully designed considering interactions with other functions. This study analyzes integration of functions situated in the different layers in the functional hierarchy of smart factory. In this study, a health monitoring system for cutting tools in a shop floor, which offers a function to manage the lifetime of cutting tools, is presented. The system is integrated with a production scheduling system, which offers a function to schedule machining processes considering efficient usage of machines as well as cutting tools, while maintaining the quality machining processes. Primary evaluation of the functional integration shows that activities in the shop floor regarding selection and replacement of cutting tools are considered in defining production schedules. It also shows that such functional integration results in increase in complexity regarding the behavioral model of humans in smart factory.

At the end processes of TFT-LCD supply chain, the LCM module factory takes charge of the process of assembling products, and its production planning needs to consider various products with different graded TFT-LCD panels while meeting customers' specific qualifications, including lowest acceptance rates for quality and zero-bad-defect quantity. Since the machine setup time of the bottleneck workstation in the factory is very long, it is of practical importance to design an appropriate production scheduling with concerns about times of machine setups and different grades of TFT-LCD products. This paper proposes a production scheduling system that comprises the material planning with downgraded products as well as the bottleneck scheduling for a LCM module factory using make-to-stock production, and solves the system based on integer programming. Experimental results show that the system not only is capable of reducing the number of setup times, but also performs well in efficiency of preparing critical materials. From a practical aspect, such a system can support production managers to plan their production scheduling effectively.

Production scheduling is a form of decision-making, which plays a crucial role in manufacturing as well as in service industries. Many approaches are used in practice in order to find good and feasible solutions in the scheduling problems. Approaches based on Genetic Algorithms (GAs) are becoming more and more widespread nowadays. This paper is the result of the author's extensive study on the use of advanced technologies in the field of production planning and scheduling and follows his previous research on the use of expert systems in this field (Metaxiotis et al., 2002). The present paper presents famous GAs known in the literature and current applications, analyses the relative benefits and concludes by sharing thoughts and estimations on GAs future prospects in this area.

A production scheduling and control system for CAR T cell manufacturing

Smart factories and intelligent manufacturing systems are the key drivers of knowledge economy in the era of Industry 4.0. One of the critical aspects is a flexible and optimised production planning and scheduling system. An efficient scheduling system can minimise the production cost and maximise machine utilization, leading to improvement in the production rate. In this study, we design an ensemble-based Harris' Hawk optimisers (EN-HHO) to address the multi-objective flexible job shop problem (MOFJSP), which is an NP-hard and complex combinatorial task. The developed ensemble model is evaluated using well-known benchmark problems, and the results compare favourably with those from similar methods in the literature.

The production scheduling of open pit mines is an intricate, complex and difficult problem to address due to its large scale and the unavailability of a truly optimal net present value (NPV) solution, as well as the uncertainty in key parameters involved. These key factors are geological and mining, financial and environmental. Geological uncertainty is a major contributor in failing to meet production targets and the financial expectations of a project especially in the early stages of a project. Stochastic integer programming (SIP) models provide a framework for optimising mine production scheduling considering uncertainty. A specific SIP formulation is shown herein that generates the optimal production schedule using equally probable simulated orebody models as input, without averaging the related grades. The optimal production schedule is then the schedule that can produce the maximum achievable discounted total value from the project, given the available orebody uncertainty described through a set of stochastically simulated orebody models. The proposed SIP model allows the management of geological risk in terms of not meeting planned targets during actual operation, unlike the traditional scheduling methods that use a single orebody model and where risk is randomly distributed between production periods while there is no control over the magnitude of the risks on the schedule. Notably, the testing of the SIP formulation in two cases, a gold and a copper deposit, shows that the expected total NPV of the schedule using the SIP approach is significantly higher (10 and 25% respectively) than the traditional schedule developed using a single estimated orebody model.

The dynamic scheduling management system architecture that is based on the parts and information packets unified technology is developed, and variable lot size production with the parts and packets unified technology is proposed. We assumed that each part or unit has a data career, which handle such as identification number, position of the part, state of the part. Using these data, progress of the project process is compared with the master schedule. If the difference arises between them, scheduling system dynamically re-schedules with dynamic lot size to optimize the processes. A pilot system that realized our proposal is developed.

A production scheduling system for a hybrid flow shop—a case study

The fracture volume is gradually changed with the depletion of fracture pressure during the production process. However, there are few flowback models available so far that can estimate the fracture volume loss using pressure transient and rate transient data. The initial flowback involves producing back the fracturing fluid after hydraulic fracturing, while the second flowback involves producing back the preloading fluid injected into the parent wells before fracturing of child wells. The main objective of this research is to compare the initial and second flowback data to capture the changes in fracture volume after production and preload processes. Such a comparison is useful for evaluating well performance and optimizing fracturing operations.We construct rate-normalized pressure (RNP) versus material balance time (MBT) diagnostic plots using both initial and second flowback data (FBi and FBs, respectively) of six multi-fractured horizontal wells completed in Niobrara and Codell formations in DJ Basin. In general, the slope of RNP plot during the FBs period is higher than that during the FBi period, indicating a potential loss of fracture volume from the FBi to the FBs period. We estimate the changes in effective fracture volume (Vef) by analyzing the changes in the RNP slope and total compressibility between these two flowback periods. Vef during FBs is in general 3%–45% lower than that during FBi. We also compare the drive mechanisms for the two flowback periods by calculating the compaction-drive index (CDI), hydrocarbon-drive index (HDI), and water-drive index (WDI). The dominant drive mechanism during both flowback periods is CDI, but its contribution is reduced by 16% in the FBs period. This drop is generally compensated by a relatively higher HDI during this period. The loss of effective fracture volume might be attributed to the pressure depletion in fractures, which occurs during the production period and can extend 800 days.

Digital twin and artificial intelligence (DT-AI) technologies present hitherto unheard-of possibilities for dynamic production scheduling in smart manufacturing. Nevertheless, a careful examination of several studies reveals significant gaps in the current state of the discipline. This paper attempts to review advancements, gaps, and opportunities in the areas of DT-AI-based production scheduling. Articles chosen for this literature analysis were mostly published within the last eight years. Based on the literature, five enabling challenges that are consistently considered in the literature include Dynamic and Unforeseen Disruptions, High System Complexity, Real-Time Data Management, Integration and Interoperability, and Adaptability and Generalizability. This review not only identifies these enabling challenges but also provides tailored outlines of progress and future directions. The findings pave the way for resilient, scalable, and interpretable DT-AI systems for production scheduling that can handle uncertainty and optimize output in real time. DTs and AI can benefit manufacturing with data-driven intelligent planning and decision-making as well as model-based systems engineering principles. This review examines these advancements and trending research directions in production scheduling.

Semiconductor production scheduling and industrial economic coordination based on deep learning and heat loss optimization