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Applicability of optimal control theory to adaptive supply chain planning and scheduling

Abstract : Decisions in supply chain planning and control are interconnected and depend on tackling uncertainties and dynamics. From this perspective, control theory (CT) is an interesting research avenue for the supply chain management (SCM). In this paper, the applicability of optimal CT to SCM is investigated. Our analysis is based on the fundamentals of control and systems theory and experimental modeling. The paper describes important issues and perspectives that delineate dynamics in supply chains, identifies and systemizes different streams in application of CT to production, logistics, and SCM in the period from 1960 to 2011. It derives some classifications, performs a critical analysis, and discusses further researches. Some drawbacks and missing links in the literature are pointed out. Several crucial application areas of control theory to SCM are discussed. Subsequently, optimal program control, challenges and advantages of its application in the SCM are addressed. It is shown how optimal program control can be applied to adaptive supply chain planning. In addition, it is concluded that with the help of CT, robustness, adaptability, and resilience of supply chains can be investigated in their consistency with operations planning and execution control within a conceptually and mathematically integrated framework. However, although SCs resemble control systems, they have some peculiarities which do not allow a direct application of CT methods. In this setting, further development of interdisciplinary approaches to supply chain optimization is argued. An extended co-operation between control and supply chain experts may have the potential to introduce more realism to the dynamic planning and models and improve real-time supply chain control policies.
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Submitted on : Wednesday, May 2, 2012 - 5:44:58 PM
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Dmitri Ivanov, Alexandre Dolgui, Boris Sokolov. Applicability of optimal control theory to adaptive supply chain planning and scheduling. Annual Reviews in Control, Elsevier, 2012, Volume 36 (Issue 1), pp.Pages 73-84. ⟨10.1016/j.arcontrol.2012.03.006⟩. ⟨emse-00693526⟩



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