The Mean-Value-at-Risk Median Problem on a Network with Random Demand Weights

Chunlin Xin and Jiamin Wang ()
Additional contact information
Chunlin Xin: Beijing University of Chemical Technology
Jiamin Wang: Long Island University Post

Chapter Chapter 13 in Contributions to Location Analysis, 2019, pp 321-341 from Springer

Abstract: Abstract Dr. Zvi Drezner’s research career has touched on many areas of location analysis. We devote the first part of this chapter to summarizing Zvi’s vast contributions to the studies of the minimax and the maximum facility location problems. His relevant publications are grouped in terms of the characteristics of the problems investigated, including space, the number of facilities to locate, and completeness of information. In particular, we provide an overview of Zvi’s work in the deterministic planar minimax problems. The second part of the chapter is our own paper on a network median problem when demand weights are independent random variables. The objective of the model proposed is to locate a single facility so as to minimize the expected total demand-weighted distance subject to a constraint on the value-at-risk (VaR). The study integrates the expectation criterion with the VaR measure and links different median models with random demand weights. Methods are suggested to identify dominant points for the optimal solution. An algorithm is developed for solving the problem.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-19111-5_13

Ordering information: This item can be ordered from
http://www.springer.com/9783030191115

DOI: 10.1007/978-3-030-19111-5_13

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().