eprintid: 1025
rev_number: 9
eprint_status: archive
dir: disk0/00/00/10/25
datestamp: 2011-12-05 11:00:57
lastmod: 2011-12-05 11:00:57
status_changed: 2011-12-05 11:00:57
type: article
metadata_visibility: show
creators_name: Sarimveis, Haralambos
creators_name: Patrinos, Panagiotis
creators_name: Tarantilis, Chris D.
creators_name: Kiranoudis, Chris T.
creators_id: 
creators_id: panagiotis.patrinos@imtlucca.it
creators_id: 
creators_id: 
title: Dynamic modeling and control of supply chain systems: a review
ispublished: pub
subjects: QA75
subjects: TJ
divisions: CSA
full_text_status: none
keywords: Supply chain management; Control; Dynamic modeling; Review; Dynamic programming; Model predictive control
abstract: 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.
date: 2008-11
date_type: published
publication: Computers and operations research 
volume: 35
number: 11
publisher: Elsevier
pagerange: 3530-3561
id_number: 10.1016/j.cor.2007.01.017
refereed: TRUE
issn: 0305-0548
official_url: http://www.sciencedirect.com/science/article/pii/S0305054807000366
citation:   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)