Integrating MRP in production systems simulation tools

Literature review suggests concentrating on the development of new reference model for manufacturing system simulation, which may implement an operation logic much closer to real industrial contexts. A production system modelling tool should be designed with the aim of standardizing and simplifying the simulation of manufacturing processes and to widespread this approach in SMEs. With this aim, the authors got committed in designing a reference model for providing a structural framework to support shop-floor simulation and optimization. This paper presents the basic framework logic and structure of the simulation tool, showing how it is possible to represent it in Business Process Modelling Notation (BPMN). On top of this, the efforts of implementing an MRP module on top of a simulation took which was originally conceived to embed look-back material handling policies area described, together with the operative solutions chosen to reach the integration

Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions

"This is an Accepted Manuscript of an article published in International Journal of Production Research on December 2014, available online: http://www.tandfonline.com/10.1080/00207543.2014.920115."In this paper, we formulate the material requirements planning) problem of a first-tier supplier in an automobile supply chain through a fuzzy multi-objective decision model, which considers three conflictive objectives to optimise: minimisation of normal, overtime and subcontracted production costs of finished goods plus the inventory costs of finished goods, raw materials and components; minimisation of idle time; minimisation of backorder quantities. Lack of knowledge or epistemic uncertainty is considered in the demand, available and required capacity data. Integrity conditions for the main decision variables of the problem are also considered. For the solution methodology, we use a fuzzy goal programming approach where the importance of the relations among the goals is considered fuzzy instead of using a crisp definition of goal weights. For illustration purposes, an example based on modifications of real-world industrial problems is used.This work has been funded by the Universitat Politecnica de Valencia Project: 'Material Requirements Planning Fourth Generation (MRPIV)' (Ref. PAID-05-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Jiménez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research. 52(23):6971-6988. doi:10.1080/00207543.2014.920115S697169885223Aköz, O., & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European Journal of Operational Research, 181(3), 1427-1433. doi:10.1016/j.ejor.2005.11.049Alfieri, A., & Matta, A. (2010). 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Modelling of ill-known requirements and integration in production planning. Production Planning & Control, 22(4), 336-352. doi:10.1080/09537281003800900Heilpern, S. (1992). The expected value of a fuzzy number. Fuzzy Sets and Systems, 47(1), 81-86. doi:10.1016/0165-0114(92)90062-9Hnaien, F., Dolgui, A., & Ould Louly, M.-A. (2008). Planned lead time optimization in material requirement planning environment for multilevel production systems. Journal of Systems Science and Systems Engineering, 17(2), 132-155. doi:10.1007/s11518-008-5072-zHung, Y.-F., & Chang, C.-B. (1999). Determining safety stocks for production planning in uncertain manufacturing. International Journal of Production Economics, 58(2), 199-208. doi:10.1016/s0925-5273(98)00124-8Inderfurth, K. (2009). How to protect against demand and yield risks in MRP systems. International Journal of Production Economics, 121(2), 474-481. doi:10.1016/j.ijpe.2007.02.005JIMÉNEZ, M. (1996). RANKING FUZZY NUMBERS THROUGH THE COMPARISON OF ITS EXPECTED INTERVALS. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 04(04), 379-388. doi:10.1142/s0218488596000226Jiménez, M., Arenas, M., Bilbao, A., & Rodrı´guez, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609. doi:10.1016/j.ejor.2005.10.002Jones, D. (2011). A practical weight sensitivity algorithm for goal and multiple objective programming. European Journal of Operational Research, 213(1), 238-245. doi:10.1016/j.ejor.2011.03.012Lage Junior, M., & Godinho Filho, M. (2010). Variations of the kanban system: Literature review and classification. International Journal of Production Economics, 125(1), 13-21. doi:10.1016/j.ijpe.2010.01.009Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G. V., & Eversdyk, D. (2004). A simulation based optimization approach to supply chain management under demand uncertainty. Computers & Chemical Engineering, 28(10), 2087-2106. doi:10.1016/j.compchemeng.2004.06.006Koh, S. C. L. (2004). MRP-controlled batch-manufacturing environment under uncertainty. Journal of the Operational Research Society, 55(3), 219-232. doi:10.1057/palgrave.jors.2601710Lai, Y.-J., & Hwang, C.-L. (1993). Possibilistic linear programming for managing interest rate risk. Fuzzy Sets and Systems, 54(2), 135-146. doi:10.1016/0165-0114(93)90271-iLee, H. L., & Billington, C. (1993). Material Management in Decentralized Supply Chains. Operations Research, 41(5), 835-847. doi:10.1287/opre.41.5.835Lee, Y. H., Kim, S. H., & Moon, C. (2002). Production-distribution planning in supply chain using a hybrid approach. Production Planning & Control, 13(1), 35-46. doi:10.1080/09537280110061566Li, X., Zhang, B., & Li, H. (2006). Computing efficient solutions to fuzzy multiple objective linear programming problems. Fuzzy Sets and Systems, 157(10), 1328-1332. doi:10.1016/j.fss.2005.12.003Louly, M.-A., & Dolgui, A. (2011). Optimal time phasing and periodicity for MRP with POQ policy. International Journal of Production Economics, 131(1), 76-86. doi:10.1016/j.ijpe.2010.04.042Louly, M. A., Dolgui, A., & Hnaien, F. (2008). Optimal supply planning in MRP environments for assembly systems with random component procurement times. International Journal of Production Research, 46(19), 5441-5467. doi:10.1080/00207540802273827Mohapatra, P., Benyoucef, L., & Tiwari, M. K. (2013). Integration of process planning and scheduling through adaptive setup planning: a multi-objective approach. International Journal of Production Research, 51(23-24), 7190-7208. doi:10.1080/00207543.2013.853890Mula, J., & Díaz-Madroñero, M. (2012). Solution Approaches for Material Requirement Planning* with Fuzzy Costs. Industrial Engineering: Innovative Networks, 349-357. doi:10.1007/978-1-4471-2321-7_39Mula, J., Poler, R., & García, J. P. (2006). Evaluación de Sistemas para la Planificación y Control de la Producción/[title] [title language=en]Evaluation of Production Planning and Control Systems. Información tecnológica, 17(1). doi:10.4067/s0718-07642006000100004Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74-97. doi:10.1016/j.fss.2005.05.045Mula, J., Poler, R., & Garcia-Sabater, J. P. (2008). Capacity and material requirement planning modelling by comparing deterministic and fuzzy models. International Journal of Production Research, 46(20), 5589-5606. doi:10.1080/00207540701413912Mula, J., Poler, R., & Garcia-Sabater, J. P. (2007). Material Requirement Planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 158(7), 783-793. doi:10.1016/j.fss.2006.11.003Mula, J., Poler, R., García-Sabater, J. P., & Lario, F. C. (2006). Models for production planning under uncertainty: A review. International Journal of Production Economics, 103(1), 271-285. doi:10.1016/j.ijpe.2005.09.001Noori, S., Feylizadeh, M. R., Bagherpour, M., Zorriassatine, F., & Parkin, R. M. (2008). Optimization of material requirement planning by fuzzy multi-objective linear programming. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(7), 887-900. doi:10.1243/09544054jem1014Olhager, J. (2013). Evolution of operations planning and control: from production to supply chains. International Journal of Production Research, 51(23-24), 6836-6843. doi:10.1080/00207543.2012.761363Peidro, D., Mula, J., Alemany, M. M. E., & Lario, F.-C. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. 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A Conceptual Model for Integrating Transport Planning: MRP IV

In this article, a conceptual model, called MRP IV, is proposed in order to serve as a reference to develop a new production technology that integrates material planning decisions, production resource capacities and supply chain transport for the purpose of avoiding the suboptimization of these plans which, today, are usually generated sequentially and independently. This article aim is twofold: (1) it identifies the advances and deficiencies in the MRP calculations, mainly based on the dynamic multi-level capacitated lot-sizing problem (MLCLSP); and (2) it proposes a conceptual model, defining the inputs, outputs, modeling and solution approaches, to overcome the deficiencies identified in current MRP systems and act as a baseline to propose resolution models and algorithms required to develop MRP IV as a decision-making system. © 2012 IFIP International Federation for Information Processing. Mula, J.; Díaz-Madroñero Boluda, FM.; Peidro Payá, D. (2012). A conceptual model for integrating transport planning: MRP IV. En IFIP Advances in Information and Communication Technology. Springer. (384):54-65. doi:10.1007/978-3-642-33980-6_7 Senia 54 65 384 Document type: Part of book or chapter of boo

Bütünleşik imalat ortamlarında ürün ağacı yapısının imalat performansı üzerine etkisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Küresel rekabet nedeniyle, ürünlerin yaşam döngüsü kısalmış, müşteri istek ve beklentileri artmış, düşük maliyetli ve zengin ürün çeşitliliği arzu edilmeye başlanılmıştır. Bu nedenlerden dolayı geleneksel olarak fazla üretilip stoklanarak müşteri isteklerinin karşılanması fikrinden uzaklaşılmaktadır. Malzeme ihtiyaç planlaması kullanılmaya başlandığı 70’lerden günümüze kadar üretim işletmeleri için öncelikler değişmiştir. Önceleri üretmek önemliyken şimdilerde istenilen zamanda, istenilen fiyatta, istenilen kalitede ürün üretimi önem kazanmıştır. Bu nedenle müşteri beklenti ve isteklerini karşılayabilmek için işletmeler yeni arayışlara ve modern üretim planlama ve kontrol sistemlerine ihtiyaç duyulmuştur. Çok aşamalı ve çok çeşitli ürün üreten işletmelerin küresel rekabet ortamında rakipleri ile rekabet edebilmesi için gerçek zamanlı bir üretim planlama ve çizelgeleme sistemlerine ihtiyaç vardır. Bu nedenle işletmelerin sahip olacağı sağlıklı bir üretim planlama ve kontrol yapısı sayesinde, maliyetlerin düşmesine, müşteri siparişlerinin karşılanma oranının yükselmesine, gerçekçi bir teslim tarihi elde edilmesine, gelecek planlamalarının etkin bir şekilde yapılmasına imkan sağlayacak gerçek zamanlı sınırlı kapasite üretim planlama ve kontrol yapısı önerilmiş ve önerilen sistem üç farklı ürün ağacı yapısı, beş farklı öncelik kuralı ve teslim tarihinin belirlenmesi için kullanılan üç farklı k sabiti değeri kullanılarak geliştirilen benzetim ortamında test edilmiştir. Geliştirilen benzetim modelinde üç bağımsız değişken girdi olarak kullanılmış ve altı farklı performans ölçütünün sonuçlarına göre değerlendirilmiştir. Alınan sonuçlar, çok değişkenli varyans analizi ile test edilmiştir. Yapılan test ve değerlendirme sonucunda ürün ağacı yapısı bağımsız değişkeninin tek başına ve diğer bağımsız değişkenlerle beraber imalat performansı üzerinde etkili olduğu tespit edilmiştir. Geliştirilen yapının siparişe üretim yapan, çok seviyeli montaj ve imalat işlemlerinin beraber yürütüldüğü bütünleşik imalat ortamlarında başarı ile uygulanabileceği düşünülmektedir.Due to global competiveness, life cycle of products have been shortened, low costed and rich product varieties have been demanded. For this reason, the idea of production to make stock for customer demand has been eliminated. Material requirement planning has been using since 1970’s. Nowadays, priorities of companies have been changed dramatically. Previously production was an important subject, whereas production on-time, on-demand, good quality and low priced products have been ground. For this reason, to satisfy customer needs and demands, companies investigated new methods and production control systems. Companies which produced multi stage and multi variety products need real time production planning and scheduling system. For this reason, companies which have good production planning and control systems, proceed low cost, satisfaction of customer demand, realistic due dates, and finite capacity planning and control systems. In this thesis, 3 different product structures, 5 different priority rules, and 3 different k constants for determining due dates have been developed to use in simulation software systems. In the developed simulation model, 3 independent variables has been used as input and evaluated with results of 6 different performance criteria. Results obtained have been tested with multiple variable variance analysis. After testing and evaluating, independent variables of product structures has an effect on manufacturing performance as single and other dependent variables. Structure developed for this work has been considered as a successful environment. Experimental results have been obtained in make-to-order environment and multi stage assembly and manufacturing operations in integrated manufacturing environment

A conceptual model for MRP IV

In current supply chains where there is a considerable offshoring of raw materials and parts suppliers, production planning can no longer be considered a separate and independent process from transportation planning. However, current systems only focus on production decisions, regardless of the transport considerations. This means that proposed production plans could be suboptimal, and even infeasible. In these cases, manual replanning is a common practice in companies until production plans are made feasible as far as available transport capacity is concerned. For the purpose of avoiding the suboptimization of these plans, we present a conceptual model, the MRP IV, which serves as a reference to develop a new production technology and integrates material planning, production resource capacities and supply chain transport decisions, and acts as a baseline to propose resolution models and algorithms required to develop MRP IV as a decision-making system. © 2012 Springer-Verlag.Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2012). A conceptual model for MRP IV. En Decision Support Systems – Collaborative Models and Approaches in Real Environments. Springer Verlag. 14-25. doi:10.1007/978-3-642-32191-7_2S1425Orlicky, J.: Material Requirements Planning. McGraw-Hill, New York (1975)White, O.W.: MRP II—Unlocking America’s Productivity Potential. CBI Publishing, Boston (1981)Schollaert, F.: Money resource planning, MRP-III: the ultimate marriage between business logistics and financial management information systems. 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