The Retail Location Problem Under Uncertain Demand

We study the problem of a retailer facing uncertainty on the demand. The main objective is to maximize his pro t by optimizing the inventory policy and sales, also considering the option to open new selling points. We propose an integrated framework to jointly optimize the strategic and tactical dec...

Full description

Autores:
Ramírez Pico, Cristian David
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
spa
OAI Identifier:
oai:repositorio.escuelaing.edu.co:001/634
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/634
http://catalogo.escuelaing.edu.co/cgi-bin/koha/opac-detail.pl?biblionumber=20817
Palabra clave:
Programación estocástica
Programación dinamica
Programación lineal
Stochastic programming
Dynamic programming
Linear programming
Rights
openAccess
License
Derechos Reservados - Escuela Colombiana de Ingeniería Julio Garavito
Description
Summary:We study the problem of a retailer facing uncertainty on the demand. The main objective is to maximize his pro t by optimizing the inventory policy and sales, also considering the option to open new selling points. We propose an integrated framework to jointly optimize the strategic and tactical decisions. First, we formulate a deterministic optimization problem (with demand known in advance) and we analyze its outcomes. The optimal solution is not satisfying because it suffers from being anticipative. Secondly, multi-stage stochastic optimization is considered. We formulate the problem in three different versions with increasing complexity. The fi rst version considers a single retailer (SRLP) and ignores the strategic decision for opening a new selling point. We solve it by stochastic dynamic programming and we discuss results. Second and third versions are: a N-retailer (NRLP) case where transshipments between retailers are possible; a case where opening decisions of retailers might be made only at the beginning of the time span. Here we propose a new resolution method gathering Stochastic Dual Dynamic Programming and Progressive Hedging algorithms.