Guidelines for the deployment and implementation of manufacturing scheduling systems

It has frequently been stated that there exists a gap between production scheduling theory and practice. In order to put theoretical findings into practice, advances in scheduling models and solution procedures should be embedded into a piece of software - a scheduling system - in companies. This results in a process that entails (1) determining its functional features, and (2) adopting a successful strategy for its development and deployment. In this paper we address the latter question and review the related literature in order to identify descriptions and recommendations of the main aspects to be taken into account when developing such systems. These issues are then discussed and classified, resulting in a set of guidelines that can help practitioners during the process of developing and deploying a scheduling system. In addition, identification of these issues can provide some insights to drive theoretical scheduling research towards those topics more in demand by practitioners, and thus help to close the aforementioned gap.Framiñan Torres, JM.; Ruiz García, R. (2012). Guidelines for the deployment and implementation of manufacturing scheduling systems. International Journal of Production Research. 50(7):1799-1812. doi:10.1080/00207543.2011.564670S17991812507Baek, D. H. (1999). A visualized human-computer interactive approach to job shop scheduling. International Journal of Computer Integrated Manufacturing, 12(1), 75-83. doi:10.1080/095119299130489Comesaña Benavides, J. A., & Carlos Prado, J. (2002). Creating an expert system for detailed scheduling. International Journal of Operations & Production Management, 22(7), 806-819. doi:10.1108/01443570210433562Bensana, E. 1986. An expert-system approach to industrial job-shop scheduling. In: Proceedings of the 1986 IEEE international conference on robotics and automation. 1986. Vol. 3, pp.1645–1650.Berglund, M., & Karltun, J. (2007). Human, technological and organizational aspects influencing the production scheduling process. International Journal of Production Economics, 110(1-2), 160-174. doi:10.1016/j.ijpe.2007.02.024Besbes, W., Teghem, J., & Loukil, T. (2010). Scheduling hybrid flow shop problem with non-fixed availability constraints. European J. of Industrial Engineering, 4(4), 413. doi:10.1504/ejie.2010.035652Bhattacharyya, S., & Koehler, G. J. (1998). Learning by Objectives for Adaptive Shop-Floor Scheduling. Decision Sciences, 29(2), 347-375. doi:10.1111/j.1540-5915.1998.tb01580.xBitran, G. R., & Tirupati, D. (1988). OR Practice—Development and Implementation of a Scheduling System for a Wafer Fabrication Facility. Operations Research, 36(3), 377-395. doi:10.1287/opre.36.3.377Buxey, G. (1989). Production scheduling: Practice and theory. 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F. (1984). ISIS?a knowledge-based system for factory scheduling. Expert Systems, 1(1), 25-49. doi:10.1111/j.1468-0394.1984.tb00424.xFraminan, J. M., & Ruiz, R. (2010). Architecture of manufacturing scheduling systems: Literature review and an integrated proposal. European Journal of Operational Research, 205(2), 237-246. doi:10.1016/j.ejor.2009.09.026Freed, T., Doerr, K. H., & Chang, T. (2007). In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations. International Journal of Production Research, 45(21), 5075-5093. doi:10.1080/00207540600818351Gao, C and Tang, L. 2008. A decision support system for color-coating line in steel industry. In: Proceedings of the IEEE international conference on automation and logistics, ICAL 2008. 2008. pp.1463–1468.Grant, T. J. (1986). Lessons for O.R. from A.I.: A Scheduling Case Study. Journal of the Operational Research Society, 37(1), 41-57. doi:10.1057/jors.1986.7Graves, S. C. (1981). A Review of Production Scheduling. Operations Research, 29(4), 646-675. doi:10.1287/opre.29.4.646HALSALL, D. N., MUHLEMANN, A. P., & PRICE, D. H. R. (1994). A review of production planning and scheduling in smaller manufacturing companies in the UK. Production Planning & Control, 5(5), 485-493. doi:10.1080/09537289408919520Higgins, P. G. (1996). Interaction in hybrid intelligent scheduling. International Journal of Human Factors in Manufacturing, 6(3), 185-203. doi:10.1002/(sici)1522-7111(199622)6:33.0.co;2-6Kanet, J. J., & Adelsberger, H. H. (1987). Expert systems in production scheduling. European Journal of Operational Research, 29(1), 51-59. doi:10.1016/0377-2217(87)90192-5Kathawala, Y., & Allen, W. R. (1993). Expert Systems and Job Shop Scheduling. International Journal of Operations & Production Management, 13(2), 23-35. doi:10.1108/01443579310025286Kerr, R. M. (1992). Expert systems in production scheduling: Lessons from a failed implementation. Journal of Systems and Software, 19(2), 123-130. doi:10.1016/0164-1212(92)90063-pKnolmayer, G., Mertens, P., & Zeier, A. (2002). Supply Chain Management Based on SAP Systems. doi:10.1007/978-3-540-24816-3Leachman, R. C., Benson, R. F., Liu, C., & Raar, D. J. (1996). IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation—Semiconductor Sector. Interfaces, 26(1), 6-37. doi:10.1287/inte.26.1.6MACCARTHY, B. L., & LIU, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59-79. doi:10.1080/00207549308956713McKay, K. N., & Black, G. W. (2007). The evolution of a production planning system: A 10-year case study. Computers in Industry, 58(8-9), 756-771. doi:10.1016/j.compind.2007.02.002McKay, K. N., Safayeni, F. R., & Buzacott, J. A. (1988). Job-Shop Scheduling Theory: What Is Relevant? Interfaces, 18(4), 84-90. doi:10.1287/inte.18.4.84McKay, K. N., Morton, T. E., Ramnath, P., & Wang, J. (2000). ?Aversion dynamics? scheduling when the system changes. Journal of Scheduling, 3(2), 71-88. doi:10.1002/(sici)1099-1425(200003/04)3:23.0.co;2-0MCKAY, K., PINEDO, M., & WEBSTER, S. (2009). PRACTICE-FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*. Production and Operations Management, 11(2), 249-258. doi:10.1111/j.1937-5956.2002.tb00494.xMissbauer, H., Hauber, W., & Stadler, W. (2009). A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry. International Journal of Production Research, 47(15), 4147-4172. doi:10.1080/00207540801950136Numao, M and Morishita, S. 1989. A scheduling environment for steel-making processes. In: Proceedings of the 5th conference on artificial intelligence applications. 1989. pp.279–286.Olhager, J., & Rapp, B. (1995). Operations Research Techniques in Manufacturing Planning and Control Systems. 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H., Wu, S. D., & Vaccari, R. (1992). New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling. Management Science, 38(10), 1495-1509. doi:10.1287/mnsc.38.10.1495Tang, L., & Wang, G. (2008). Decision support system for the batching problems of steelmaking and continuous-casting production. Omega, 36(6), 976-991. doi:10.1016/j.omega.2007.11.002T’kindt, V., Billaut, J.-C., Bouquard, J.-L., Lenté, C., Martineau, P., Néron, E., … Tacquard, C. (2005). The e-OCEA project: towards an Internet decision system for scheduling problems. Decision Support Systems, 40(2), 329-337. doi:10.1016/j.dss.2004.04.001Wiers, VCS. 1997. Human–computer interaction in production scheduling: Analysis and design of decision support systems for production scheduling tasks. Ph.D. Thesis, Technische Universiteit Eindhoven, NetherlandsWiers, V. C. S. (2002). A case study on the integration of APS and ERP in a steel processing plant. 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Design & development of a simulation model to analyse scheduling rules in an FMS in a virtual manufacturing environment : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Manufacturing and Industrial Technology at Massey University

Due to the rapid changes in the needs of the customer for new products, the future manufacturing systems must cope with these changes. Hence, the need for the manufacturing systems to support these changes in the products with shorter lead times within a single manufacturing facility. The Virtual Manufacturing System (VMS) is one concept which can assist in meeting these demands. The VMS concept enables the manufacturing system designers to emulate and test the performance of the future manufacturing systems. This research has given an overview of the new concepts of Virtual Manufacturing Systems and Virtual Manufacturing in general. A Virtual Reality Software tool has been used to realise the VMS concept. A Virtual Manufacturing Environment representing a Flexible Manufacturing System (FMS) has been modelled. A simulation control language is employed for developing simulation control logics and decision making control logics for the development of the FMS model. The modelled FMS is implemented and tested through simulation experiments. The testing is done by analysing the traditional scheduling rules in a manufacturing facility. Average Machine Utilisation, Mean Flow Time, Average Queue Lengths and the System Production Rate are measured as the System Performance Measures for the evaluation of the scheduling rules. This research has identified that the Virtual Manufacturing Software is a powerful tool which can identify optimum configurations and highlight potential problems before a final and expensive manufacturing system is established physically

How environment dynamics affects production scheduling: requirements for development of CPPS models

Production scheduling can be affected by many disturbances in the manufacturing system, and consequently, the feasible schedules previously defined became obsolete. Emerging of new technologies associated with Industry 4.0, such as Cyber-Physical Production Systems, as a paradigm of implementation of control and support in decision making, should embed the capacity to simulate different environment scenarios based on the data collected by the manufacturing systems. This paper presents the evaluation of environment dynamics effect on production scheduling, considering three scheduling models and three environment scenarios, through a case study. Results show that environment dynamics affect production schedules, and a very strong or strong positive correlation between environment dynamics scenarios and total completion time with delay, over three scheduling paradigms. Based on these results, the requirement for mandatory inclusion of a module for different environment dynamics scenarios generation and the corresponded simulations, of a Cyber-Physical Production Systems architecture, is confirmed.This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment

Manufacturing activities and production control are constantly growing. Despite this, it is necessary to improve the increasing variety of scheduling and layout adjustments for dynamic and flexible responses in volatile environments with disruptions or failures. Faced with the lack of realistic and practical manufacturing scenarios, this approach allows simulating and solving the problem of job shop scheduling on a production system by taking advantage of genetic algorithm and particle swarm optimization algorithm combined with the flexibility and robustness of a multi-agent system and dynamic rescheduling alternatives. Therefore, this hybrid decision support system intends to obtain optimized solutions and enable humans to interact with the system to properly adjust priorities or refine setups or solutions, in an interactive and user-friendly way. The system allows to evaluate the optimization performance of each one of the algorithms proposed, as well as to obtain decentralization in responsiveness and dynamic decisions for rescheduling due to the occurance of unexpected events.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Cooperation platform for distributed manufacturing

The aim of the paper is to analyse contemporary trends in distributed manufacturing (DM) research and to present a concept to develop and test some task allocation, planning and scheduling algorithms for DM network organisations. Some concepts to identify key factor criteria and reasoning policies and rules for production/manufacturing decision support system are also undertaken. And finally, an aim is to draw a proposal for a development of a prototype decision support system with necessary communication and knowledge oriented modules to be implemented in an example of dynamic, DM and logistics network structure, particularly for very popular dynamic cluster forms in Poland. The developed concept of the organization of a multi-entity DM network will enable business-effective use of the system, supporting manufacturing decision making, consulting and offering information services in the control centre (the so-called Competence Centre) by constructing virtual reality and access to services in a distributed network of cloud computing type. Integration of the whole system into one information system will enable analysis and network resource optimization of manufacturing and logistics processes, new analytical functions, reduction of delays in the manufacturing system, management of changes and risks, and visualization of the current state of the DM system

Comparing Production Design Activities and Location-Based Planning Tools

What are the differences between production system design and work structuring? And between phase scheduling and work structuring? Which lean planning tool is better suited for each one of these design processes: line of balance, takt-time planning or flowline? This paper aims to answer these questions through a comparison and deeper understanding of production design processes, as well as the potential uses of location-based tools for production planning and control in each design effort. The method used is the literature review analyses on main lean terms and tools applied for production system design. With a better comprehension of the terms and tools, it is expected that academics and lean practitioners will be able to apply lean construction in a more aware and sensible manner. The results will also support researcher’s decision about the most suitable lean tool to apply in the case studies in different production design processes

Event Monitoring System to Classify Unexpected Events for Production Planning

[EN] Production planning prepares companies to a future production scenario. The decision process followed to obtain the production plan considers real data and estimated data of this future scenario. However, these plans can be affected by unexpected events that alter the planned scenario and in consequence, the production planning. This is especially critical when the production planning is ongoing. Thus providing information about these events can be critical to reconsider the production planning. We herein propose an event monitoring system to identify events and to classify them into different impact levels. The information obtained from this system helps to build a risk matrix, which determines the significance of the risk from the impact level and the likelihood. A prototype has been built following this proposal.This research has been carried out in the framework of the project GV/2014/010 funded by the Generalitat Valenciana (Identificacion de la informacion proporcionada por los nuevos sistemas de deteccion accesibles mediante internet en el ambito de las "sensing enterprises" para la mejora de la toma de decisiones en la planificacion de la produccion).Boza, A.; Alarcón Valero, F.; Alemany Díaz, MDM.; Cuenca, L. (2017). Event Monitoring System to Classify Unexpected Events for Production Planning. Lecture Notes in Business Information Processing. 291:140-154. https://doi.org/10.1007/978-3-319-62386-3_7S140154291Barták, R.: On the boundary of planning and scheduling: a study (1999)Buzacott, J.A., Corsten, H., Gössinger, R., Schneider, H.M.: Production Planning and Control: Basics and Concepts. 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Manag. 26(1), 80–86 (2008)Bearzotti, L.A., Salomone, E., Chiotti, O.J.: An autonomous multi-agent approach to supply chain event management. Int. J. Prod. Econ. 135(1), 468–478 (2012)Baron, M.M., Pate-Cornell, M.E.: Designing risk-management strategies for critical engineering systems. IEEE Trans. Eng. Manag. 46(1), 87–100 (1999)Bartolini, C., Stefanelli, C., Tortonesi, M.: SYMIAN: analysis and performance improvement of the IT incident management process. IEEE Trans. Netw. Serv. Manag. 7(3), 132–144 (2010)Cox Jr., L.A.: What’s wrong with risk matrices? Risk Anal. Int. J. 28(2), 497–512 (2008)Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.: Past, present, and future of decision support technology. Decis. 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Computer-assisted production scheduling, planning and control in foundries

The present study describes a pragmatic approach to the implementation of production planning and scheduling techniques in foundries of all types and looks at the use of `state-of-the-art' management control and information systems. Following a review of systems for the classification of manufacturing companies, a definitive statement is made which highlights the important differences between foundries (i.e. `component makers') and other manufacturing companies (i.e. `component buyers'). An investigation of the manual procedures which are used to plan and control the manufacture of components reveals the inherent problems facing foundry production management staff, which suggests the unsuitability of many manufacturing techniques which have been applied to general engineering companies. From the literature it was discovered that computer-assisted systems are required which are primarily `information-based' rather than `decision based', whilst the availability of low-cost computers and `packaged-software' has enabled foundries to `get their feet wet' without the financial penalties which characterized many of the early attempts at computer-assistance (i.e. pre-1980). Moreover, no evidence of a single methodology for foundry scheduling emerged from the review. A philosophy for the development of a CAPM system is presented, which details the essential information requirements and puts forward proposals for the subsequent interactions between types of information and the sub-system of CAPM which they support. The work developed was oriented specifically at the functions of production planning and scheduling and introduces the concept of `manual interaction' for effective scheduling. The techniques developed were designed to use the information which is readily available in foundries and were found to be practically successful following the implementation of the techniques into a wide variety of foundries. The limitations of the techniques developed are subsequently discussed within the wider issues which form a CAPM system, prior to a presentation of the conclusions which can be drawn from the study

A decision support system for modelling and implementing the supply network configuration and operations scheduling problem in the machine tool industry

[EN] This paper presents a decision support system to simultaneously solve the supply network configuration problem and the operations scheduling problem for the machine tool industry. A novel database structure, which is able to consider alternative operations and alternative bills of material, has been used. An algorithm for complete enumeration to determine all the feasible solutions using stroke graphs is introduced. A multiagent-based simulator evaluates the different key performance indicators that the supply network deals with for each alternative solution (e.g. workload, profits, delivery times, etc.) to determine that ‘satisficed’ by the collaborative decision-making among its members. A case study based on a Spanish company that assembles highly customised machines and tools in several European plants is considered. From the experiments results based on data linked to this industry, it will be demonstrated that the tool is potentially useful for stakeholders and for the central decision-maker to make decisions collaboratively in a multisite context caseWe thank the EWG-DSS and their four expert anonymous referees as well as the guest editorial board for their useful suggestions and criticism on earlier versions of this paper. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. NMP2-SL-2009-229333 and has been partially supported by the Spanish Ministry of Science and Innovation within the 'Proyectos de Investigacion Fundamental No Orientada Programme' through Project 'CORSARI MAGIC DPI2010-18243'. Julien Maheut holds a VALi+d grant funded by the Regional Valencian Government (Ref. ACIF/2010/222).Maheut, JPD.; Besga, JM.; Uribetxebarria, J.; García Sabater, JP. (2014). A decision support system for modelling and implementing the supply network configuration and operations scheduling problem in the machine tool industry. Production Planning and Control. 25(8):679-697. https://doi.org/10.1080/09537287.2013.798087S67969725

Developing an ambient intelligent-based decision support system for production and control planning

Scheduling production instructions in a manufacturing facility is key to assure a efficient process that assures the desired product quantities are produced in time, with quality and with the right resources. An efficient production avoids the creation of downstream delays, and early completion which both can be detrimental if storage space is limited and contracted quantities are important. Therefore, the production, planning and control of manufacturing is increasingly more difficult as family products increases. This paper presents an ongoing Ambient Intelligent decision support system development that aims to provide assistance on the creation on standard work procedures that assure production quantity and efficiency by means of ambient intelligence, optimization heuristics and machine learning in the context of a large organization.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundac¸ ˜ao para a Ciˆencia e a Tecnologia (Portuguese Foundation for Science and Technology) within the Project Scope UID/CEC/00319/2013. This research is also sponsored by the Portugal Incentive System for Research and Technological Development. Project in co-promotion no 002814/2015 (iFACTORY 2015-2018).info:eu-repo/semantics/publishedVersio