Heterogeneous Multi-resource Planning and Allocation Under Stochastic Demand

We study the capacity planning and allocation decisions for multiple heterogeneous resources, considering potential demand scenarios, where each demand requests a subset of the available resource types simultaneously at a specified time, location, and duration (smRmD). We model this problem as a two-stage stochastic integer program and consider two variants for the objective function: (a) maximize the expected reward of demands met over all scenarios, subject to a budget B for resources, and (b) maximize the expected reward of demands met over all scenarios minus the cost of resources. Contributions of this work include (i) a thorough complexity analysis of smRmD and its variants, (ii) analysis of structural properties, (iii) development of various approximation algorithms using the unique structural properties of smRmD and its variants, and (iv) an extensive computational study to explore the ease with which exact and approximate solutions may be found. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research has been supported in part by National Science Foundation (NSF) Graduate Research Fellowship [DGE-1650044], NSF [Grants CMMI-1538860, NSF-AF:1910423, and NSF-AF:1717947], and the following Georgia Tech benefactors: William W. George, Andrea Laliberte, Richard ”Rick” E. & Charlene Zalesky, and Claudia & Paul Raines.