Integrated production-distribution planning optimization models: A review in collaborative networks context
Keywords:Collaborative planning, Supply chain, Production planning, Distribution planning
AbstractResearchers in the area of collaborative networks are more and more aware of proposing collaborative approaches to address planning processes, due to the advantages associated when enterprises perform integrated planning models. Collaborative production-distribution planning, among the supply network actors, is considered a proper mechanism to support enterprises on dealing with uncertainties and dynamicity associated to the current markets. Enterprises, and especially SMEs, should be able to overcome the continuous changes of the market by increasing their agility. Carrying out collaborative planning allows enterprises to enhance their readiness and agility for facing the market turbulences. However, SMEs have limited access when incorporating optimization tools to deal with collaborative planning, reducing their ability to respond to the competition. The problem to solve is to provide SMEs affordable solutions to support collaborative planning. In this regard, new optimisation algorithms are required in order to improve the collaboration within the supply network partners. As part of the H2020 Cloud Collaborative Manufacturing Networks (C2NET) research project, this paper presents a study on integrated production and distribution plans. The main objective of the research is to identify gaps in current optimization models, proposed to address integrated planning, taking into account the requirements and needs of the industry. Thus, the needs of the companies belonging to the industrial pilots, defined in the C2NET project, are identified; analysing how these needs are covered by the optimization models proposed in the literature, to deal with the integrated production-distribution planning.
Aliev, R.A., Fazlollahi, B., Guirimov, B.G., Aliev, R.R. (2007). Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management. Information Sciences, 177(20), 4241-4255. https://doi.org/10.1016/j.ins.2007.04.012
Andres, B., Sanchis, R., Poler, R. (2016). A cloud platform to support collaboration in supply networks. International Journal of Production Management and Engineering, 4(1), 5-3. https://doi.org/10.4995/ijpme.2016.4418
Arntzen, B.C., Brown, G.G., Harrison, T.P., Trafton, L.L. (1995). Global supply chain management at Digital Equipment Corporation. Interfaces, 25(1), 69-93. https://doi.org/10.1287/inte.25.1.69
Chan, F.T., Chung, S.H., Choy, K.L. (2006). Optimization of order fulfillment in distribution network problems. Journal of Intelligent Manufacturing, 17(3), 307-319. https://doi.org/10.1007/s10845-005-0003-z
Chen, M., Wang, W. (1997). A linear programming model for integrated steel production and distribution planning. International Journal of Operations & Production Management, 17(6), 592-610. https://doi.org/10.1108/01443579710167276
Dhaenens, C., Finke, G. (2001). An integrated model for an industrial production–distribution problem. IIE Transactions, 33(9), 705-715. https://doi.org/10.1080/07408170108936867
Ekşioğlu, S.D., Romeijn, H.E., Pardalos, P.M. (2006). Cross-facility management of production and transportation planning problem. Computers & Operations Research, 33(11), 3231-3251. https://doi.org/10.1016/j.cor.2005.02.038
European Commission. (2005). The New SME Definition: User Guide and Model Declaration. https://ec.europa.eu/digital-agenda/en/news/new-sme-definition-user-guide-and-model-declaration, Retrieved December 2015.
European Commission. (2014). An Integrated Industrial Policy for the Globalisation Era. Putting Competitiveness and Sustainability at Centre Stage. http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=URISERV%3Aet0005, Retrieved December 2015.
European Commission. Enterprise and Industry. (2012). European Competitiveness Report 2012. Reaping the benefits of globalization. http://wbc-inco.net/object/document/11137# Retrieved December 2015.
European Commission. Enterprise and Industry. (2013). Fact and figures about the EU's Small and Medium Enterprise (SME). http://ec.europa.eu/enterprise/index_en.htm, Retrieved December 2015.
Fildes, R., Goodwin, P., Lawrence, M., Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25(1), 3-23. https://doi.org/10.1016/j.ijforecast.2008.11.010
Fliedner, G. (2003). CPFR: an emerging supply chain tool. Industrial Management & data systems, 103(1), 14-21. https://doi.org/10.1108/02635570310456850
Florez, J.V., Lauras, M., Okongwu, U., Dupont, L. (2015). A decision support system for ro-bust humanitarian facility location. Engineering Applications of Artificial Intelligence. 46, 326-335. http://dx.doi.org/10.1016/j.engappai.2015.06.020
Gelper, S., Fried, R., Croux, C. (2010). Robust forecasting with exponential and Holt-Winters smoothing. Journal of forecasting, 29(3), 285-300. https://doi.org/10.1002/for.1125
Gupta, A., Maranas, C.D. (2003). Managing demand uncertainty in supply chain planning. Computers & Chemical Engineering, 27(8), 1219-1227. https://doi.org/10.1016/S0098-1354(03)00048-6
Jang, Y.J., Jang, S.Y., Chang, B.M., Park, J. (2002). A combined model of network design and production/distribution planning for a supply network. Computers & Industrial Engineering, 43(1), 263-281. https://doi.org/10.1016/S0360-8352(02)00074-8
Jayaraman, V., Pirkul, H. (2001). Planning and coordination of production and distribution facilities for multiple commodities. European journal of operational research, 133(2), 394-408. https://doi.org/10.1016/S0377-2217(00)00033-3
Jung, H., Jeong, B. (2005). Decentralised production-distribution planning system using col-laborative agents in supply chain network. The International Journal of Advanced Manufacturing Technology, 25(1-2), 167-173. https://doi.org/10.1007/s00170-003-1792-x
Lauras, M., Lamothe, J., Benaben, F., Andres, B., Poler, R. (2015). Towards an Agile and Collaborative Platform for Managing Supply Chain Uncertainties. In Enterprise Interoperability, 64-72. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-47157-9_6
Lee, Y.H., Kim, S.H. (2002). Production–distribution planning in supply chain considering capacity constraints. Computers & industrial engineering, 43(1), 169-190. https://doi.org/10.1016/S0360-8352(02)00063-3
Martin, A.J. (1995). DRP: distribution resource planning: the gateway to true quick response and continuous replenishment. John Wiley & Sons.
Meijboom, B., Obel, B. (2007). Tactical coordination in a multi-location and multi-stage oper-ations structure: A model and a pharmaceutical company case. Omega, 35(3), 258-273. https://doi.org/10.1016/j.omega.2005.06.003
Park, Y.B. (2005). An integrated approach for production and distribution planning in supply chain management. International Journal of Production Research, 43(6), 1205-1224. https://doi.org/10.1080/00207540412331327718
Rim, S.C., Jiang, J., Lee, C.J. (2014). Strategic Inventory Positioning for MTO Manufactur-ing Using ASR Lead Time. In Logistics Operations, Supply Chain Management and Sustainability, 441-456. Springer International Publishing.
Rizk, N., Martel, A., D’Amours, S. (2006). Multi-item dynamic production-distribution planning in process industries with divergent finishing stages. Computers & Operations Research, 33(12), 3600-3623. https://doi.org/10.1016/j.cor.2005.02.047
Sakawa, M., Nishizaki, I., Uemura, Y. (2001). Fuzzy programming and profit and cost allocation for a production and transportation problem. European Journal of Operational Research, 131(1), 1-15. https://doi.org/10.1016/S0377-2217(00)00104-1
Santoso, T., Ahmed, S., Goetschalckx, M., Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167(1), 96-115. https://doi.org/10.1016/j.ejor.2004.01.046
Selim, H., Araz, C., Ozkarahan, I. (2008). Collaborative production–distribution planning in supply chain: a fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44(3), 396-419. https://doi.org/10.1016/j.tre.2006.11.001
Supply Chain Council. (2012). Supply Chain Operations Reference Model (SCOR), Supply Chain Operations Management Retrieved from: http://www.apics.org/sites/apics-supply-chain-council/frameworks/scor
Torabi, S.A., Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. https://doi.org/10.1016/j.fss.2007.08.010
Venkatadri, U., Srinivasan, A., Montreuil, B., Saraswat, A. (2006). Optimization-based decision support for order promising in supply chain networks. International Journal of Production Economics, 103(1), 117-130. https://doi.org/10.1016/j.ijpe.2005.05.019
Wang, F., Lai, X., Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262-269. https://doi.org/10.1016/j.dss.2010.11.020
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