Volume 45 , 2015.

abstract bibtex

abstract bibtex

Copyright ? 2015, AIDIC Servizi S.r.l.,.One of the key objectives of an optimised supply chain is to maintain a low operation cost as well as the service quality at a satisfactory level under demand uncertainty. However, the supply chain network design and inventory control optimisation are usually conducted in a sequential manner where the supply chain network is first determined by solving a mixed-integer programing (MIP) problem, and then the supply chain system with the given network design is tested as a "what if" problem in order to evaluate and improve its performance. Over the last decade, simulation modelling is regarded as an efficient tool for evaluating the performance of a real-world supply chain under different conditions and flexible control policies and simulation-based optimisation has been widely studied for solving inventory management problem under various uncertainties. In this work a hybrid computational framework is proposed to solve both the network design problem and the associated inventory control problem simultaneously. By incorporating region-wise metamodeling method to reduce the computation load, a multi-echelon supply chain case with 13 inventory stocking nodes can be solved within 3,621 s with the proposed algorithm. As a comparison, the simulation-based problem is also solved by the genetic algorithm (GA) toolbox in MATLAB, which only returns a 31 % higher cost after 13,844 s.

@book{ title = {A simulation-based optimization method for solving the integrated supply chain network design and inventory control problem under uncertainty}, type = {book}, year = {2015}, source = {Chemical Engineering Transactions}, identifiers = {[object Object]}, volume = {45}, id = {c9026c0b-5f2e-3715-bfe4-8f8734a7d6fd}, created = {2016-08-19T13:20:31.000Z}, file_attached = {false}, profile_id = {f27171f8-b200-3870-9028-dcb6a084733d}, last_modified = {2017-03-25T02:01:24.507Z}, read = {false}, starred = {false}, authored = {true}, confirmed = {false}, hidden = {false}, private_publication = {false}, abstract = {Copyright ? 2015, AIDIC Servizi S.r.l.,.One of the key objectives of an optimised supply chain is to maintain a low operation cost as well as the service quality at a satisfactory level under demand uncertainty. However, the supply chain network design and inventory control optimisation are usually conducted in a sequential manner where the supply chain network is first determined by solving a mixed-integer programing (MIP) problem, and then the supply chain system with the given network design is tested as a "what if" problem in order to evaluate and improve its performance. Over the last decade, simulation modelling is regarded as an efficient tool for evaluating the performance of a real-world supply chain under different conditions and flexible control policies and simulation-based optimisation has been widely studied for solving inventory management problem under various uncertainties. In this work a hybrid computational framework is proposed to solve both the network design problem and the associated inventory control problem simultaneously. By incorporating region-wise metamodeling method to reduce the computation load, a multi-echelon supply chain case with 13 inventory stocking nodes can be solved within 3,621 s with the proposed algorithm. As a comparison, the simulation-based problem is also solved by the genetic algorithm (GA) toolbox in MATLAB, which only returns a 31 % higher cost after 13,844 s.}, bibtype = {book}, author = {Ye, W. and You, F.} }

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