Sample average approximation applied to the capacitated-facilities location problem with unreliable facilities

Abstract

The capacitated-facilities location problem (CFLP) deals with opening facilities with a finite capacity to serve a set of customers. This paper addresses the discrete CFLP when the opened facilities are unreliable, i.e. they are unavailable to provide service to customers. Such problems have gained prominence in the recent past owing to their application in the area of supply-chain disruptions. A stochastic programming formulation for the CFLP with unreliable facilities is presented and the benefit of investing in redundant facility locations is demonstrated. A sampling-based algorithm called the sample average approximation algorithm is used to approximately solve this model and present computational results.

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