Publication Type
Journal Article
Version
submittedVersion
Publication Date
7-2018
Abstract
It is widely believed that a little flexibility added at the right place can reap significant benefits for operations. Unfortunately, despite the extensive literature on this topic, we are not aware of any general methodology that can be used to guide managers in designing sparse (i.e., slightly flexible) and yet efficient operations. We address this issue using a distributionally robust approach to model the performance of a stochastic system under different process structures. We use the dual prices obtained from a related conic program to guide managers in the design process. This leads to a general solution methodology for the construction of efficient sparse structures for several classes of operational problems. Our approach can be used to design simple yet efficient structures for workforce deployment and for any level of sparsity requirement, to respond to deviations and disruptions in the operational environment. Furthermore, in the case of the classical process flexibility problem, our methodology can recover the k-chain structures that are known to be extremely efficient for this type of problem when the system is balanced and symmetric. We can also obtain the analog of 2-chain for nonsymmetrical system using this methodology.
Keywords
Sparse and Efficient Operation, Sensitivity Analysis, Conic Program, Manufacturing Flexibility, Strong Duality
Discipline
Operations and Supply Chain Management
Research Areas
Operations Management
Publication
Management Science
Volume
64
Issue
7
First Page
2973
Last Page
3468
ISSN
0025-1909
Identifier
10.1287/mnsc.2017.2761
Publisher
INFORMS (Institute for Operations Research and Management Sciences)
Citation
YAN, Zhenzhen; GAO, Sarah Yini; and TEO, Chung Piaw.
On the design of sparse but efficient structures in operations. (2018). Management Science. 64, (7), 2973-3468.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/5276
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1287/mnsc.2017.2761