journal article·2022-05-25Open access

Mathematical Modeling and Optimal Control for a Class of Dynamic Supply Chain: A Systems Theory Approach

Heidi Taboada, Yasser A. Davizón, José F. Espíritu, Jaime Sánchez-Leal

Applied Sciences· Volume 12· Issue 11

Abstract

Dynamic supply chains (SC) are important to reduce inventory, enable the flow of materials, maximize profits, and minimize costs. This research work presents a capacity–inventory management model via system dynamics for a dynamic supply SC, applying model-based optimal control techniques. In the context of high-volume manufacturing (HVM) that present low variability and predictable demand, for mathematical modeling purposes, a set of coupled first-order ordinary differential equations, with an analogy from the mixing problem, is presented, which relates capacity and inventory levels, taking into account a production rate at each node of interaction. The application of ordinary differential equations via the mixing problem (or compartmental analysis) is important based on the idea of a balance between the influx and outflux of raw material along the supply chain. A proper literature review on optimal control for supply chains is analyzed. The mathematical model introduced is presented in a linear time-invariant (LTI) state-space formulation. Stability analysis for the dynamic serial SC is presented, and a sensitivity analysis is also conducted for the capacity and production rate parameters considering the effects of variations in parameters along the SC. An energy-based optimal control is also developed with proper simulations.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Supply Chain and Inventory Management · Scheduling and Optimization Algorithms · Advanced Queuing Theory Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Mechanical and Industrial Engineering, Texas A&M University-Kingsville, 700 University Blvd, Kingsville, TX 78363, USA

Department of Information Technology, Universidad Politécnica del Mar y la Sierra, Potrerillos del Norote, Tayoltita 82740, Mexico

Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA

References (40)

Show source-provided references
1. Optimal control in dynamic food supply chains using big data10.1016/j.cor.2020.105117 ↗
2. The investment strategies for a dynamic supply chain under stochastic demands10.1016/j.ijpe.2011.11.021 ↗
3. Dynamic supply chain modeling using a new fuzzy hybrid negotiation mechanism10.1016/j.ijpe.2009.06.034 ↗
4. Revisiting information systems to support a dynamic supply chain: Issues and perspectives10.1080/09537280802608019 ↗
5. Analyzing manufacturing problems using V-A-T analysis
6. Control architectures for industrial additive manufacturing systems10.1177/0954405417703420 ↗
7. A dynamic perspective on the key drivers of innovation-led lean approaches to achieve sustainability in manufacturing supply chain10.1016/j.ijpe.2018.12.002 ↗
8. Supply chain performance measurement: A literature review10.1080/00207540903089536 ↗
9. Inventory management in supply chain10.1016/j.matpr.2017.11.641 ↗
10. Diagnosing inventory management systems: An empirical evaluation of a conceptual approach10.1016/j.ijpe.2006.12.003 ↗
11. Cooperative game theory and inventory management10.1016/j.ejor.2010.06.025 ↗
12. The ubiquitous nature of inventory: Vendor Managed Consignment Inventory in adverse market conditions10.1016/j.ejor.2019.07.070 ↗
13. A dynamic Supplier Selection and Inventory Management Model for a Serial Supply Chain with a Novel Supplier Price Break Scheme and Flexible Time Periods10.1016/j.ejor.2018.07.031 ↗
14. A fuzzy echelon approach for inventory management in supply chains10.1016/S0377-2217(02)00441-1 ↗
15. Inventory management in supply chains: A reinforcement learning approach10.1016/S0925-5273(00)00156-0 ↗
16. The empirical determinants of inventory levels in high-volume manufacturing10.1111/j.1937-5956.1999.tb00060.x ↗
17. Wiener, N. (1948). Cybernetics or Control and Communications in the Animal and the Machines, The MIT Press.
18. Davizon, Y.A. (2020). Mathematical Modeling, Optimal Control and Stability Analysis for Dynamic Supply Chains. [Ph.D. Thesis, The University of Texas at El Paso].
19. Optimal integrated ordering and production policy in a supply chain with stochastic lead-time, processing-time, and demand10.1109/TAC.2009.2026925 ↗
20. Supply chain with inventory review and dependent demand distributions: Dynamic inventory outsourcing10.1109/TASE.2008.2006569 ↗
21. Linear–quadratic optimal control strategy for periodic-review inventory systems10.1016/j.automatica.2010.09.010 ↗
22. Linear-Quadratic optimal control of periodic-review Perishable inventory systems10.1109/TCST.2011.2161086 ↗
23. Applicability of optimal control theory to adaptive supply chain planning and scheduling10.1016/j.arcontrol.2012.03.006 ↗
24. Robust dynamic schedule coordination control in the supply chain10.1016/j.cie.2016.01.009 ↗
25. A survey on control theory applications to operational systems, supply chain management, and Industry 4.010.1016/j.arcontrol.2018.10.014 ↗
26. Scheduling in production, supply chain and Industry 4.0 systems by optimal control: Fundamentals, state-of-the-art and applications10.1080/00207543.2018.1442948 ↗
27. Inter-Organizational control of low-carbon production in a supply chain10.1109/ACCESS.2019.2954916 ↗
28. Optimal control approach to advertising strategies of a supply chain under consignment contract10.1109/ACCESS.2019.2907778 ↗
29. New optimal-control-based advertising strategies and coordination of a supply chain with differentiated products under consignment contract10.1109/ACCESS.2019.2956287 ↗
30. An optimal control model of the low-carbon supply chain: Joint emission reduction, pricing strategies, and new coordination contract design10.1109/ACCESS.2020.3000482 ↗
31. Numerical schemes and genetic algorithms for the optimal control of a continuous model of supply chains
32. He, Y., Zheng, S., and Yuan, L. (2021). Dynamics of Fractional-Order Digital Manufacturing Supply Chain System and Its Control and Synchronization. Fractal Fract., 5.10.3390/fractalfract5030128 ↗
33. Kegenbekov, Z., and Jackson, I. (2021). Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning. Algorithms, 14.10.3390/a14080240 ↗
34. Badillo, I., Tejeida, R., Morales, O., and Flores, M. (2011). Supply Chain Management from a Systems Science Perspective, Supply Chain Management—New Perspectives, Intech.10.5772/22229 ↗
35. Zill, D.G. (2017). Advanced Engineering Mathematics, Jones & Bartlett Learning. [6th ed.].
36. Feedback controls for continuous priority models in supply chain management10.2478/cmam-2011-0011 ↗
37. Hassan, A.Y. (2018). Square Matrices Associated to Mixing Problems ODE Systems. Matrix Theory—Applications and Theorems, Intech.
38. Bassingthwaighte, J.B., Butterworth, E., Jardine, B., and Raymond, G.M. (2012). Compartmental Modeling in the Analysis of Biological Systems. Toxicology: Volume I, Methods in Molecular Biology, Springer.10.1007/978-1-62703-050-2_17 ↗
39. Digital implementation of the two-compartmental Pinsky–Rinzel pyramidal neuron model10.1109/TBCAS.2017.2753541 ↗
40. Fogarty, D.W., Blackstone, J.H., and Hoffmann, T.R. (1991). Production & Inventory Management, South-Western Publishing Company.