Control Theory Applications in Logistics – MPC and other Approaches

• Autorzy: Orzechowska J. , Bartoszewicz A. , Burnham K. J. , Petrovic D.

Warianty tytułu

PL

Zastosowanie metod teorii regulacji w logistyce - podejście klasyczne i sterowanie predykcyjne

• Języki publikacji: EN

Abstrakty

EN

The paper concerns an application of engineering regulation theory concepts to modelling and effective control of logistic systems. Nowadays an achievement of inventory keeping cost vs. benefit trade-off becomes extremely important. This is, however, a complex task with respect to uncertain demand and lead times. These uncertainties result in such problems as high storage costs, varying inventory levels (bullwhip effect) and deterioration of goods. The paper shows a brief review of contributions made in this area of study with special focus on Model Predictive Control.

PL

Artykuł przedstawia krótki przegląd zastosowań metod teorii regulacji do modelowania i sterowania systemów logistycznych. Ponieważ osiągnięcie takiego poziomu zapasów, aby zredukować koszty ich magazynowania i jednocześnie zachować ciągłość podaży nie jest zadaniem łatwym, uzasadnione jest stosowanie do tego celu obecnie dobrze rozwiniętych metod teorii sterowania. Artykuł przedstawia zwięzły przegląd literatury dotyczący tej tematyki ze szczególnym uwzględnieniem bardzo skutecznych metod sterowania predykcyjnego.

Słowa kluczowe

EN

inventory management; control theory; model predictive control

PL

zarządzanie zapasami; teoria sterowania; model sterowania predykcyjnego

• Wydawca: Sieć Badawcza Łukasiewicz - Poznański Instytut Technologiczny
• Czasopismo: Logistyka
• Rocznik: 2012
• Tom: nr 3
• Opis fizyczny: Bibliogr. 52 poz., rys., pełen tekst na CD

Twórcy

autor

Orzechowska J.

• Coventry University, Control Theory and Application Centre; Priory Street, Coventry CV1 5FD

autor

Bartoszewicz A.

• Politechnika Łódzka, Instytut Automatyki; 90-924 Łódź, ul. Bohdana Stefanowskiego 18/22

autor

Burnham K. J.

• Coventry University, Control Theory and Application Centre; Priory Street, Coventry CV1 5FD

autor

Petrovic D.

• Coventry University, Control Theory and Application Centre; Priory Street, Coventry CV1 5FD

Bibliografia

• [1] Aggelogiannaki, E., Doganis, P. & Sarimveis, H., 2008. An adaptive model predictive control configuration for production-inventory systems. International Journal of Production Economics, 114(1), 165-178.
• [2] Aggelogiannaki, E. & Sarimveis, H., 2008. Design of a novel adaptive inventory control system based on the online identification of lead time. International Journal of Production Economics, 114(2), 781-792.
• [3] Agrell, P. J. & Wikner, J. 1996. An MCDM framework for dynamic systems. International Journal of Production Economics, 45(1-3), 279-292.
• [4] Ahmadi, Javid & Azad, N., 2010. Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 46(5), 582-597.
• [5] Avinadav, T.& Arponen, T., 2009. An EOQ model for items with a fixed shelf-life and a declining demand rate based on time-to-expiry. Journal of Operational Research, 26( 6), 759-767.
• [6] Baumol, W.J. & Vinod, H.D.,1970. An inventory theoretic model of freight transport demand, Management Science, 16, 413–421.
• [7] Braun, M., Rivera E.D., Flores, M.E., Carlyle, W.M. & Kempf, K.G., 2003. A model predictive control framework for robust management of multi-product, multi-echelon demand networks. Annual Reviews in Control, 27(2), 229.
• [8] Chung , K. J. & Liao, J.J., 2009. The optimal ordering policy of the EOQ model under trade credit depending on the ordering quantity from the DCF approach. European Journal of Operational Research, 196 (2), 563-568.
• [9] Dejonckheere, J., Disney, S.M., Lambrecht, M.R. & Towill, D.R. 2002. Transfer function analysis of forecasting induced bullwhip in supply chains. International Journal of Production Economics, 78(2), 133-144.
• [10] Dejonckheere, J., Disney, S.M., Lambrecht, M.R. & Towill, D.R. 2003. Measuring and avoiding the bullwhip effect: a control theoretic approach. European Journal of Operational Research, 147(3), 657-590.
• [11] Dejonckheere, J., Disney, S.M., Lambrecht, M.R. & Towill, D.R. 2004. The impact of information on the bullwhip effect in supply chains: a control engineering perspective. European Journal of Operational Research, 153(3), 727- 750.
• [12] Disney, S.M. & Towill, D.R., 1996. Industrial dynamics modelling of supply chains. International Journal of Physical Distribution & Logistics Management, 26 (2), 23-42.
• [13] Disney, S.M. & Towill, D.R., 2002. A discrete transfer function model to determine the dynamic stability of a vendor management inventory supply chain, Industrial Journal of Production Research, 40 (1), 179-204.
• [14] Disney, S.M. & Towill, D.R. 2003a. Vendor-managed inventory and bullwhip reduction in a two-level supply chain. International Journal of Operation and Production Management, 23(6), 625-651.
• [15] Disney, S.M. & Towill, D.R., 2003b. On the bullwhip and inventory variance produced by an ordering policy, OMEGA: The International Journal of Management Science, 31 (3), 157-167.
• [16] Disney, S.M. & Towill, D.R. 2005. Elimination drift in inventory and order based production control systems. International Journal of Production Economics, 93-94(8), 331-344.
• [17] Disney, S.M. & Towill, D.R., 2006. A methodology for benchmarking replenishment-induced bullwhip, An International Journal of Supply Chain Management, 11 (2), 160-168.
• [18] Ehrhardt, R.,1997. A model of JIT make-to-stock inventory with stochastic demand. Journal of the Operational Research Society, 48 (10), 1013-1020.
• [19] Feng, Y. et al., 2011. Solving single-product economic lot-sizing problem with non-increasing setup cost, constant capacity and convex inventory cost in O(N log N) time. Computers & Operations Research, 38(4), 717-722.
• [20] Forrester, J., 1958. Industrial dynamics, a major breakthrough for decision makers, Harvard Business Review, July– August, 36(4), 37–66.
• [21] Gaalman, G. & Disney, S.M., 2006. State space investigation of the bullwhip problem with ARMA(1,1) demand processes. International Journal of Production Economics, 104(2), 327-339.
• [22] Gaalman, G., 2006. Bullwhip reduction for ARMA demand: the proportional order-up-to policy versus the full-statefeedback policy. Automatica, 42(8), 1283-1290.
• [23] Grubbström, R.W. & Wikner, J., 1996. Inventory trigger control policies developed in terms of control theory. International Journal of Production Economics, 45(1-3), 397-406.
• [24] Hoberg, K., Bradley, J.R. & Thonemann, U.W., 2007. Analyzing the effect of the inventory policy on order and inventory variability with linear control theory. European Journal of Operational Research, 176(3), 1620-1642.
• [25] Hsu K. & Wen-Kai, 2009. EOQ Model for imperfective items under a one-time-only discount. Omega 37(5), 1018- 1026.
• [26] Ignaciuk, P. & Bartoszewicz, A., 2011. Dead-time compensation in continuous-review perishable inventory systems with a remote supply source. Archives of Control Sciences, 21(1), 5-28.
• [27] Ignaciuk, P. & Bartoszewicz, A., 2010a. Linear-quadratic optimal control strategy for periodic-review inventory systems. Automatica, 46(12), 1982-1993.
• [28] Ignaciuk, P. & Bartoszewicz, A., 2010b. Smith predictor based control of continuous-review perishable inventory systems with a single supply source. 8th European Workshop on Advanced Control and Diagnosis.
• [29] Ignaciuk, P. & Bartoszewicz, A., 2010c. LQ optimal sliding mode supply policy for periodic inventory systems. IEEE Transactions on Automatic Control, 55(1), 269-274.
• [30] Ignaciuk, P. & Bartoszewicz A., 2010d. An inventory management in periodic-review systems with multiple capacitated suppliers and batch ordering. in Applications of Systems Science, Exit Publishing House, Warszawa, 307-314. 1774
• [31] Köchel, P. & Nieländer, U., 2005. Simulation-based optimisation of multi-echelon inventory systems. International Journal of Production Economics, 93-94, pp. 505-513.
• [32] Konstantaras, I. and Skouri, K., 2010. Lot sizing for a single product recovery system with variable setup numbers. European Journal of Operational Research, 203(2), 326-335.
• [33] Lalwani, C.S., Disney, S.M. & Towill, D.R., 2006. Controllable, observable and stable state space representations of a generalized order-up-to policy. International Journal of Production Economics, 101(1), 172-184.
• [34] Li, J., Edwin Cheng, T. and Wang, S., 2007. Analysis of postponement strategy for perishable items by EOQ-based models. International Journal of Production Economics, 107(1), 31-38.
• [35] Li, X. & Marlin, T.E., 2009. Robust supply chain performance via Model Predictive Control. Computers & Chemical Engineering, 33(12), 2134-2143.
• [36] Lin, P.-H., Wong, D.S.-H., Jang, S.-S., Shieh, S.-S. & Chu, J.-Z., 2004. Controller design and reduction of bullwhip for a model supply chain system using Z-transform analysis. Journal of Process Control, 14(5), 487-499.
• [37] Liu, S.-C. & Chen, J.-R., 2011. A heuristic method for the inventory routing and pricing problem in a supply chain. Expert Systems with Applications, 38(3), pp. 1447-1456.
• [38] Mariani, L. & Nicoletti, B., 1973. Optimization of deterministic, multiproduct inventory model with joint replenishment. Management Science, 20(3), 349-362.
• [39] Pasandideh, S.H.R., Niaki, S.T.A. & Nia, A.R., 2011. A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model. Expert Systems with Applications, 38(3), 2708-2716.
• [40] Perea-Lopez, E., Ydstie, B.E. & Grossmann, I.E. 2003. A model predictive control strategy for supply chain optimisation. Computers & Chemical Engineering, 27(8/9), 1201-1218.
• [41] Potter, A., Towill, D.R., Bohme, T. & Disney, S.M., 2009. The influence of multi-product production strategy on factory induced bullwhip. International Journal of Production Research, 47(20), 5739-5759.
• [42] Rodrigues, L. & Boukas, E. K., 2006. Piecewise-linear H¥ controller synthesis with application to inventory control of switched production systems. Automatica, 42(1), 1245-1254.
• [43] Samanta, B. & Al-Araimi, S.A., 2001. An inventory control model using fuzzy logic. International Journal of Production Economics, 73(3), 217-226.
• [44] Simon, H.A., 1952. On the application of servomechanism theory in the study of production control. Econometrica, 20(2), 247–268.
• [45] Towill, D.R. 1982. Dynamic analysis of an inventory and order based production control system. International Journal of Production Research, 20(6), 671-687.
• [46] Tzafestas, S., Kapsiotis, G. & Kyriannakis, E., 1997. Model-based predictive control for generalized production planning problems. Computers in Industry, 34(2), 201-210.
• [47] Vassian, H.J, 1954. Application of discrete variable servo theory to inventory control. Arthur D. Little, Inc., Cambridge Massachusetts.
• [48] Venkateswaran, J. & Son, Y.-J., 2006. Effect of information update frequency on the stability of production– inventory control systems International Journal of Production Economics, 106 (1), 171-190.
• [49] Wang, W., Rivera, D.E. & Kempf, K.G., 2007. Model predictive control strategies for supply chain management in semiconductor manufacturing. International Journal of Production Economics, 107(1), 56-77.
• [50] Wikner, J., Towill, D.R. & Naim, M., 1991. Smoothing supply chain dynamics. International Journal of Production Economics, 22(3), 231-248.
• [51] Zafra-Cabeza, A., Ridao, D.E., Camacho, E.F., Kempf, K.G, & Rivera, D.E., 2007, Managing risk in semiconductor manufacturing: A stochastic predictive control approach. Control Engineering Practice, 15(8), 969-984.
• [52] Zhou, L., Disney S. & Towill, D.R., 2010. A pragmatic approach to the design of bullwhip controllers. International Journal of Production Economics, 128(2), 556-568.