Journal articles: 'Production scheduling. Flexible manufacturing systems'

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Tomastik, R. N., P. B. Luh, and Guandong Liu. "Scheduling flexible manufacturing systems for apparel production." IEEE Transactions on Robotics and Automation 12, no. 5 (1996): 789–99. http://dx.doi.org/10.1109/70.538983.

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Bonfioli, M., M. Garetti, and A. Pozzetti. "Production scheduling and operational control of flexible manufacturing systems." Robotica 3, no. 4 (1985): 233–44. http://dx.doi.org/10.1017/s0263574700002332.

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SUMMARYOnly hardware and software flexibility combined can yield the overall system flexibility required in newly designed FMSs in which the expected part mix is quite large and continually changing.The paper shows how modular design and integration are fundamental steps in software design for the management and control of FMSs. The main subsystems of a control system, built up by putting together a number of standardized modules requiring little or no customization, are also described. An experimental control system designed according to these criteria is also presented.

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Fontes, Dalila B. M. M., and Seyed Mahdi Homayouni. "Joint production and transportation scheduling in flexible manufacturing systems." Journal of Global Optimization 74, no. 4 (2018): 879–908. http://dx.doi.org/10.1007/s10898-018-0681-7.

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Zeng, Bin, Rui Wang, and Hong Yu Chen. "Design of Simulation Model for Production Scheduling in Flexible Manufacturing Systems." Advanced Materials Research 230-232 (May 2011): 814–18. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.814.

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The complex interaction and the high costs of modern manufacturing systems make it necessary to evaluate their use performance. Production scheduling problem is one of the key problems of research of manufacturing systems since with a proper scheduling, the utilization of resources is optimized and orders are produced on time which improves the shop performance and associated cost benefits. However the complexity of modern production systems makes the use of analytical tools more difficult. Thus a computer simulation model of the existing computer integrated manufacturing system based on the control logic that describes the operation of the system is developed to test the performance of different scheduling rules with respect to mean flow time, machine efficiency and total run time as performance measures. According to the results of experiments, the model agrees with the real system.

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Wenzelburger, Philipp, and Frank Allgöwer. "Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0." Applied Sciences 11, no. 17 (2021): 8145. http://dx.doi.org/10.3390/app11178145.

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In the context of Industry 4.0, flexible manufacturing systems play an important role. They are designed to provide the possibility to adapt the production process by reacting to changes and enabling customer specific products. The versatility of such manufacturing systems, however, also needs to be exploited by advanced control strategies. To this end, we present a novel scheduling scheme that is able to flexibly react to changes in the manufacturing system by means of Model Predictive Control (MPC). To introduce flexibility from the start, the initial scheduling problem, which is very general and covers a variety of special cases, is formulated in a modular way. This modularity is then preserved during an automatic transformation into a Petri Net formulation, which constitutes the basis for the two presented MPC schemes. We prove that both schemes are guaranteed to complete the production problem in closed loop when reasonable assumptions are fulfilled. The advantages of the presented control framework for flexible manufacturing systems are that it covers a wide variety of scheduling problems, that it is able to exploit the available flexibility of the manufacturing system, and that it allows to prove the completion of the production problem.

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Belkahal Driss, Olfa, Ouadji Korbaa, Khaled Ghedira, and Pascal Yim. "A distributed transient inter-production scheduling for flexible manufacturing systems." Journal Européen des Systèmes Automatisés 41, no. 1 (2007): 101–23. http://dx.doi.org/10.3166/jesa.41.101-123.

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Homayouni, Seyed Mahdi, and Dalila B. M. M. Fontes. "Production and transport scheduling in flexible job shop manufacturing systems." Journal of Global Optimization 79, no. 2 (2021): 463–502. http://dx.doi.org/10.1007/s10898-021-00992-6.

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Archimède, Bernard, and Thierry Coudert. "A Multi-Agent Scheduling Approach for the Flexible Manufacturing Production Systems." IFAC Proceedings Volumes 31, no. 32 (1998): 143–48. http://dx.doi.org/10.1016/s1474-6670(17)36348-6.

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Sharafali, Moosa, Henry C. Co, and Mark Goh. "Production scheduling in a flexible manufacturing system under random demand." European Journal of Operational Research 158, no. 1 (2004): 89–102. http://dx.doi.org/10.1016/s0377-2217(03)00300-x.

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Ghosh, Soumen, and Cheryl Gaimon. "Routing flexibility and production scheduling in a flexible manufacturing system." European Journal of Operational Research 60, no. 3 (1992): 344–64. http://dx.doi.org/10.1016/0377-2217(92)90086-o.

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Varela, Maria Leonilde R., André S. Santos, Ana M. Madureira, Goran D. Putnik, and Maria Manuela Cruz-Cunha. "Collaborative Framework for Dynamic Scheduling Supporting in Networked Manufacturing Environments." International Journal of Web Portals 6, no. 3 (2014): 33–51. http://dx.doi.org/10.4018/ijwp.2014070103.

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Scheduling continues to play an important role in manufacturing systems. It enables the production of suitable scheduling plans, considering shared resources between several different products, through several manufacturing environments including networked ones. High levels of uncertainty characterize networked manufacturing environments. Processes have specific and complex requirements and management requisites, along with diversified objectives, which are dynamic and often conflicting. Dynamic adaptation and a real-time response for manufacturing scheduling is still possible and is critical in this new manufacturing environments, which have a flexible nature, where disturbances on working conditions occur on a continuous and even unexpected basis. Therefore, scheduling systems should have the ability of automatically and intelligently maintain a real-time adaptation and optimization of orders production, to effectively and efficiently adapt these manufacturing environments to the inherent dynamic of markets. In this paper a collaborative framework for supporting dynamic scheduling in networked manufacturing environments is proposed, based on a hyper-organization model and on hyper-heuristics, in order to obtain feasible and robust scheduling plans.

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Sattar, Abdul, Qadir Bakhsh, and Muhammad Sharif. "Industrial Automation and Manufacturing Systems: Concepts and Applications." Advanced Materials Research 903 (February 2014): 291–96. http://dx.doi.org/10.4028/www.scientific.net/amr.903.291.

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Manufacturing comprises an effective and efficient integration of automation tools and advanced technologies for the industrial production. Automation is an advanced technique used in the manufacturing industry for integrating the machine tools to automatically perform different tasks. This paper presents the study about industrial automation and manufacturing system. The research and development in the area of automation includes programmable logic control (PLC), robotics, distributed control system (DCS), computerized numerical control machine (CNC), radio frequency identification (RFID). The intelligent systems for scheduling and manufacturing the product such as flexible manufacturing systems (FMS), computer aided manufacturing (CAM), computer integrated manufacturing (CIM), lean manufacturing and green manufacturing.

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Meziane, Mohammed El Amine, and Noria Taghezout. "Predictive Reactive Approach for Energy-Aware Scheduling and Control of Flexible Manufacturing Processes." International Journal of Information Systems and Supply Chain Management 11, no. 4 (2018): 43–62. http://dx.doi.org/10.4018/ijisscm.2018100103.

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Manufacturing processes are responsible for a considerable amount of global energy consumption and world CO2 emissions. Reducing energy consumption during manufacturing is considered one of the most important strategies in contributing to the green supply chain. In this context, the authors propose a new predictive-reactive approach to control energy consumption during manufacturing processes. In addition to forecasting the energy needs, the proposed approach controls the uncertainty of energy volatility and limits energy waste during manufacturing processes. With the integration of this economic-environmental manufacturing efficiency in supply chains, and controlling uncertainty, this approach positively contributes to green and agile supply chains. A multi-objective genetic algorithm (NSGA-2) is proposed as a predictive method, and a new reactive method is developed to dynamically control the energy consumption throughout the peak energy consumption in real time. The approach was tested on the AIP-PRIMECA benchmark, which reflects a real production cell.

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Zeballos, L. J. "A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems." Robotics and Computer-Integrated Manufacturing 26, no. 6 (2010): 725–43. http://dx.doi.org/10.1016/j.rcim.2010.04.005.

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Al-Turki, Umar M., Haitham Saleh, Tamer Deyab, and Yasser Almoghathawi. "Resource Allocation and Job Dispatching for Unreliable Flexible Flow Shop Manufacturing System." Advanced Materials Research 445 (January 2012): 947–52. http://dx.doi.org/10.4028/www.scientific.net/amr.445.947.

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Resource allocation, product batching and production scheduling are three different problems in manufacturing systems of different structures such as flexible flow shop manufacturing systems. These problems are usually dealt with independently for a certain objective function related to production efficiency and effectiveness. Handling all of them in an integrated manner is a challenge facing many manufacturing systems in practice and that challenge increases for highly complicated and stochastic systems. Random arrival of products, machine setup time requirements, unexpected machine breakdowns, and multiple conflicting objective functions are some of the common complications in such systems. This research attempts to study the integrated problem under the mentioned complications with various objective functions. The decisions parameters are the batch size, the number of machines at each workstation, and the dispatching policy. Discrete event simulation is used as an optimization tools. The system is modeled using the ARENA software and different scenarios are tested for optimum parameter selection under different conditions.

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Kück, Mirko, Jens Ehm, Michael Freitag, Enzo M. Frazzon, and Ricardo Pimentel. "A Data-Driven Simulation-Based Optimisation Approach for Adaptive Scheduling and Control of Dynamic Manufacturing Systems." Advanced Materials Research 1140 (August 2016): 449–56. http://dx.doi.org/10.4028/www.scientific.net/amr.1140.449.

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The increasing customisation of products, which leads to higher numbers of product variants with smaller lot sizes, requires a high flexibility of manufacturing systems. These systems are subject to dynamic influences and need increasing effort for the generation of the production schedules and for the control of the processes. This paper presents an approach that addresses these challenges. First, scheduling is done by coupling an optimisation heuristic with a simulation model to handle complex and stochastic manufacturing systems. Second, the simulation model is continuously adapted by real-time data from the shop floor. If, e.g., a machine breakdown or a rush order appears, the simulation model and consequently the scheduling model is updated and the optimisation heuristic adjusts an existing schedule or generates a new one. This approach uses real-time data provided by future cyber-physical systems to integrate scheduling and control and to manage the dynamics of highly flexible manufacturing systems.

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Ranky, Paul G. "A generic tool management system architecture for flexible manufacturing systems (FMS)." Robotica 6, no. 3 (1988): 221–34. http://dx.doi.org/10.1017/s0263574700004331.

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SUMMARYConsidering the fact that Flexible Manufacturing Systems (FMS) should be able to accommodate a variety of different parts in random order, tool management at cell level and tool transportation, tool data management, tooling data collection, tool maintenance, and manual and/or robotized tool assembly at FMS system level are very important. Tooling information in FMS is used by several subsystems, including: production planning, process control, dynamic scheduling, part programming, tool preset and maintenance, robotized and/or manual tool assembly, stock control and materials storage.The paper summarizes the major tasks to be solved when designing tool management systems for FMS, as well as gives a solution for describing the data structure of a tool data base integrated with a generic tool description method, and shows a sample transaction of the way the FMS real-time control system can access and use this data base.

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Rezig, Sadok, Wajih Ezzeddine, Sadok Turki, and Nidhal Rezg. "Mathematical Model for Production Plan Optimization—A Case Study of Discrete Event Systems." Mathematics 8, no. 6 (2020): 955. http://dx.doi.org/10.3390/math8060955.

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This paper proposes an optimal scheduling model under production and maintenance constraints for a real case of a discrete event system. The intent was to use the rich mathematical theory and algorithms of optimization in the study of this important class of systems. The current study detailed firstly a new approach for mapping a simulation event relationship graph into a mixed-integer program, with a flexible workshop real case. Several other potential applications of the mathematical model are examined, thanks to the model constraints flexibility characteristics, including a general case of a manufacturing system for optimal resource scheduling, an application on the case of hospital beds’ management. The model extension could be also interesting for other applications like museum systems or the case of big data in complex and social networks.

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Persi, Piero, Walter Ukovich, Raffaele Pesenti, and Marino Nicolich. "A hierarchic approach to production planning and scheduling of a flexible manufacturing system." Robotics and Computer-Integrated Manufacturing 15, no. 5 (1999): 373–85. http://dx.doi.org/10.1016/s0736-5845(99)00034-4.

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Gehlhoff, Felix, and Alexander Fay. "On agent-based decentralized and integrated scheduling for small-scale manufacturing." at - Automatisierungstechnik 68, no. 1 (2020): 15–31. http://dx.doi.org/10.1515/auto-2019-0105.

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AbstractSmall-scale manufacturing often relies on flexible production systems that can cope with frequent changes of products and equipment. Transports are a significant part of the production flow, especially in the domain of large and heavy workpieces that requires explicit planning to avoid unnecessary delays. This contribution takes a detailed look at how to create feasible integrated schedules within a decentralised or even heterarchical architecture and which information the agents have to exchange. These schedules incorporate constraints such as the blocking-constraint. They also consider dynamic setup and operation durations while finding a good-enough solution. The proposed agent-based solution applies to a wide variety of scheduling problems and reveals positive properties in terms of scalability and reconfigurability.

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Lin, James T., Chun-Chih Chiu, Edward Huang, and Hung-Ming Chen. "A Multi-Fidelity Model Approach for Simultaneous Scheduling of Machines and Vehicles in Flexible Manufacturing Systems." Asia-Pacific Journal of Operational Research 35, no. 01 (2018): 1850005. http://dx.doi.org/10.1142/s0217595918500057.

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Driven by sensor technologies and Internet of Things, massive real-time data from highly interconnected devices are available, which enables the improvement of decision-making quality. Scheduling of such production systems can be challenging as it must incorporate the latest data and be able to re-plan quickly. In this research, a multi-fidelity model for simultaneous scheduling problem of machines and vehicles at flexible manufacturing system has been proposed. In order to improve the computational efficiency, we extend the framework, called multi-fidelity optimization with ordinal transformation and optimal sampling, with combining with the K-means method. The proposed framework enables the benefits of both fast and inexpensive low-fidelity models with accurate but more expensive high-fidelity models. Results show that this approach can significantly decrease computational cost compared with other algorithms in the literature.

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Meilanitasari, Prita, and Seung-Jun Shin. "A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems." Processes 9, no. 8 (2021): 1391. http://dx.doi.org/10.3390/pr9081391.

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This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.

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Santos, Leandro Pereira dos, Guilherme Ernani Vieira, Higor Vinicius dos R. Leite, and Maria Teresinha Arns Steiner. "Ant Colony Optimisation for Backward Production Scheduling." Advances in Artificial Intelligence 2012 (September 19, 2012): 1–12. http://dx.doi.org/10.1155/2012/312132.

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The main objective of a production scheduling system is to assign tasks (orders or jobs) to resources and sequence them as efficiently and economically (optimised) as possible. Achieving this goal is a difficult task in complex environment where capacity is usually limited. In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time. For this reason, in many cases, a good solution that is quickly found is preferred. In such situations, the use of metaheuristics is an appropriate strategy. In these last two decades, some out-of-the-shelf systems have been developed using such techniques. This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO) in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings. This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year. This work demonstrates the applicability of this artificial intelligence technique. In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster.

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Kück, M., J. Ehm, T. Hildebrandt, M. Prof Freitag, and E. M. Prof Frazzon. "Adaptive PPS durch simulationsbasierte Optimierung*/Adaptive PPC by simulation-based optimization." wt Werkstattstechnik online 107, no. 04 (2017): 288–92. http://dx.doi.org/10.37544/1436-4980-2017-04-92.

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Der Trend zur Fertigung individualisierter Produkte in kleinen Losgrößen erfordert hochflexible Produktionssysteme. Durch die damit verbundene Systemdynamik wird die Reihenfolgeplanung zu einem komplexen Planungsproblem. Der Beitrag beschreibt ein simulationsbasiertes Optimierungsverfahren, welches Echtzeitinformationen zur adaptiven Selektion geeigneter Prioritätsregeln verwendet. Das Potenzial des Ansatzes wird anhand eines Anwendungsfalls aus der Halbleiterindustrie demonstriert. &nbsp; The trend to manufacturing individualized products in small-scale series demands highly flexible production systems. Because of the dynamic nature of such production systems, scheduling becomes a complex planning problem with frequent need for rescheduling. This article describes a data-driven simulation-based optimization approach using real-time information for adaptive job shop scheduling. The potential of the approach is demonstrated by a use case from semiconductor industry.

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Zhang, Fuqiang, and Jingjing Li. "An Improved Particle Swarm Optimization Algorithm for Integrated Scheduling Model in AGV-Served Manufacturing Systems." Journal of Advanced Manufacturing Systems 17, no. 03 (2018): 375–90. http://dx.doi.org/10.1142/s0219686718500221.

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To address the resources optimization problem of AGV-served manufacturing systems driven by multi-varieties and small-batch production orders, a scheduling model integrating machines and automated guided vehicles (AGVs) is proposed. In this model, the makespan of jobs from raw material storage to finished parts storage through multi-stage processes has been used as the objective function, and the utilization ratios of machines and AGVs have been used as the comprehensive evaluation functions. An improved particle swarm optimization algorithm with the characteristics of main particles and nested particles is developed to solve a reasonable scheduling scheme. Compared with the basic particle swarm optimization algorithm and genetic algorithm, the numerical result suggests that the nested particle swarm optimization algorithm has more advantages in convergence and solving efficiency. It is expected that this study can provide a useful reference for the flexible adjustment of AGV-served manufacturing systems.

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Kuo, Chung-Hsien, Han-Pang Huang, Kuang C. Wei, and Steve S. H. Tang. "System Modeling and Real-Time Simulator for Highly Model-Mixed Assembly Systems." Journal of Manufacturing Science and Engineering 121, no. 2 (1999): 282–89. http://dx.doi.org/10.1115/1.2831217.

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In recent years, the automobile industry is facing more challenge than ever due to rapid model change requirements and demand fluctuations. Traditional fixed-type production systems are less capable in coping with the rapid change, while flexible production systems can better meet the requirement of low-volume production with model variations. Flexible machines integrated with model-mixed assembly lines form flexible assembly systems (FASs). In this paper, Colored Timed Petri Net (CTPN) is used to model a highly model-mixed automobile assembly system. Based on this CTPN model, a real-time simulator was developed along with different dispatching rules. Besides, the algorithm of balanced budget work standard (BWS) for a small batch production is developed for adjusting the loading balance of the operators in the highly model-mixed assembly lines. The simulator can be used as an effective tool for developing scheduling strategies.

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Yao, Fengjia, Bugra Alkan, Bilal Ahmad, and Robert Harrison. "Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation." Sensors 20, no. 21 (2020): 6333. http://dx.doi.org/10.3390/s20216333.

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Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.

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Choudhury, B. B., B. B. Biswal, D. Mishra, and R. N. Mahapatra. "Appropriate Evolutionary Algorithm for Scheduling in FMS." International Journal of Applied Evolutionary Computation 2, no. 3 (2011): 15–26. http://dx.doi.org/10.4018/jaec.2011070102.

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The diffusion of flexible manufacturing systems (FMS) has not only invigorated production systems, but has also given considerable impetus to relevant analytical fields like scheduling theory and adaptive controls. Depending on the demand of the job there can be variation in batch size. The change in the jobs depends upon the renewal rate. But this does not involve much change in the FMS setup. This paper obtains an optimal schedule of operations to minimize the total processing time in a modular FMS. The FMS setup considered here consists of four numbers of machines to accomplish the desired machining operations. The scheduling deals with optimizing the cost function in terms of machining time. The powers Evolutionary Algorithms, like genetic algorithm (GA) and simulated annealing (SA), can be beneficially utilized for optimization of scheduling FMS. The present work utilizes these powerful approaches and finds out their appropriateness for planning and scheduling of FMS producing variety of parts in batch mode.

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Wang, Wanzhu, Qazi Salman Khalid, Muhammad Abas, et al. "Implementation of POLCA Integrated QRM Framework for Optimized Production Performance—A Case Study." Sustainability 13, no. 6 (2021): 3452. http://dx.doi.org/10.3390/su13063452.

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Quick response manufacturing (QRM) is a relatively new concept that enfolds all the preceding approaches, namely, just in time (JIT), flexible manufacturing, agile manufacturing, and lean production. QRM is compatible with existing materials requirement planning (MRP) systems and can be implemented efficiently. The ideas from QRM have been highly influential in custom-made engineer-to-order and make-to-order (ETO/MTO) high-mix and low-volume production environments. This study investigates the effectiveness of the POLCA (paired cell overlapping loops of cards) integrated QRM framework for reducing lead time. The POLCA integrated QRM approach was implemented in a precise product manufacturing industry. The industry was facing high penalties due to improper planning and uncontrolled lead times. The implementation of QRM with the POLCA framework indicated optimized production scheduling and significant improvement in lead time and work in process (WIP). After implementing the new manufacturing strategy, the performance parameters showed significant improvement in terms of reducing the percentage loss of profit.

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Dziurzanski, Piotr, Shuai Zhao, Sebastian Scholze, Albert Zilverberg, Karl Krone, and Leandro Soares Indrusiak. "Process planning and scheduling optimisation with alternative recipes." at - Automatisierungstechnik 68, no. 2 (2020): 140–47. http://dx.doi.org/10.1515/auto-2019-0104.

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AbstractThis paper considers an application of a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes, to a real-world chemical plant. The problem is optimised using a multi-objective genetic algorithm with customised mutation and elitism operators that minimises both the total production time and the produced commodity surplus. The algorithm evaluation is performed with both random and historic manufacturing orders. The latter demonstrated that the proposed system can lead to more than 10 % makespan improvements in comparison with human operators.

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Huang, Rong-Hwa, Shao-Jung Chang, and Shun-Chi Yu. "A Study of Flexible Flow Shop Scheduling Problem with Various Heterogeneous Labors." Mathematical Problems in Engineering 2021 (July 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/5529612.

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This study considers the necessity of hiring heterogeneous labors. So far, many studies focus more on manufacturing of equipment and control systems in the intelligent production planning. In fact, the regular labors’ processing time may be affected by the external factors and the disabled labors by the mentally handicapped. Therefore, this study sets the processing time of the two types of labors as fuzzy sets. The extra processing time from overtime generated by physical deterioration of old-aged labors is equal to the processing time of regular labors multiplied by the physical deterioration rate of old-aged labors on machine. The coefficient of the cost function is the stepwise function of cost structure. Besides, the dispatching rule based on floating time utilizes ant colony optimization to minimize the makespan. The data test results indicate that the proposed algorithm can efficiently dispatch and schedule the operations, with the average improvement ratio about 11.76%, and demonstrates high capability for the intelligent production planning

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Wang, Lei, Chaomin Luo, and Jingcao Cai. "A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm." Journal of Advanced Transportation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1527858.

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In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA) is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES) and improved population diversity strategy (IPDS) are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.

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Zhang, Yi, Haihua Zhu, and Dunbing Tang. "An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem." Kybernetes 49, no. 12 (2019): 2873–92. http://dx.doi.org/10.1108/k-06-2019-0430.

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Purpose With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed. Design/methodology/approach After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization. Findings Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence. Research limitations/implications This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown. Practical implications IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum. Social implications This research provides an efficient scheduling method for solving the FJSP problem. Originality/value This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

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Wu, Kuo-Yang, Sendren Sheng-Dong Xu, and Tzong-Chen Wu. "Optimal Scheduling for Retrieval Jobs in Double-Deep AS/RS by Evolutionary Algorithms." Abstract and Applied Analysis 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/634812.

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We investigate the optimal scheduling of retrieval jobs for double-deep type Automated Storage and Retrieval Systems (AS/RS) in the Flexible Manufacturing System (FMS) used in modern industrial production. Three types of evolutionary algorithms, the Genetic Algorithm (GA), the Immune Genetic Algorithm (IGA), and the Particle Swarm Optimization (PSO) algorithm, are implemented to obtain the optimal assignments. The objective is to minimize the working distance, that is, the shortest retrieval time travelled by the Storage and Retrieval (S/R) machine. Simulation results and comparisons show the advantages and feasibility of the proposed methods.

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Araúzo, José Alberto, Ricardo Del Olmo, and Juan José Laviós. "Subasta combinatoria para la programación dinámica en sistemas de fabricación distribuidos." Dirección y Organización, no. 51 (December 1, 2013): 55–64. http://dx.doi.org/10.37610/dyo.v0i51.438.

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Los métodos de programación de operaciones tradicionales, estáticos y basados en arquitecturas centralizadas o jerárquicas, no son suficientemente flexibles para adaptarse al dinamismo y complejidad de los sistemas de fabricación actuales. Por ello, la investigación en técnicas dinámicas de programación y control on-line está creciendo rápidamente. En este ar tículo se presenta una técnica de programación dinámica on-line basada en modelos de mercado e implementada sobre un sistema multiagente. Esta propuesta, además de dinámica, es distribuida no jerárquica, lo que aporta al sistema las características deseadas.Palabras claves: programación y control de la producción, programación on-line, sistemas de fabricación basados en agentes, subastas combinatorias.Combinatorial auction for dynamic scheduling in distributed manufacturing systemsAbstract: The traditional static scheduling methods, based on hierarchical and centralized architectures, are not flexible enough to self-adapt to the dynamism and complexity of today’s manufacturing systems. For this reason, new proposals to improve the scheduling and control of agile manufacturing systems constantly appear. The auction based allocation methods as well as the software paradigm of multiagent systems, which offers new techniques to face complex unsolved problems, can help to find promising solutions in manufacturing systems. Traditionally, scheduling problems have been solved offline by a centralized decision-maker that use a global optimisation model. We propose to include in the system several decision-makers modelled as agents instead. We consider two kinds of agents: order agents and machines agents. Each order agent represents a product that is characterized by its operations, precedence relationships and due date. The goal of each order agent is to find machines that can perform the required operations and hence completing successfully the order. Each production order creates its own schedule (local schedule). An auction mechanism ensures that local schedules are nearly compatible (several orders don’t use the same machine at the same moment) and globally efficient. Every agent in the system can communicate with other agents through the exchange of messages. The interaction mechanism is ruled by means of a combinatorial auction where a theoretical basis is provided for structuring message sequencing, bid evaluation, and price updating. Our research contributes to the auction technique in manufacturing scheduling and control in two basic aspects: (1) we apply the auction mechanism for a routing flexible environment (an operation can be performed in several machines with a differing efficiency), (2) we propose an implementation that can schedule online, updating real-time information: planning horizon, changes in orders, changes in machine availability and capabilities. We include explicitly the option of reallocate resources in real time when a new order arrives to the system. In order to test the features of this approach we display some computational results. Preliminary results show efficient performance in dynamic scenarios, but there are still many matters to investigate. Future works will be devoted to test the proposed approach on more case studies or even on real cases. We can add complexity to the structure of the system, and we must improve some aspects of the auction mechanism such as convergence and stability.Keywords: manufacturing programming and control, on-line scheduling, agent based manufacturing systems, combinatorial auctions.

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Fattahi, Parviz, Naeeme Bagheri Rad, Fatemeh Daneshamooz, and Samad Ahmadi. "A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process." Assembly Automation 40, no. 3 (2020): 419–32. http://dx.doi.org/10.1108/aa-11-2018-0178.

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Purpose The purpose of this paper is to present a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations. In this problem, each product is produced by assembling a set of several different parts. At first, the parts are processed in a flexible job shop system, and then at the second stage, the parts are assembled and products are produced. Design/methodology/approach As the problem is non-deterministic polynomial-time-hard, a new hybrid particle swarm optimization and parallel variable neighborhood search (HPSOPVNS) algorithm is proposed. In this hybrid algorithm, particle swarm optimization (PSO) algorithm is used for global exploration of search space and parallel variable neighborhood search (PVNS) algorithm for local search at vicinity of solutions obtained in each iteration. For parameter tuning of the metaheuristic algorithms, Taguchi approach is used. Also, a statistical test is proposed to compare the ability of metaheuristics at finding the best solution in the medium and large sizes. Findings Numerical experiments are used to evaluate and validate the performance and effectiveness of HPSOPVNS algorithm with hybrid particle swarm optimization with a variable neighborhood search (HPSOVNS) algorithm, PSO algorithm and hybrid genetic algorithm and Tabu search (HGATS). The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms. Practical implications Scheduling of manufacturing parts and planning of assembly operations are two steps in production systems that have been studied independently. However, with regard to many manufacturing industries having assembly lines after manufacturing stage, it is necessary to deal with a combination of these problems that is considered in this paper. Originality/value This paper proposed a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations.

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Göppert, Amon, Leon Mohring, and Robert H. Schmitt. "Predicting performance indicators with ANNs for AI-based online scheduling in dynamically interconnected assembly systems." Production Engineering 15, no. 5 (2021): 619–33. http://dx.doi.org/10.1007/s11740-021-01057-z.

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AbstractMass customization demands shorter manufacturing system response times due to frequent product changes. This increase in system dynamics imposes additional flexibility requirements especially on assembly processes, as complexity accumulates in this last step of value creation. Flexible and dynamically interconnected assembly systems can meet the increased requirements as opposed to traditional dedicated assembly line approaches. The high complexity and dynamical environment in these kinds of systems lead to the demand for real-time online control and scheduling solutions. Within the decision-making of online scheduling, the capability of predicting the consequences of available actions is crucial. In real-time environments, running extensive discrete-event simulations to evaluate how actions unfold requires too much computing time. Artificial neural networks (ANN) are a viable alternative to quickly evaluate the potential future performance value of a production state for online production planning and control. They can predict performance indicators such as the expected makespan given the current production status. Leveraging recent advances in artificial intelligence (AI) game algorithms, an assembly control system based on Google DeepMind’s AlphaZero was created. Specifically, an ANN is incorporated into the approach that suggests favorable job routing decisions and predicts the value of actions. The results show that the trained network can predict favorable actions with an accuracy of over 95% and estimate the makespan with an error smaller than 3%.

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Li, Yuanyuan, Stefano Carabelli, Edoardo Fadda, Daniele Manerba, Roberto Tadei, and Olivier Terzo. "Machine learning and optimization for production rescheduling in Industry 4.0." International Journal of Advanced Manufacturing Technology 110, no. 9-10 (2020): 2445–63. http://dx.doi.org/10.1007/s00170-020-05850-5.

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Abstract Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

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Rahman, Humyun Fuad, Mukund Nilakantan Janardhanan, and Peter Nielsen. "An integrated approach for line balancing and AGV scheduling towards smart assembly systems." Assembly Automation 40, no. 2 (2020): 219–34. http://dx.doi.org/10.1108/aa-03-2019-0057.

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Purpose Optimizing material handling within the factory is one of the key problems of modern assembly line systems. The purpose of this paper is to focus on simultaneously balancing a robotic assembly line and the scheduling of material handling required for the operation of such a system, a topic that has received limited attention in academia. Manufacturing industries focus on full autonomy because of the rapid advancements in different elements of Industry 4.0 such as the internet of things, big data and cloud computing. In smart assembly systems, this autonomy aims at the integration of automated material handling equipment such as automated guided vehicles (AGVs) to robotic assembly line systems to ensure a reliable and flexible production system. Design/methodology/approach This paper tackles the problem of designing a balanced robotic assembly line and the scheduling of AGVs to feed materials to these lines such that the cycle time and total tardiness of the assembly system are minimized. Because of the combination of two well-known complex problems such as line balancing and material handling and a heuristic- and metaheuristic-based integrated decision approach is proposed. Findings A detailed computational study demonstrates how an integrated decision approach can serve as an efficient managerial tool in designing/redesigning assembly line systems and support automated transportation infrastructure. Originality/value This study is beneficial for production managers in understanding the main decisional steps involved in the designing/redesigning of smart assembly systems and providing guidelines in decision-making. Moreover, this study explores the material distribution scheduling problems in assembly systems, which is not yet comprehensively explored in the literature.

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Yang, Guang, Yan Fang Yue, Jin Ye Wang, and Yong Di Zhang. "A Machining Shop Scheduling Model Based on UML and IDEF." Applied Mechanics and Materials 26-28 (June 2010): 870–74. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.870.

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Combining with production features of the machining shop in small and medium manufacturing enterprises and difficulties in their information construction, we studied deeply on the scheduling model suitable for complex information environment of the machining shop, and established the job planning and scheduling system model. The hybrid modeling method of UML and IDEF was adopted to describe the architecture of the job planning and scheduling system in machining shop. IDEF, which has good flexibility and logic, is used to describe the system's functions and needs, and then express an implementation scheme that can meet the needs and realize the functions. At the same time, UML is utilized to reflect the chronological relationship of the interaction between functional objects. From the perspectives of structure, function, information and control, both of the above advantages are combined to establish the flexible and reusable model for job planning and scheduling system in machining shop.

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Delgoshaei, Aidin, Abolfazl Mirzazadeh, and Ahad Ali. "A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs." Brazilian Journal of Operations & Production Management 15, no. 4 (2018): 499–516. http://dx.doi.org/10.14488/bjopm.2018.v15.n4.a4.

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Highlights:  Cellular Manufacturing systems cover a wide range of industries. Inflation rate can impose financial harms on cellular manufacturing systems. The over-allocation of workers, which usually happens in dynamic systems, causes reduction of the system performance. The proposed algorithm in this research can successfully schedule cellular systems to reduce system costs.  Goal: The main aim is to determine the best trade-off values between in-house manufacturing and outsourcing, and track the impact of uncertain costs on gained schedules. To be more comprehensive, the performance of human resources is restricted and the partial demands are considered uncertain. Design / Methodology / Approach: In this paper a new method for minimizing human resource costs, including operating, salary, hiring, firing, and outsourcing in a dynamic cellular manufacturing system is presented where all system costs are uncertain during manufacturing periods and can be affected by inflation rate. For this purpose, a multi-period scheduling model that is flexible enough to use in real industries has been proposed. To solve the proposed model, a hybrid Ant Colony Optimization and the Tabu Search algorithm (ACTS) are proposed and the outcomes are compared with a Branch-and-Bound based algorithm. Results: Our findings showed that the inflation rate has significant effect on multi-period system planning. Moreover, utilizing system capability by the operator, for promoting and using temporary workers, can effectively reduce system costs. It is also found that workers’ performance has significant effect on total system costs. Limitations of the investigation: This research covers the cellular manufacturing systems. Practical implications: The algorithm is applied for 17 series of dataset that are found in the literature. The proposed algorithm can be easily applied in real industries. Originality / Value: The authors confirm that the current research and its results are original and have not been published before. The proposed algorithm is useful to schedule cellular manufacturing systems and analyse various production conditions.

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Zywicki, K., and P. Rewers. "A simulation-based approach to study the influence of different production flows on manufacturing of customized products." Advances in Production Engineering & Management 15, no. 4 (2020): 467–80. http://dx.doi.org/10.14743/apem2020.4.379.

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Manufacturing products tailored to the individual requirements of customers is a must if companies want to compete effectively on the market. The production of customized goods poses new challenges for all areas of functioning of production systems. It is necessary to adopt such rules and methods that will allow a flexible response to product design changes and their demand In the organization of production flow (materials and information). The article presents research carried out in the SmartFactory laboratory of the Poznań University of Technology regarding the impact of the structure of products (customization) on the realization of current production orders. The research was carried out using the FlexSim simulation environment. Based on simulation experiments for three forms of organization of production flow with varying degrees of flexibility of production resources, an analysis was made of the time of execution of various sets of production orders and the level of use of available working time. The results of research indicate that in the production of products with low and high planned labor consumption, the use of universal production station is the most advantageous. For such a solution, the degree of utilization of the available working time of production stations is also the highest. It was also found that the principles of scheduling production orders affect the effectiveness of the production system. The best results were obtained for the production schedule, where the sequence of production orders was established from the lowest planned time of resource loading.

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Koekemoer, Martin, and Igor Gorlach. "Development of a reconfigurable pallet system for a robotic cell." MATEC Web of Conferences 210 (2018): 02003. http://dx.doi.org/10.1051/matecconf/201821002003.

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Advanced manufacturing systems allow rapid changes of production processes by means of reconfigurability providing mass customisation of products with high productivity, quality and low costs. Reconfigurable Manufacturing Systems (RMS) employ conventional as well as special purpose CNC machines, robots and material handling systems. In customised automated assembly, a number of different workpieces need to be processed simultaneously at various workstations according to their process plans. Therefore, a material handling system is an important part of RMS, whose main task is to provide reliable, accurate and efficient transfer of materials according to the process scheduling, without bottlenecks and stoppages. In this research, a reconfigurable pallet system was developed to facilitate automated robotic assembly for a highly customised production environment. The aim is to design a material handling system for conveying, sorting and processing of parts, which are supplied by robots and part feeders in different configurations. The developed pallet system provides a low-cost solution and it includes four flexible conveyors and part handling devices. All the elements of the system were successfully integrated with an intelligent controller. A user-friendly human machine interface provides easy reconfigurability of the pallet system and interfacing with robots, processing stations and part feeding sub-systems. The main advantages of the developed material handling system are the ease of operation, its reconfigurability and low-cost. The system demonstrates the advantages of reconfigurable material handling systems and it can be employed for training purposes.

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Ramezanian, Reza, Sahar Fallah Sanami, and Mohsen Shafiei Nikabadi. "A simultaneous planning of production and scheduling operations in flexible flow shops: case study of tile industry." International Journal of Advanced Manufacturing Technology 88, no. 9-12 (2016): 2389–403. http://dx.doi.org/10.1007/s00170-016-8955-z.

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Zhang, Shoujing, Haotian Du, Sebastian Borucki, Shoufeng Jin, Tiantian Hou, and Zhixiong Li. "Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm." Machines 9, no. 6 (2021): 108. http://dx.doi.org/10.3390/machines9060108.

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Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application.

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Kamel, Khaled, and Eman Kamel. "PLC Batch Process Control Design and Implementation Fundamentals." September 2020 2, no. 3 (2020): 155–61. http://dx.doi.org/10.36548/jei.2020.3.001.

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Batch process control is typically used for repeated chemical reaction tasks. It starts with a measured liquid material filling operations followed by a controlled reaction leading to the discharge or transport of processed quantities of material. The input materials is contained in vessel reactor and subjected to a sequence of processing activities over a recipe predefined duration of time. Batch systems are designed to measure, process, and discharge a varying volume of liquid from drums, tanks, reactors, or other large storage vessel using a programmable logic controller (PLC). These systems are common in pharmaceutical, chemical packaging, Beverage processing, personal care product, biotech manufacturing, dairy processing, soap manufacturing, and food processing industries. This paper briefly discusses the fundamental techniques used in specifying, designing, and implementing a PLC batch process control [1, 2]. A simplified batch process is used to illustrate key issues in designing and implementing such systems. In addition to the structured PLC ladder design; more focus is given to safety requirements, redundancy, interlocking, input data validation, and safe operation. The Allen Bradley (AB) SLC 500 PLC along with the LogixPro simulator are used to illustrate the concepts discussed in this paper. Two pumps are used to bring in material during the tank filling and a third pump is used to drain processed product. The three pumps are equipped with flow meters providing pulses proportional to the actual flow rate through the individual pipes. The tank material is heated to a predefined temperature duration followed by mixing for a set time before discharge. Batch control systems provides automated process controls, typically and universally using PLC’s networked to HMI’s and other data storage, analysis, and assessment computers. The overall system perform several tasks including recipe development and download, production scheduling, batch management and execution, equipment performance monitoring, inventory, production history and tracking functionalities. Flexible batch control systems are designed to accommodate smaller batches of products with greater requirements / recipes variation, efficiently and quickly. In addition to providing process consistency, continuous batch process control quality improvements are attained through the automatic collection and analysis of real-time reliable and accurate event performance data [3, 4].

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Pachpor, Pravin S., R. L. Shrivastava, Dinesh Seth, and Shaligram Pokharel. "Application of Petri nets towards improved utilization of machines in job shop manufacturing environments." Journal of Manufacturing Technology Management 28, no. 2 (2017): 169–88. http://dx.doi.org/10.1108/jmtm-05-2016-0064.

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Purpose The purpose of this paper is to demonstrate the use of Petri nets in a job shop setup for the improvement in the utilization of machines. Design/methodology/approach The study discusses concepts such as reachable state, token and matrix equations set, and demonstrates the improvements in machines’ utilization in a job shop. It makes use of algorithms to generate reachable markings to obtain utilization. The study not only describes the application of theory, but also extends the body of knowledge on Petri nets and job shops. Findings In this study, machines’ utilization has been studied in a job shop with six machines and eight products. The study finds that substantial utilization improvement in job shop set up can be obtained through the application of Petri nets. The study also exposes that Petri nets are mostly used for machines, jobs and tools scheduling problems, but its use in machines’ utilization is neglected. The framework and application presented here along with generalizable findings, is the first to report about machine utilization improvement in job shop manufacturing environment. Practical implications Job shops are characterized by high unit production cost, low investments, low volume and high variety, complex flows, flexible and skilled work force, general purpose machines, high material handling; resulting in poor utilization of machines. Therefore, the findings of this study can help in reducing such costs through better machine utilization. This can help in increasing the competitiveness of the companies. Originality/value The contribution of study lies in investigating and improving stage wise utilization in a job shop setup. It has never been reported before.

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Jain, A. K., and H. A. Elmaraghy. "Production scheduling/rescheduling in flexible manufacturing." International Journal of Production Research 35, no. 1 (1997): 281–309. http://dx.doi.org/10.1080/002075497196082.

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Doulgeri, Z., R. D. Hibberd, T. M. Husband, and A. Chisholm. "The Scheduling of Flexible Manufacturing Systems." CIRP Annals 36, no. 1 (1987): 343–46. http://dx.doi.org/10.1016/s0007-8506(07)62618-3.

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Wu, X. B., and V. A. Armentano. "Distributive Scheduling of Flexible Manufacturing Systems." IFAC Proceedings Volumes 25, no. 7 (1992): 67–71. http://dx.doi.org/10.1016/s1474-6670(17)52341-1.

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Journal articles on the topic 'Production scheduling. Flexible manufacturing systems'

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