The Retail Location Problem Under Uncertain Demand (2017)
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.