Model predictive control strategies for supply chain management in semiconductor manufacturing

Wang, Wenlin; Rivera, Daniel E.; Kempf, Karl G.

doi:10.1016/j.ijpe.2006.05.013

Cited by 110 publications

(62 citation statements)

References 10 publications

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“…Morari and Lee (1999) showed that many important practical and theoretical problems can be formulated in the RHC framework. The RHC algorithm consists of two main steps: (1) prediction of future system behavior on the basis of current measurements and a system model and (2) solution of an optimization problem for determining future values of the manipulated variables, subject to constraints (Wang et al 2007). For a given planning time horizon T , with step 0 k  corresponding to the time instant in a way that a performance index defined over the considered period is optimized subject to some operational constraints.…”

Section: Methodsmentioning

confidence: 99%

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

Assimizele¹,

Oppen²,

Bye³

2013

ECMS 2013 Proceedings Edited By: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang

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Oil tanker traffic constitutes a vital part of the maritime operations in the High North and is associated with considerable risk to the environment. As a consequence, the Norwegian Coastal Administration (NCA) administers a number of vessel traffic services (VTS) centers along the Norwegian coast, one of which is located in the town of Vardø, in the extreme northeast part of Norway. The task of the operators at the VTS center in Vardø is to command a fleet of tug vessels patrolling the northern Norwegian coastline such that the risk of oil tanker drifting accidents is reduced. Currently, these operators do not use computer algorithms or mathematical models to solve this dynamic resource allocation problem but rely on their own knowledge and experience when faced with constantly changing weather and traffic conditions. We therefore propose a novel sustainable model called the receding horizon mixed integer programming (RHMIP) model for optimal dynamic allocation of patrol vessels to oil tankers. The model combines features from model predictive control and linear programming. Simulations run with real-world parameters highlight the performance and quality of our method. The developed RHMIP model can be implemented as an operational decision support tool to the NCA.

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Section: Methodsmentioning

confidence: 99%

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

Assimizele¹,

Oppen²,

Bye³

2013

ECMS 2013 Proceedings Edited By: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang

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“…The wood, as a natural resource, is inte- Overview MPC supply chains: Potential use and benefits in forest-based supply chains 2013). Its main characteristics and advantages are: a cost function that measures the supply chain performance that can be maximized or minimized, MPC can be formulated to be stable and robust even in the presence of disturbances and stochastic demand (Wang et al, 2007;Puigjaner & Laínez, 2008;Fu et al, 2013). Also, it enables the integration of constraints in production, inventory levels and dispatch capacity, and defines operational objectives concerning how the following/ monitoring of inventory goals and responses to customers' orders are achieved (Wang et al, 2007), among others.…”

Section: Mpc Application In Supply Chainsmentioning

confidence: 99%

“…Its main characteristics and advantages are: a cost function that measures the supply chain performance that can be maximized or minimized, MPC can be formulated to be stable and robust even in the presence of disturbances and stochastic demand (Wang et al, 2007;Puigjaner & Laínez, 2008;Fu et al, 2013). Also, it enables the integration of constraints in production, inventory levels and dispatch capacity, and defines operational objectives concerning how the following/ monitoring of inventory goals and responses to customers' orders are achieved (Wang et al, 2007), among others. The operating principle behind MPC is based on the online optimization with a sliding prediction window that evaluates a cost function for the control action determination (Seborg et al, 2004;Camacho & Bordons, 2007;Alessandri et al, 2011).…”

Section: Mpc Application In Supply Chainsmentioning

confidence: 99%

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Overview of MPC applications in supply chains: Potential use and benefits in the management of forest-based supply chains

et al. 2015

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Aim of study: This work aims to provide an overview of Model Predictive Controllers (MPC) applications in supply chains, to describe the forest-based supply chain and to analyse the potential use and benefits of MPC in a case study concerning a biomass supply chain.Area of study: The proposed methods are being applied to a company located in Finland.Material and methods: Supply chains are complex systems where actions and partners’ coordination influence the whole system performance. The increase of competitiveness and need of quick responses to the costumers implies the use of efficient management techniques. The control theory, particularly MPC, has been successfully used as a supply chain management tool. MPC is able to deal with dynamic interactions between the partners and to globally optimize the supply chain performance in the presence of disturbances. However, as far as is authors’ knowledge, there are no applications of this methodology in the forest-based supply chains. This work proposes a control architecture to improve the performance of the forest supply chain. The controller is based on prediction models which are able to simulate the system and deal with disturbances.Main results: The preliminary results enable to evaluate the impacts of disturbances in the supply chain. Thus, it is possible to react beforehand, controlling the schedules and tasks’ allocation, or alert the planning level in order to generate a new plan.Research highlights: Overview of MPC applications in supply chains; forest-based supply chain description; case study presentation: wood biomass supply chain for energy production; MPC architecture proposal to decrease the operation times.Keywords: biomass; forest; Model Predictive Control; planning; supply chain.

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“…In [12] and [16] autoregressive moving average (ARMA) system structure has been applied in order to model uncertain demand. Then in [6,[17][18][19] model predictive control of supply chain has been proposed and in [20] a robust controller for the continuous-time system with uncertain processing time and delay has been designed by minimising H ∞ -norm. However, practical implementation of the strategy described in [20] requires application of numerical methods in order to obtain the control law parameters, which limits its analytical tractability.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control of periodic review perishable inventories with multiple suppliers and transportation losses

Bartoszewicz¹,

Maciejewski²

2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. The purpose of this paper is to develop robust and computationally efficient supply chain management strategy ensuring fast reaction to the demand variations for periodic review perishable inventory systems. For that purpose, we apply a sliding mode approach and we propose a new discrete time warehouse management strategy. The strategy employs the sliding hyperplane appropriately designed to ensure a dead-beat performance of the closed loop system. Our strategy not only explicitly takes into account decay of goods stored in the warehouse (perishing inventories) but it also accounts for transportation losses which take place on the way from suppliers to the warehouse. The proposed strategy ensures full customers' demand satisfaction, minimizes the on-hand inventory volume and prevents from exceeding the warehouse capacity. This reflects the need of simultaneous minimization of the lost sales costs and inventory holding costs. Furthermore, the strategy ensures that the ordered quantities of goods are always non-negative and upper bounded. These favourable properties of the proposed strategy are formally stated as a lemma and three theorems and proved in the paper.

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Model predictive control strategies for supply chain management in semiconductor manufacturing

Cited by 110 publications

References 10 publications

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

Overview of MPC applications in supply chains: Potential use and benefits in the management of forest-based supply chains

Sliding mode control of periodic review perishable inventories with multiple suppliers and transportation losses

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