Multi-objective Flexible Job-Shop Scheduling with an Ensemble Optimisation Model

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.

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.

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.

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 concept and prototypical implementation of a flexibly integrated production planning and scheduling system called FIPS (flexible integration of production system) is described. It consists of three major parts: a feature-recognizing system (FREDOS) that evaluates the 3-D-BREP (boundary representation) geometrical description of a part in terms of generic features that correspond to the execution of a basic manufacturing operation; a static process planning system (SSM) that identifies all combinations of manufacturing resources that are suited to produce the part; and a dynamic resource-allocation system (DRS) that decides online which of the possible resources has to be used in order to execute a manufacturing order. The assignment strategies may be individually specified by the application using the specification language SORC. A description is given of the basic ideas behind these systems along with a short overview of their implementation. >

Flexible job shop scheduling problem is a key factor of using efficiently in production systems. This paper attempts to simultaneously optimize three objectives including minimization of the make span, total workload and maximum workload of jobs. Since the multi objective flexible job shop scheduling problem is strongly NP-Hard, an integrated heuristic approach has been used to solve it. The proposed approach was based on a floating search procedure that has used some heuristic algorithms. Within floating search procedure utilize local heuristic algorithms; it makes the considered problem into two sections including assigning and sequencing sub problem. First of all search is done upon assignment space achieving an acceptable solution and then search would continue on sequencing space based on a heuristic algorithm. This paper has used a multi-objective approach for producing Pareto solution. Thus proposed approach was adapted on NSGA II algorithm and evaluated Pareto-archives. The elements and parameters of the proposed algorithms were adjusted upon preliminary experiments. Finally, computational results were used to analyze efficiency of the proposed algorithm and this results showed that the proposed algorithm capable to produce efficient solutions.

In the era of Industry 4.0, when smart factories and networked systems are reshaping the landscape of industrial production, the protection of important data and information security is a top priority. Cyber-physical systems and the technology that supports it are the keys to Industry 4.0. It is founded on four essential design principles: interoperability, availability of information, technological assistance, and decentralized decision-making. These design principles, however, provide new weaknesses that could be exploited by bad people. To protect these systems from emerging dangers, great consideration should be given to the proactive and adaptive security measures, which will consequently enable the continuing growth and success of Industry 4.0 technologies. This paper will delve into the multifaceted challenges that Industry 4.0 presents in terms of data security and the emerging solutions and strategies required protecting vital information in this brave new world of manufacturing. The exploration of these challenges and the proposed solutions are essential for businesses and policymakers alike to navigate the complexities of data security and ensure the resilience of critical information in the digital age of Industry 4.0.

Industry 4.0 has changed the thinking of industry owners in terms of technological usage. With the help of modern digital technology, industry can fulfill the requirements of customers easily and compete strongly against their competitors. In order to achieve good quality of products at an affordable price, industry needs skilled people who are aware of autonomous and intelligent components. To prepare skilled people compatible with Industry 4.0, education plays a very important role. The chapter starts with which kind of qualifications are needed to fit in the smart factory era. In next section, the chapter deals with challenges that emerge in education in order to implement skills suitable for Industry 4.0. Lastly, the chapter describes opportunities for the education sector as far as the smart factory is concerned.

Smart Factories in the Era of Industry 4.0: Convergence of IoT and Cyber-Physical Systems for Intelligent Automation, Connectivity, and Resilient Manufacturing

In the intelligent transformation of the global manufacturing industry, petroleum equipment processing is facing problems such as improving efficiency and ensuring quality, and the intelligent development of Manufacturing Execution System is crucial. Based on this, starting from the optimal scheduling algorithm and system architecture design, an intelligent manufacturing system scheme integrating artificial bee colony algorithm was proposed. In this scheme, a mathematical model is constructed, and the artificial bee colony algorithm is used to optimize the scheduling. Design a layered architecture to improve system performance; It has functions such as record rollback, intelligent splitting and dispatching quality inspection, and assembly line planning tree. Practice shows that this solution effectively improves the production efficiency and product quality of petroleum equipment processing, and helps the digital transformation of the industry.

Increased operational constraints on spacecraft and ground station operations have led to stricter ground software management and better performance throughput. For Mission Planning and Scheduling Systems, the ability to process numerous scheduling and planning input requests from multiple systems, applying high-level logistical rules, and providing export capabilities to all external entities, including the spacecraft itself, must be balanced against system performance. The constraint on the processing time of the schedule comes from a mission-defined need-by time, which is driven by execution or contact time. flexplan, an operational Mission Planning and Scheduling (MPS) system, has addressed the balance of performance and system capabilities. flexplan utilizes a combination of a relational database and soft-algorithm scheduling to maximize system performance. This paper demonstrates the method for the design of the internal database processing and maintenance. Performance methodologies and capabilities are presented to demonstrate software strength in two case studies: The Lunar Reconnaissance Orbiter (LRO) low lunar-orbit science mission currently operational and the Ground Network Schedule System (GNSS) multi-mission, multi-stations study of NASA’s infrastructure.

Emergency task offloading strategy based on cloud-edge-end collaboration for smart factories

This paper describes an expert system for paper production scheduling which has been in operation since January 1989 at Tomakomai Mill of Oji Paper Co., Ltd. The mill has ten papermaking machines and produces 200 different types of paper products each month.The problems associated with the production process are complex and large in scale, because of cross-machinery constraints such as limits on the supply of energy and pulp, and the combination of different products during the same production period. Production scheduling is implemented in three steps : group product allocation, individual allocation, and pulp and energy balance adjustment.The system provides a knowledge maintenance tool by employing several CRT screens, and the expert shell used is Toshiba's “ASIREX” which runs on the AS 3000 series engineering workstations.

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.

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.