@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.}
}

{"_id":"d65LM6mHirvcfh6Ro","bibbaseid":"ye-you-asimulationbasedoptimizationmethodforsolvingtheintegratedsupplychainnetworkdesignandinventorycontrolproblemunderuncertainty-2015","downloads":0,"creationDate":"2017-12-28T02:43:51.719Z","title":"A simulation-based optimization method for solving the integrated supply chain network design and inventory control problem under uncertainty","author_short":["Ye, W.","You, F."],"year":2015,"bibtype":"book","biburl":null,"bibdata":{"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.","bibtex":"@book{\n title = {A simulation-based optimization method for solving the integrated supply chain network design and inventory control problem under uncertainty},\n type = {book},\n year = {2015},\n source = {Chemical Engineering Transactions},\n identifiers = {[object Object]},\n volume = {45},\n id = {c9026c0b-5f2e-3715-bfe4-8f8734a7d6fd},\n created = {2016-08-19T13:20:31.000Z},\n file_attached = {false},\n profile_id = {f27171f8-b200-3870-9028-dcb6a084733d},\n last_modified = {2017-03-25T02:01:24.507Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n 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.},\n bibtype = {book},\n author = {Ye, W. and You, F.}\n}","author_short":["Ye, W.","You, F."],"bibbaseid":"ye-you-asimulationbasedoptimizationmethodforsolvingtheintegratedsupplychainnetworkdesignandinventorycontrolproblemunderuncertainty-2015","role":"author","urls":{},"downloads":0},"search_terms":["simulation","based","optimization","method","solving","integrated","supply","chain","network","design","inventory","control","problem","under","uncertainty","ye","you"],"keywords":[],"authorIDs":["5a445a67dd7f81802d00001d"]}