Heterogeneous Multi-resource Planning and Allocation Under Stochastic Demand

Arden Baxter (), Pinar Keskinocak () and Mohit Singh ()
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Arden Baxter: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332; Center for Health and Humanitarian Systems, Georgia Institute of Technology, Atlanta, Georgia 30332
Pinar Keskinocak: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332; Center for Health and Humanitarian Systems, Georgia Institute of Technology, Atlanta, Georgia 30332
Mohit Singh: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

INFORMS Journal on Computing, 2023, vol. 35, issue 5, 929-951

Abstract: 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.

Keywords: stochastic integer programming; resource allocation; resource planning; approximation algorithms (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:35:y:2023:i:5:p:929-951

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