Overview of MPC applications in supply chains: Potential use and benefits in the management of forest-based supply chains

Tatiana M. Pinho, A. Paulo Moreira, Germano Veiga, José Boaventura-Cunha

Abstract


Aim of study: This work aims to provide an overview of Model Predictive Controllers (MPC) applications in supply chains, to describe the forest-based supply chain and to analyse the potential use and benefits of MPC in a case study concerning a biomass supply chain.

Area of study: The proposed methods are being applied to a company located in Finland.

Material and methods: Supply chains are complex systems where actions and partners’ coordination influence the whole system performance. The increase of competitiveness and need of quick responses to the costumers implies the use of efficient management techniques. The control theory, particularly MPC, has been successfully used as a supply chain management tool. MPC is able to deal with dynamic interactions between the partners and to globally optimize the supply chain performance in the presence of disturbances. However, as far as is authors’ knowledge, there are no applications of this methodology in the forest-based supply chains. This work proposes a control architecture to improve the performance of the forest supply chain. The controller is based on prediction models which are able to simulate the system and deal with disturbances.

Main results: The preliminary results enable to evaluate the impacts of disturbances in the supply chain. Thus, it is possible to react beforehand, controlling the schedules and tasks’ allocation, or alert the planning level in order to generate a new plan.

Research highlights:   Overview of MPC applications in supply chains; forest-based supply chain description; case study presentation: wood biomass supply chain for energy production; MPC architecture proposal to decrease the operation times.

Keywords: biomass; forest; Model Predictive Control; planning; supply chain.


Keywords


biomass; forest; Model Predictive Control; planning; supply chain

Full Text:

PDF HTML XML

References


References

Al-e-Hashem SMJM, Aryanezhad MB, Sadjadi SJ, 2012. An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment. Int J Adv Manuf Tech 58(5-8): 765-782. http://dx.doi.org/10.1007/s00170-011-3396-1

Alessandri A, Gaggero M, Tonelli F, 2011. Min-Max and Predictive Control for the Management of Distribution in Supply Chains. IEEE Transactions on Control Systems Technology 19(5): 1075-1089. http://dx.doi.org/10.1109/TCST.2010.2076283

Alonso-Ayuso A, Escudero LF, Guignard M, Quinteros M, Weintraub A, 2011. Forestry management under uncertainty. Ann Oper Res 190: 17-39. http://dx.doi.org/10.1007/s10479-009-0561-0

Badole CM, Jain R, Rathore AP, Nepal B, 2012. Research and Opportunities in Supply Chain Modeling: A Review. Int J Sup Chain Mgt 1(3): 63-86.

Beaudoin D, LeBel L, Frayret J-M, 2007. Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis. Can J For Res 37: 128 140. http://dx.doi.org/10.1139/x06-223

Beaudoin D, Frayret J-M, LeBel L, 2008. Hierarchical forest management with anticipation: an application to tactical-operational planning integration. Can J For Res 38: 2198-2211. http://dx.doi.org/10.1139/X08-055

Beaudoin D, Frayret J-M, LeBel L, 2010. Negotiation-based distributed wood procurement planning within a multi-firm environment. Forest Policy and Economics 12: 79-93. http://dx.doi.org/10.1016/j.forpol.2009.07.007

Blanco A, Masini G, Petracci N, Bandoni A, 2008. Model Predictive Control Based Planning in the Fruit Industry. 18th European Symposium on Computer Aided Process Engineering – ESCAPE 18. pp: 1-6.

Bose S, Pekny JF, 2000. A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain. Computers and Chemical Engineering 24: 329-335. http://dx.doi.org/10.1016/S0098-1354(00)00469-5

Braun MW, Rivera DE, Flores ME, Carlyle WM, Kempf KG, 2003. A Model Predictive Control framework for robust management of multi-product, multi-echelon demand networks. Annual Reviews in Control 27: 229-245. http://dx.doi.org/10.1016/j.arcontrol.2003.09.006

Bredström D, Lundgren JT, Rönnqvist M, Carlsson D, Mason A, 2004. Supply chain optimization in the pulp mill industry – IP models, column generation and novel constraint branches. Eur J Oper Res 156: 2-22. http://dx.doi.org/10.1016/j.ejor.2003.08.001

Bredström D, Rönnqvist M, 2008. Combined vehicle routing and scheduling with temporal precedence and synchronization constraints. Eur J Oper Res 191: 19 31. http://dx.doi.org/10.1016/j.ejor.2007.07.033

Brumelle S, Granot D, Halme M, Vertinsky I, 1998. A tabu search algorithm for finding good harvest schedules satisfying green-up constraints. Eur J Oper Res 106: 408-424. http://dx.doi.org/10.1016/S0377-2217(97)00282-8

Camacho EF, Bordons C, 2007. Model Predictive Control, Second Edition. Springer-Verlag London. 405 pp. http://dx.doi.org/10.1007/978-0-85729-398-5

Carlgren C-G, Carlsson D, Rönnqvist M, 2006. Log sorting in forest harvest areas integrated with transportation planning using backhauling. Scand J Forest Res 21: 260-271. http://dx.doi.org/10.1080/02827580600739021

Carlsson D, Rönnqvist M, 2005. Supply chain management in forestry – case studies at Södra Cell AB. Eur J Oper Res 163: 589-616. http://dx.doi.org/10.1016/j.ejor.2004.02.001

Carlsson D, Rönnqvist M, 2007. Backhauling in forest transportation: models, methods, and practical usage. Can J For Res 37: 2612-2623. http://dx.doi.org/10.1139/X07-106

Cea C, Jofré A, 2000. Linking strategic and tactical forestry planning decisions. Ann Oper Res 95: 131-158. http://dx.doi.org/10.1023/A:1018978813833

Chauhan SS, Frayret J-M, LeBel L, 2009. Multi-commodity supply network planning in the forest supply chain. Eur J Oper Res 196: 688-696. http://dx.doi.org/10.1016/j.ejor.2008.03.024

Chauhan SS, Frayret J-M, LeBel L, 2011. Supply network planning in the forest supply chain with bucking decisions anticipation. Ann Oper Res 190: 93-115. http://dx.doi.org/10.1007/s10479-009-0621-5

Church RL, Murray AT, Figueroa MA, Barber KH, 2000. Support system development for forest ecosystem management. Eur J Oper Res 121: 247-258. http://dx.doi.org/10.1016/S0377-2217(99)00215-5

Clark MM, Meller RD, McDonald TP, Ting CC, 2000. A new harvest operation cost model to evaluate forest harvest layout alternatives. Ann Oper Res 95: 115 129. http://dx.doi.org/10.1023/A:1018914527037

Dems A, Rousseau L-M, Frayret J-M, 2013. Effects of different cut-to-length harvesting structures on the economic value of a wood procurement planning problem. Ann Oper Res 1 22. http://dx.doi.org/10.1007/s10479-013-1336-1

Doganis P, Aggelogiannaki E, Sarimveis H, 2008. A Model Predictive Control and Time Series Forecasting Framework for Supply Chain Management. World Academy of Science, Engineering and Technology 15: 766-770.

Dunbar WB, Desa S, 2005. Distributed Model Predictive Control for Dynamic Supply Chain Management. Proceedings of the Int. Workshop on Assessment and Future Directions of Nonlinear MPC. pp: 1-10.

Eshlaghy AT, Razavi M, 2011. Modeling and Simulating Supply Chain Management. Applied Mathematical Sciences 5(17): 817-828.

Forsberg M, Frisk M, Rönnqvist M, 2005. FlowOpt – a decision support tool for strategic and tactical transportation planning in forestry. Int J For Eng 16(2): 101-114.

Fu D, Ionescu CM, Aghezzaf E-H, Keyser RD, 2013. A Centralized Model Predictive Control Strategy for Dynamic Supply Chain Management. 2013 IFAC Conference on Manufacturing Modelling, Management, and Control, Saint Petersburg, Russia, June 19-21. pp: 1630-1635.

Fu D, Aghezzaf E-H, Keyser RD, 2014. A model predictive control framework for centralised management of a supply chain dynamical system. Systems Science & Control Engineering: An Open Access Journal 2: 250-260. http://dx.doi.org/10.1080/21642583.2014.895449

Goycoolea M, Murray AT, Barahona F, Epstein R, Weintraub A, 2005. Harvest Scheduling Subject to Maximum Area Restrictions: Exploring Exact Approaches. Operations Research 53(3): 490-500. http://dx.doi.org/10.1287/opre.1040.0169

Guinard M, Ryu C, Spielberg K, 1998. Model tightening for integrated timber harvest and transportation planning. Eur J Oper Res 111: 448-460. http://dx.doi.org/10.1016/S0377-2217(97)00362-7

Gunn EA, Richards EW, 2005. Solving the adjacency problem with stand-centred constraints. Can J For Res 35: 832-842. http://dx.doi.org/10.1139/x05-013

Hachemi NE, Gendreau M, Rousseau L-M, 2008. Solving a Log-Truck Scheduling Problem with Constraint Programming. In L. Perron and M. Trick (Eds.): CPAIOR 2008, LNCS 5015. pp: 293-297. http://dx.doi.org/10.1007/978-3-540-68155-7_25

Hachemi NE, Gendreau M, Rousseau L-M, 2011. A hybrid constraint programming approach to the log-truck scheduling problem. Ann Oper Res 184: 163-178. http://dx.doi.org/10.1007/s10479-010-0698-x

Hachemi NE, Gendreau M, Rousseau L-M, 2013. A heuristic to solve the synchronized log-truck scheduling problem. Computers & Operations Research 40: 666-673. http://dx.doi.org/10.1016/j.cor.2011.02.002

Hai D, Hao Z, Ping LY, 2011. Model Predictive Control for inventory Management in Supply Chain Planning. Procedia Engineering 15: 1154-1159. http://dx.doi.org/10.1016/j.proeng.2011.08.213

Han H, Qiao J, 2014. Nonlinear Model-Predictive Control for Industrial Processes: An Application to Wastewater Treatment Process. IEEE Transactions on Industrial Electronics 61(4): 1970-1982. http://dx.doi.org/10.1109/TIE.2013.2266086

Heinimann HR, 2010. A concept in adaptive ecosystem management – An engineering perspective. For Ecol Manag 259: 848-856.

Heinonen T, Pukkala T, 2004. A Comparison of One- and Two-Compartment Neighbourhoods in Heuristic Search with Spatial Forest Management Goals. Silva Fennica 38(3): 319-332. http://dx.doi.org/10.14214/sf.419

Henningsson M, Karlsson J, Rönnqvist M, 2007. Optimization Models for Forest Road Upgrade Planning. J Math Model Algor 6: 3-23. http://dx.doi.org/10.1007/s10852-006-9047-0

Huang D, Sarjoughian HS, Wang W, Godding G, Rivera DE, Kempf KG, Mittelmann H, 2009. Simulation of Semiconductor Manufacturing Supply-Chain Systems With DEVS, MPC, and KIB. IEEE Transactions on Semiconductor Manufacturing 22(1): 164-174. http://dx.doi.org/10.1109/TSM.2008.2011680

Ivanov D, Dolgui A, Sokolov B, 2012. Applicability of optimal control theory to adaptive supply chain planning and scheduling. Ann Rev Control 36: 73-84. http://dx.doi.org/10.1016/j.arcontrol.2012.03.006

Janamanchi B, Burns JR, 2013. Control Theory Concepts Applied to Retail Supply Chain: A System Dynamics Modeling Environment Study. Model Simul Engineering 2013: 1-15. http://dx.doi.org/10.1155/2013/421350

Kapsiotis G, Tzafestas S, 1992. Decision making for inventory/production planning using model-based predictive control. Parallel and distributed computing in engineering systems: 551 556.

Karlsson J, Rönnqvist M, Bergström J, 2003. Short-term harvest planning including scheduling of harvest crews. Int Trans Op Res 10: 413-431. http://dx.doi.org/10.1111/1475-3995.00419

Karlsson J, Rönnqvist M, Bergström J, 2004. An optimization model for annual harvest planning. Can J For Res. 34: 1747-1754. http://dx.doi.org/10.1139/x04-043

Kawtar T, Said A, Youssef B, 2014. Stochastic Model Predictive Control for optimization costs in multi-level supply chain. Int J Computer Sci Issues 11(1-2): 28-32.

Laurikkala H, Ketonen M, Suominen S, Huttunen P, Alaruka J, 2005. Demand estimation and dynamic modelling as timber products industry SCM tools. World Congress 16(1): 1642 1648.

Lee HL, Padmanabhan V, Whang S, 1997. Information Distortion in a Supply Chain: The Bullwhip Effect. Manag Sci 43(4): 546-558. http://dx.doi.org/10.1287/mnsc.43.4.546

Li J, Zhai J, Chen Y, Liu S, 2010. System Dynamic Simulation Approach for Supply Chain with Capability Limit J Computers 5(7): 1125-1132.

Li J, Lin Y, Jin F, 2012. A Supply Chain Simulation Model with Customer's Satisfaction. Int J Adv Computing Technol (IJACT) 4(10): 125-132. http://dx.doi.org/10.4156/ijact.vol4.issue10.15

Li X, Marlin TE, 2009. Robust supply chain performance via Model Predictive Control. Computers Chemical Engineering 33: 2134-2143. http://dx.doi.org/10.1016/j.compchemeng.2009.06.029

Liu G, Han S, Zhao X, Nelson JD, Wang H, Wang W, 2006. Optimisation algorithms for spatially constrained forest planning. Ecological Modelling 194: 421-428. http://dx.doi.org/10.1016/j.ecolmodel.2005.10.028

Maestre JM, Peña DM, Camacho EF, 2009. Distributed MPC: a supply chain case study. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, P.R. China, December 16-18. pp: 7099-7104. http://dx.doi.org/10.1109/cdc.2009.5400590

Marshall HD, Murphy G, Boston K, 2006. Three Mathematical Models for Bucking-to-Order. Silva Fennica 40(1): 127-142. http://dx.doi.org/10.14214/sf.356

Marques AF, Borges JG, Sousa P, Pinho AM, 2011. An enterprise architecture approach to forest management support systems design: an application to pulpwood supply management in Portugal. Eur J Forest Res 130: 935-948. http://dx.doi.org/10.1007/s10342-011-0482-8

Martins I, Constantino M, Borges JG, 2005. A column generation approach for solving a non-temporal forest harvest model with spatial structure constraints. Eur J Oper Res 161: 478-498. http://dx.doi.org/10.1016/j.ejor.2003.07.021

Mastragostino R, Patel S, Swartz CLE, 2014. Robust decision making for hybrid process supply chain systems via model predictive control. Computers Chemical Engineering 62: 37-55. http://dx.doi.org/10.1016/j.compchemeng.2013.10.019

Mentzer JT, DeWitt W, Keebler JS, Min S, Nix NW, Smith CD, Zacharia ZG, 2001. Defining supply chain management. J Business Logistics 22(2): 1-25. http://dx.doi.org/10.1002/j.2158-1592.2001.tb00001.x

Mestan E, Türkay M, Arkun Y, 2006. Optimization of Operations in Supply Chain Systems Using Hybrid Systems Approach and Model Predictive Control Ind Eng Chem Res 45: 6493 6503.

Min H, Zhou G, 2002. Supply chain modeling: past, present and future. Computers & Industrial Engineering 43: 231-249. http://dx.doi.org/10.1016/S0360-8352(02)00066-9

Miranbeigi M, Jalali A, Miranbeigi A, 2010. A Constrained Inventory Level Optimal Control on Supply Chain Management System. Int J Innov Manag Technol 1(1): 69-74.

Mourtzis D, Papokostas N, Makris S, Xanthakis V, Chryssolouris G, 2008. Supply chain modeling and control for producing highly customized products. CIRP Ann Manufacturing Technol 57: 451-454. http://dx.doi.org/10.1016/j.cirp.2008.03.106

Niu J, Zhao J, Xu Z, Shao Z, Qian J, 2013. Model predictive control with dynamic pricing and probability inventory of a single supply chain unit. Asia-Pac J Chem Eng 8: 547 554. http://dx.doi.org/10.1002/apj.1695

Palander T, Väätäinen J, 2005. Impacts of interenterprise collaboration and backhauling on wood procurement in Finland. Scand J Forest Res 20: 177-183. http://dx.doi.org/10.1080/02827580510008301

Palander T, Kainulainen J, Koskinen R, 2005. A computer-supported group decision making system for timber procurement planning in Finland. Scand J Forest Res 20: 514-520. http://dx.doi.org/10.1080/02827580500339823

Pannek J, Frazzon E, 2014. Supply Chain Optimization via Distributed Model Predictive Control. Proc Appl Math Mech 14: 905-906. http://dx.doi.org/10.1002/pamm.201410433

Park B-c, Jeong S, 2014. A Modeling Framework of Supply Chain Simulation. J Supply Chain Oper Manag 12(2): 91-106.

Parmigiani A, Klassen RD, Russo MV, 2011. Efficiency meets accountability: Performance implications of supply chain configuration, control, and capabilities. J Oper Manag 29: 212-223. http://dx.doi.org/10.1016/j.jom.2011.01.001

Perea-López E, Ydstie BE, Grossmann IE, 2003. A model predictive control strategy for supply chain optimization. Computers Chemical Engineering 27: 1201-1218. http://dx.doi.org/10.1016/S0098-1354(03)00047-4

Petrovic D, Roy R, Petrovic R, 1999. Supply chain modelling using fuzzy sets. Int J Production Economics 59: 443-453. http://dx.doi.org/10.1016/S0925-5273(98)00109-1

Puigjaner L, Laínez JM, 2008. Capturing dynamics in integrated supply chain management. Computers Chemic Engin 32: 2582-2605. http://dx.doi.org/10.1016/j.compchemeng.2007.10.003

Rong A, Akkerman R, Grunow M, 2011. An optimization approach for managing fresh food quality throughout the supply chain. Int J Produc Econom 131: 421-429. http://dx.doi.org/10.1016/j.ijpe.2009.11.026

Rönnqvist M, 2003. Optimization in forestry. Math Program Ser. B 97: 267-284.

Rönnqvist M, Sahlin H, Carlsson D, 1998. Operative Planning Dispatching Forestry Transportation. LiTH-MAT-R. pp: 1-31.

Sarimveis H, Patrinos P, Tarantilis CD, Kiranoudis CT, 2008. Dynamic modeling and control of supply chain systems: A review. Computers & Operations Research 35: 3530-3561. http://dx.doi.org/10.1016/j.cor.2007.01.017

Seborg DE, Edgar TF, Mellichamp DA, 2004. Process dynamics and control, Second Edition. John Wiley & Sons, Inc., USA. 713 pp.

Seferlis P, Giannelos NF, 2004. A two-layered optimisation-based control strategy for multi-echelon supply chain networks. Computers Chemic Engin 28: 799-809. http://dx.doi.org/10.1016/j.compchemeng.2004.02.022

Seuring S, 2011. Supply Chain Management for Sustainable Products – Insights From Research Applying Mixed Methodologies. Bus Strat Env 20: 471-484. http://dx.doi.org/10.1002/bse.702

Stevens GC, 1989. Integrating the Supply Chain. International Journal of Physical Distribution & Materials Management 19(8): 3-8. http://dx.doi.org/10.1108/EUM0000000000329

Subramanian K, Rawlings JB, Maravelias CT, Flores-Cerrillo J, Megan L, 2013. Integration of control theory and scheduling methods for supply chain management. Computers Chemic Engin 51: 4-20. http://dx.doi.org/10.1016/j.compchemeng.2012.06.012

Subramanian K, Rawlings JB, Maravelias CT, 2014. Economic model predictive control for inventory management in supply chains Computers Chemic Engin 64: 71-80.

Swaminathan JM, Smith SF, Sadeh NM, 1998. Modeling Supply Chain Dynamics: A Multiagent Approach. Decision Sciences 29(3): 607-632. http://dx.doi.org/10.1111/j.1540-5915.1998.tb01356.x

Tzafestas S, Kapsiotis G, Kyriannakis E, 1997. Model-based predictive control for generalized production planning problems. Computers in Industry 34: 201-210. http://dx.doi.org/10.1016/S0166-3615(97)00055-9

Varma VK, Ferguson I, Wild I, 2000. Decision support system for the sustainable forest management. For Ecol Manag 128: 49-55.

Vorst JGAJ, Beulens AJM, 2002. Identifying sources of uncertainty to generate supply chain redesign strategies. International Journal of Physical Distribution & Logistics Management 32(6): 409-430. http://dx.doi.org/10.1108/09600030210437951

Wang J, 2013. Model Predictive Control Strategy for Petrochemical Supply Chain Planning under Uncertainty. 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), Shenyang, China, Dec 20-22. pp: 27-30. http://dx.doi.org/10.1109/mec.2013.6885045

Wang W, Rivera DE, 2008. Model Predictive Control for Tactical Decision-Making in Semiconductor Manufacturing Supply Chain Management. IEEE Transactions on Control Systems Technology 16(5): 841-855. http://dx.doi.org/10.1109/TCST.2007.916327

Wang W, Rivera DE., Kempf KG, Smith KD, 2004. A Model Predictive Control Strategy for Supply Chain Management in Semiconductor Manufacturing under Uncertainty. 2004 American Control Conference, Boston, MA, June 30-July 2. pp: 4577-4582.

Wang W, Rivera DE, Kempf KG, 2007. Model predictive control strategies for supply chain management in semiconductor manufacturing. Int J Produc Econom 107: 56-77. http://dx.doi.org/10.1016/j.ijpe.2006.05.013

Wang Y, Boyd S, 2010. Fast Model Predictive Control Using Online Optimization, IEEE Transactions on Control Systems Technology 18(2): 267-278. http://dx.doi.org/10.1109/TCST.2009.2017934




DOI: 10.5424/fs/2015243-08148

Webpage: www.inia.es/Forestsystems