On the Design of Sparse But Efficient Structures in Operations

44 Pages Posted: 17 Jan 2017

See all articles by Zhenzhen Yan

Zhenzhen Yan

Nanyang Technological University

Sarah Yini Gao

Singapore Management University - Lee Kong Chian School of Business

Chung-Piaw Teo

NUS Business School - Department of Decision Sciences

Date Written: November 8, 2016

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 to design 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 non-symmetrical system using this methodology.

Keywords: Sparse and Efficient Operation, Sensitivity Analysis, Conic Program, Manufacturing Flexibility, Strong Duality

Suggested Citation

Yan, Zhenzhen and Gao, Sarah Yini and Teo, Chung-Piaw, On the Design of Sparse But Efficient Structures in Operations (November 8, 2016). Available at SSRN: https://ssrn.com/abstract=2899008 or http://dx.doi.org/10.2139/ssrn.2899008

Zhenzhen Yan (Contact Author)

Nanyang Technological University ( email )

MAS 05-19, 21 Nanyang Link
Singapore, 637371
Singapore

Sarah Yini Gao

Singapore Management University - Lee Kong Chian School of Business ( email )

469 Bukit Timah Road
Singapore 912409
Singapore

Chung-Piaw Teo

NUS Business School - Department of Decision Sciences ( email )

15 Kent Ridge Drive
Mochtar Riady Building, BIZ 1 8-69
119245
Singapore

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