2024-03-19T02:10:48Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/1089572017-09-24T08:29:02Zcom_10803_311col_10803_318
TDX (Tesis Doctorals en Xarxa)
author
Kostin, Andrey
authoremail
andrey.kostin@urv.cat
authoremailshow
false
director
Jiménez Esteller, Laureano
codirector
Guillén Gosálbez, Gonzalo
authorsendemail
true
2013-04-04T10:54:24Z
2013-04-04T10:54:24Z
2013-03-18
http://hdl.handle.net/10803/108957
T.447-2013
El objetivo es desarrollar una herramienta de apoyo a la toma de decisiones para la planificación estratégica de cadenas de suministro (CS). La tarea consiste en determinar el número, ubicación y capacidad de todos los nodos de la CS, su política de expansión, el transporte y la producción entre todos los nodos de la red. El problema se formula como un modelo de programación lineal entera mixta (MILP) que se resuelve utilizando diferentes herramientas. En primer lugar se desarrolló una estrategia de descomposición para acelerar el proceso de resolución En segundo, se utilizó el algoritmo de aproximación para resolver el problema MILP estocástico. Por último, el modelo multi-objetivo incorpora las soluciones de compromiso entre los aspectos económicos y ambientales. Todas las formulaciones se aplicaron al caso real de la industria de caña de azúcar en Argentina. El objetivo de las herramientas es ayudar a los responsables de planificación estratégica de las infraestructuras para la producción de productos químicos.
The aim of this thesis is to provide a decision-support tool for the strategic planning of supply chains (SCs). The task consists of determining the number, location and capacities of all SC facilities, their expansion policy, the transportation links that need to be established, and the production rates and flows of all materials involved in the network. The problem is formulated as a mixed-integer linear programming (MILP) model, which is solved using several mathematical programming tools. First, a decomposition strategy was developed to expedite the solving procedure. Second, the approximation algorithm was utilized to solve the stochastic version of the MILP. Finally, the multi-objective model was developed to incorporate the trade-off between economical and ecological issues. All formulations were applied to a real case based on the Argentinean sugarcane industry. The tools presented are intended to help policy-makers in the strategic planning of infrastructures for chemicals production.
eng
Supply chain management
Life cycle assessment
Multiobjective optimization
Development of advanced mathematical programming methods for supply chain management
info:eu-repo/semantics/doctoralThesis info:eu-repo/semantics/publishedVersion
URL
https://www.tdx.cat/bitstream/10803/108957/1/Thesis.pdf
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Thesis.pdf.txt