Sarimveis, Haralambos and Patrinos, Panagiotis and Tarantilis, Chris D. and Kiranoudis, Chris T. Dynamic modeling and control of supply chain systems: a review. Computers and operations research , 35 (11). pp. 3530-3561. ISSN 0305-0548 (2008)Full text not available from this repository.
Supply chains are complicated dynamical systems triggered by customer demands. Proper selection of equipment, machinery, buildings and transportation fleets is a key component for the success of such systems. However, efficiency of supply chains mostly depends on management decisions, which are often based on intuition and experience. Due to the increasing complexity of supply chain systems (which is the result of changes in customer preferences, the globalization of the economy and the stringy competition among companies), these decisions are often far from optimum. Another factor that causes difficulties in decision making is that different stages in supply chains are often supervised by different groups of people with different managing philosophies. From the early 1950s it became evident that a rigorous framework for analyzing the dynamics of supply chains and taking proper decisions could improve substantially the performance of the systems. Due to the resemblance of supply chains to engineering dynamical systems, control theory has provided a solid background for building such a framework. During the last half century many mathematical tools emerging from the control literature have been applied to the supply chain management problem. These tools vary from classical transfer function analysis to highly sophisticated control methodologies, such as model predictive control (MPC) and neuro-dynamic programming. The aim of this paper is to provide a review of this effort. The reader will find representative references of many alternative control philosophies and identify the advantages, weaknesses and complexities of each one. The bottom line of this review is that a joint co-operation between control experts and supply chain managers has the potential to introduce more realism to the dynamical models and develop improved supply chain management policies.
|Uncontrolled Keywords:||Supply chain management; Control; Dynamic modeling; Review; Dynamic programming; Model predictive control|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
|Research Area:||Computer Science and Applications|
|Date Deposited:||05 Dec 2011 11:00|
|Last Modified:||05 Dec 2011 11:00|
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